{"group":{"id":1,"name":"Community","lockable":false,"created_at":"2012-01-18T18:02:15.000Z","updated_at":"2026-04-16T00:12:35.000Z","description":"Problems submitted by members of the MATLAB Central community.","is_default":true,"created_by":161519,"badge_id":null,"featured":false,"trending":false,"solution_count_in_trending_period":0,"trending_last_calculated":"2026-04-16T00:00:00.000Z","image_id":null,"published":true,"community_created":false,"status_id":2,"is_default_group_for_player":false,"deleted_by":null,"deleted_at":null,"restored_by":null,"restored_at":null,"description_opc":null,"description_html":null,"published_at":null},"problems":[{"id":45427,"title":"King's Cage","description":"Given the position of the king on the chessboard, determine the minimum number of steps it'll require to reach the destination.\r\n\r\n\u003chttps://en.wikipedia.org/wiki/Chess#Movement\u003e\r\n\r\nFor simplicity, numerical notation is used to represent the positions.","description_html":"\u003cp\u003eGiven the position of the king on the chessboard, determine the minimum number of steps it'll require to reach the destination.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Chess#Movement\"\u003ehttps://en.wikipedia.org/wiki/Chess#Movement\u003c/a\u003e\u003c/p\u003e\u003cp\u003eFor simplicity, numerical notation is used to represent the positions.\u003c/p\u003e","function_template":"function i = king(x,y)","test_suite":"%%\r\nx=[1,1];\r\ny=[5,5];\r\nassert(isequal( king(x,y),4))\r\n\r\n%%\r\nx=[6,6];\r\ny=[2,3];\r\nassert(isequal( king(x,y),4))\r\n\r\n%%\r\nx=[5,1];\r\ny=[5,5];\r\nassert(isequal( king(x,y),4))\r\n\r\n%%\r\nx=[2,3];\r\ny=[8,8];\r\nassert(isequal( king(x,y),6))\r\n\r\n%%\r\nx=[2,8];\r\ny=[7,1];\r\nassert(isequal( king(x,y),7))\r\n\r\n%%\r\nx=[1,4];\r\ny=[8,3];\r\nassert(isequal( king(x,y),7))\r\n\r\n\r\n%%\r\nx=[5,8];\r\ny=[5,8];\r\nassert(isequal( king(x,y),0))\r\n\r\n\r\n%%\r\nx=[1,4];\r\ny=[3,4];\r\nassert(isequal( king(x,y),2))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":58,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-07T06:55:52.000Z","updated_at":"2026-04-12T10:12:31.000Z","published_at":"2020-04-07T06:55:52.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the position of the king on the chessboard, determine the minimum number of steps it'll require to reach the destination.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Chess#Movement\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://en.wikipedia.org/wiki/Chess#Movement\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor simplicity, numerical notation is used to represent the positions.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":590,"title":"Greed is good - Simple partition P[n].","description":"Find a simple partition P[n]. E.g. P[10] = 4 + 3 + 2 + 1.\r\nThere are many solutions, compute just one set.\r\nDon't repeat numbers.\r\nBe Greedy ;-)\r\nTo check against trivial solutions, E.g. [x-k, k] etc; but I'll provide you with one to start.\r\nShow me how you write the whole solution.\r\nBonus points if you solve the general problem of producing all unique partitions of [n].","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 174.167px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 87.0833px; transform-origin: 407px 87.0833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 169px 8px; transform-origin: 169px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFind a simple partition P[n]. E.g. P[10] = 4 + 3 + 2 + 1.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 102.167px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 51.0833px; transform-origin: 391px 51.0833px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 152px 8px; transform-origin: 152px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThere are many solutions, compute just one set.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 70.5px 8px; transform-origin: 70.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDon't repeat numbers.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 43px 8px; transform-origin: 43px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBe Greedy ;-)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 267px 8px; transform-origin: 267px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo check against trivial solutions, E.g. [x-k, k] etc; but I'll provide you with one to start.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 133.5px 8px; transform-origin: 133.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eShow me how you write the whole solution.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 269px 8px; transform-origin: 269px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBonus points if you solve the general problem of producing all unique partitions of [n].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = simplepartition(x)\r\n y = [x-1,1]; %trivial\r\nend\r\n","test_suite":"%%\r\nx = 10000;\r\nz = simplepartition(x); zL = length(z);\r\nassert(isequal(sum(unique(z)),x) \u0026\u0026 zL \u003e= ceil(log2(x)/2) \u0026 all(unique(z)\u003e0))\r\n\r\n%%\r\nx = 40190;\r\nz = simplepartition(x); zL = length(z);\r\nassert(isequal(sum(unique(z)),x) \u0026\u0026 zL \u003e= ceil(log2(x)/2) \u0026 all(unique(z)\u003e0))\r\n\r\n%%\r\nx = 149;\r\nz = simplepartition(x); zL = length(z);\r\nassert(isequal(sum(unique(z)),x) \u0026\u0026 zL \u003e= ceil(log2(x)/2) \u0026 all(unique(z)\u003e0))\r\n\r\n%%\r\nx = 20;\r\nz = simplepartition(x); zL = length(z);\r\nassert(isequal(sum(unique(z)),x) \u0026\u0026 zL \u003e= ceil(log2(x)/2) \u0026 all(unique(z)\u003e0))","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":3378,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":33,"test_suite_updated_at":"2022-01-05T17:27:44.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2012-04-17T03:13:18.000Z","updated_at":"2026-01-06T08:27:01.000Z","published_at":"2012-04-17T03:14:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind a simple partition P[n]. E.g. P[10] = 4 + 3 + 2 + 1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are many solutions, compute just one set.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDon't repeat numbers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBe Greedy ;-)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo check against trivial solutions, E.g. [x-k, k] etc; but I'll provide you with one to start.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eShow me how you write the whole solution.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBonus points if you solve the general problem of producing all unique partitions of [n].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45342,"title":"Sieve of Eratosthenes","description":"Find the nth lucky prime number.\r\n\r\n\u003chttps://planetmath.org/luckyprime\u003e\r\n\r\ncan u find a way for large n?","description_html":"\u003cp\u003eFind the nth lucky prime number.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://planetmath.org/luckyprime\"\u003ehttps://planetmath.org/luckyprime\u003c/a\u003e\u003c/p\u003e\u003cp\u003ecan u find a way for large n?\u003c/p\u003e","function_template":"function y = lucky_prime(n)\r\n y = x;\r\nend","test_suite":"%%\r\nassert(isequal(lucky_prime(4),31))\r\n%%\r\nassert(isequal(lucky_prime(10),127))\r\n%%\r\nassert(isequal(lucky_prime(20),349))\r\n%%\r\nassert(isequal(lucky_prime(27),541))\r\n%%\r\nassert(isequal(lucky_prime(39),823))\r\n%%\r\nassert(isequal(lucky_prime(50),1123))\r\n%%\r\nassert(isequal(lucky_prime(60),1579))\r\n%%\r\nassert(isequal(lucky_prime(70),1987))\r\n%%\r\nassert(isequal(lucky_prime(90),2971))\r\n%%\r\nassert(isequal(lucky_prime(80),2473))\r\n%%\r\nassert(isequal(lucky_prime(200),9403))\r\n%%\r\nassert(isequal(lucky_prime(260),12799))\r\n%%\r\nassert(isequal(lucky_prime(440),25237))\r\n%%\r\nassert(isequal(lucky_prime(600),38461))\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":"2020-04-02T00:55:41.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2020-02-18T19:05:49.000Z","updated_at":"2026-01-19T18:26:06.000Z","published_at":"2020-02-18T19:39:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the nth lucky prime number.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://planetmath.org/luckyprime\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://planetmath.org/luckyprime\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ecan u find a way for large n?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45425,"title":"The Tortoise and the Hare - 01","description":"Suppose in an infinitely long line, the hare is standing in position 0.\r\n\r\nFrom that place, it can jump either in the +ve direction or in the -ve [but not both].\r\n\r\nOne condition is that, in i-th jump, it can move i step. Meaning -\r\n \r\n 0\r\n1 [1st step \u003e\u003e so 0+1]\r\n3 [2nd step \u003e\u003e so 1+2]\r\n6\r\n10\r\n\r\nGiven a position x, determine whether the hare will be in that position or not. \r\n\r\nFor example, \r\n\r\n if x=15 then true \r\n if x=14 then false.\r\n\r\nSimilar problem \u003chttps://www.mathworks.com/matlabcentral/cody/problems/45347-cat-s-paw-01\u003e","description_html":"\u003cp\u003eSuppose in an infinitely long line, the hare is standing in position 0.\u003c/p\u003e\u003cp\u003eFrom that place, it can jump either in the +ve direction or in the -ve [but not both].\u003c/p\u003e\u003cp\u003eOne condition is that, in i-th jump, it can move i step. Meaning -\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e0\r\n1 [1st step \u0026gt;\u0026gt; so 0+1]\r\n3 [2nd step \u0026gt;\u0026gt; so 1+2]\r\n6\r\n10\r\n\u003c/pre\u003e\u003cp\u003eGiven a position x, determine whether the hare will be in that position or not.\u003c/p\u003e\u003cp\u003eFor example,\u003c/p\u003e\u003cpre\u003e if x=15 then true \r\n if x=14 then false.\u003c/pre\u003e\u003cp\u003eSimilar problem \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45347-cat-s-paw-01\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45347-cat-s-paw-01\u003c/a\u003e\u003c/p\u003e","function_template":"function tf = rabbit_02(x)","test_suite":"%%\r\nassert(isequal(rabbit_02(15),1))\r\n%%\r\nassert(isequal(rabbit_02(45),1))\r\n%%\r\nassert(isequal(rabbit_02(555),0))\r\n%%\r\nassert(isequal(rabbit_02(25651),1))\r\n%%\r\nassert(isequal(rabbit_02(-256514),0))\r\n%%\r\nassert(isequal(rabbit_02(-2566245),1))\r\n%%\r\nassert(isequal(rabbit_02(10011),1))\r\n%%\r\nassert(isequal(rabbit_02(9191),0))\r\n%%\r\nassert(isequal(rabbit_02(9191985078),1))\r\n%%\r\nassert(isequal(rabbit_02(-111222333),0))\r\n%%\r\nassert(isequal(rabbit_02(-111236070),1))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":2,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":53,"test_suite_updated_at":"2020-04-07T04:52:58.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-07T04:44:29.000Z","updated_at":"2026-02-09T21:15:15.000Z","published_at":"2020-04-07T04:52:58.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSuppose in an infinitely long line, the hare is standing in position 0.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFrom that place, it can jump either in the +ve direction or in the -ve [but not both].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOne condition is that, in i-th jump, it can move i step. Meaning -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[0\\n1 [1st step \u003e\u003e so 0+1]\\n3 [2nd step \u003e\u003e so 1+2]\\n6\\n10]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a position x, determine whether the hare will be in that position or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ if x=15 then true \\n if x=14 then false.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSimilar problem\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45347-cat-s-paw-01\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45347-cat-s-paw-01\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45377,"title":"Maya - 03","description":"Following up on the previous two problems, now you've to add or subtract two Maya numerals.\r\n\r\nThe 3rd input will be a function handle.\r\n\r\nAnd the output should also be in Maya representation. \r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/45376-maya-02\u003e\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/45375-maya-01\u003e","description_html":"\u003cp\u003eFollowing up on the previous two problems, now you've to add or subtract two Maya numerals.\u003c/p\u003e\u003cp\u003eThe 3rd input will be a function handle.\u003c/p\u003e\u003cp\u003eAnd the output should also be in Maya representation.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45376-maya-02\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45376-maya-02\u003c/a\u003e \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45375-maya-01\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45375-maya-01\u003c/a\u003e\u003c/p\u003e","function_template":"function out = maya_03(a,b,fun)","test_suite":"%%\r\nassert(isequal(maya_03('.','..',@plus),'...'))\r\n%%\r\nassert(isequal(maya_03('. o o o o','..',@minus),'....___ ....___ ....___ ...___'))\r\n%%\r\nassert(isequal(maya_03('. o o o o','._ o ....__ __',@minus),'...__ ....___ _ __'))\r\n%%\r\nassert(isequal(maya_03('. o o o o','._ o ....__ __',@plus),'. ._ o ....__ __'))\r\n%%\r\nassert(isequal(maya_03('._','._',@minus),'o'))\r\n%%\r\nassert(isequal(maya_03('._ _ _','_ _ _ ._',@plus),'_ .__ __ .__'))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2020-03-23T02:11:58.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-03-23T02:01:45.000Z","updated_at":"2026-01-06T08:29:50.000Z","published_at":"2020-03-23T02:11:58.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFollowing up on the previous two problems, now you've to add or subtract two Maya numerals.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe 3rd input will be a function handle.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAnd the output should also be in Maya representation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45376-maya-02\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45376-maya-02\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45375-maya-01\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45375-maya-01\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45385,"title":"Coin distribution","description":"Imagine, u r in a shop. ur bill is n(2200). u want to pay the bill with minimum no of coins u have.\r\n\r\nu've coins of - 2000,1000,500,100,50,20,10,5,2,1.\r\n\r\nThere are multiple ways to do that but due to the imposed condition, the correct solution for the above scenario is -\r\n\r\n 2000 - 1\r\n 100 - 2\r\n\r\nthe output should be a 2D matrix of size 2-by-x; where the 1st row contains the coins u used and 2nd row contains how many. \r\n\r\n out=[2000 100;\r\n 1 2]","description_html":"\u003cp\u003eImagine, u r in a shop. ur bill is n(2200). u want to pay the bill with minimum no of coins u have.\u003c/p\u003e\u003cp\u003eu've coins of - 2000,1000,500,100,50,20,10,5,2,1.\u003c/p\u003e\u003cp\u003eThere are multiple ways to do that but due to the imposed condition, the correct solution for the above scenario is -\u003c/p\u003e\u003cpre\u003e 2000 - 1\r\n 100 - 2\u003c/pre\u003e\u003cp\u003ethe output should be a 2D matrix of size 2-by-x; where the 1st row contains the coins u used and 2nd row contains how many.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eout=[2000 100;\r\n 1 2]\r\n\u003c/pre\u003e","function_template":"function out = coin(n)","test_suite":"%%\r\ny=[2000 100\r\n 1 2]\r\nassert(isequal(coin(2200),y))\r\n\r\n%%\r\ny=[100,20,2;2,1,1]\r\nassert(isequal(coin(222),y))\r\n\r\n%%\r\ny=[2000 500 100 50 20 10 5 2 1\r\n 3 1 2 1 1 1 1 1 1]\r\nassert(isequal( coin(6788),y))\r\n\r\n%%\r\ny=[2000 100 20 5 2 1\r\n 56728 3 2 1 1 1]\r\nassert(isequal( coin(113456348),y))\r\n\r\n%%\r\ny=[2;2]\r\nassert(isequal( coin(4),y))\r\n\r\n%%\r\ny=[1000 100 20 5 2\r\n 1 4 2 1 2]\r\nassert(isequal( coin(1449),y))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":46,"test_suite_updated_at":"2020-03-24T19:30:04.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-03-24T18:58:31.000Z","updated_at":"2026-02-09T18:16:23.000Z","published_at":"2020-03-24T19:30:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eImagine, u r in a shop. ur bill is n(2200). u want to pay the bill with minimum no of coins u have.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eu've coins of - 2000,1000,500,100,50,20,10,5,2,1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are multiple ways to do that but due to the imposed condition, the correct solution for the above scenario is -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 2000 - 1\\n 100 - 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe output should be a 2D matrix of size 2-by-x; where the 1st row contains the coins u used and 2nd row contains how many.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[out=[2000 100;\\n 1 2]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45247,"title":"Tell your secret","description":"A secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons?\r\nOutput will be for two scenarios -\r\n\r\n* z(1) -- each person can continue spreading the secret\r\n* z(2) -- each person can tell the secret to only two persons\r\n\r\nn.b. outputs can only be multiples of 5. ","description_html":"\u003cp\u003eA secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons?\r\nOutput will be for two scenarios -\u003c/p\u003e\u003cul\u003e\u003cli\u003ez(1) -- each person can continue spreading the secret\u003c/li\u003e\u003cli\u003ez(2) -- each person can tell the secret to only two persons\u003c/li\u003e\u003c/ul\u003e\u003cp\u003en.b. outputs can only be multiples of 5.\u003c/p\u003e","function_template":"function t = puzzle_tell_ur_secret(n)","test_suite":"%%\r\nn = 1;\r\nz = [0,0];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 2;\r\nz = [5,5];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 3;\r\nz = [5,5];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 8;\r\nz = [10,15];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 20;\r\nz = [15,20];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 31;\r\nz = [20,20];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 64;\r\nz = [20,30];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 100;\r\nz = [25,30];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 768;\r\nz = [35,45];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 3000;\r\nz = [40,55];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 100000;\r\nz = [55,80];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2019-12-27T00:41:26.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2019-12-27T00:10:25.000Z","updated_at":"2026-03-13T11:59:30.000Z","published_at":"2019-12-27T00:41:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons? Output will be for two scenarios -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ez(1) -- each person can continue spreading the secret\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ez(2) -- each person can tell the secret to only two persons\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en.b. outputs can only be multiples of 5.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":475,"title":"Is this group simply connected?","description":"Given connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\r\n\r\nExample 1:\r\n \r\n Input node_pairs = [ 8 9\r\n 8 3 ]\r\n Output tf is true\r\n \r\nThe three nodes of this graph are fully connected, since this graph could be drawn like so:\r\n\r\n 3--8--9\r\n\r\nExample 2:\r\n\r\n Input node_pairs = [ 1 2 \r\n 2 3\r\n 1 4\r\n 3 4\r\n 5 6 ]\r\n Output tf is false\r\n\r\nThis graph could be drawn like so:\r\n\r\n 1--2 5--6\r\n | |\r\n 4--3\r\n\r\nThere are two distinct subgraphs.","description_html":"\u003cp\u003eGiven connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\u003c/p\u003e\u003cp\u003eExample 1:\u003c/p\u003e\u003cpre\u003e Input node_pairs = [ 8 9\r\n 8 3 ]\r\n Output tf is true\u003c/pre\u003e\u003cp\u003eThe three nodes of this graph are fully connected, since this graph could be drawn like so:\u003c/p\u003e\u003cpre\u003e 3--8--9\u003c/pre\u003e\u003cp\u003eExample 2:\u003c/p\u003e\u003cpre\u003e Input node_pairs = [ 1 2 \r\n 2 3\r\n 1 4\r\n 3 4\r\n 5 6 ]\r\n Output tf is false\u003c/pre\u003e\u003cp\u003eThis graph could be drawn like so:\u003c/p\u003e\u003cpre\u003e 1--2 5--6\r\n | |\r\n 4--3\u003c/pre\u003e\u003cp\u003eThere are two distinct subgraphs.\u003c/p\u003e","function_template":"function tf = isConnected(node_pairs)\r\n tf = true;\r\nend","test_suite":"%%\r\n\r\nnode_pairs = [8 9; 8 3];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2;\r\n 2 3;\r\n 1 4;\r\n 3 4;\r\n 5 6 ];\r\ntf = false;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2;\r\n 2 3;\r\n 1 4;\r\n 3 4;\r\n 5 6;\r\n 6 2 ];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2; 2 100];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2; 50 100];\r\ntf = false;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 4 17 ];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n","published":true,"deleted":false,"likes_count":6,"comments_count":2,"created_by":7,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":36,"test_suite_updated_at":"2012-03-09T22:15:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-09T22:05:26.000Z","updated_at":"2026-03-03T23:05:27.000Z","published_at":"2012-03-12T16:23:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input node_pairs = [ 8 9\\n 8 3 ]\\n Output tf is true]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe three nodes of this graph are fully connected, since this graph could be drawn like so:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 3--8--9]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 2:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input node_pairs = [ 1 2 \\n 2 3\\n 1 4\\n 3 4\\n 5 6 ]\\n Output tf is false]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis graph could be drawn like so:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 1--2 5--6\\n | |\\n 4--3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are two distinct subgraphs.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45363,"title":"Exponentiation","description":"Given 3 integers b,e,k; find -- \r\n\r\n mod(b^e,k)","description_html":"\u003cp\u003eGiven 3 integers b,e,k; find --\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003emod(b^e,k)\r\n\u003c/pre\u003e","function_template":"function x = mod_expo(b,e,k)\r\nend","test_suite":"%%\r\nassert(isequal(mod_expo(5,3,13),8))\r\n%%\r\nassert(isequal(mod_expo(10,4,11),1))\r\n%%\r\nassert(isequal(mod_expo(4,13,497),445))\r\n%%\r\nassert(isequal(mod_expo(4,311,497),464))\r\n%%\r\nassert(isequal(mod_expo(50,300,13),1))\r\n%%\r\nassert(isequal(mod_expo(1000,300,23),3))\r\n%%\r\nassert(isequal(mod_expo(64,43,4),0))\r\n%%\r\nassert(isequal(mod_expo(644,43,5),4))\r\n%%\r\nassert(isequal(mod_expo(327,211,56),47))\r\n%%\r\nassert(isequal(mod_expo(3277,211,234),1))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":31,"test_suite_updated_at":"2020-03-14T15:29:04.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-03-13T18:57:51.000Z","updated_at":"2026-01-25T09:46:56.000Z","published_at":"2020-03-13T18:58:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven 3 integers b,e,k; find --\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[mod(b^e,k)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":55290,"title":"Cut the rod","description":"A rod of length n can be cut in different sizes. Different price is associated with different length of cuts. \r\nlength, len= [1, 2, 3, 4, 5, 6, 7, 8]\r\nprice, p = [1, 5, 8, 9,10,17,17,20]\r\nHere, if you cut a piece of length 5, the price for that piece is 10. For length of 8, the price is 20.\r\nSay, you have to obtain a rod of length x. By cutting the rod in which way will give you the maximum price.\r\n\r\nFor instance, say x=4. you can cut the rod in pieces like (1,3)/(3,1), (2,2), (1,1,1,1), (1,1,2)/(1,2,1)/... or (4).\r\nThe maximum revenue that you can get here is when you cut the rod in (2,2) pieces to get length x =\u003e 5+5=10. \r\nFor (1,3)=\u003e9; (1,1,1,1)=\u003e 4; (1,1,2)=\u003e7, (4)=\u003e9. \r\n\r\nIn this problem, you have to return the maximum reveneue you can obtain by cutting the rod of size x.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 322.875px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 161.438px; transform-origin: 407px 161.438px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA rod of length n can be cut in different sizes. Different price is associated with different length of cuts. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 40.875px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 20.4375px; transform-origin: 391px 20.4375px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elength, len= [1, 2, 3, 4, 5, 6, 7, 8]\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eprice, p = [1, 5, 8, 9,10,17,17,20]\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eHere, if you cut a piece of length 5, the price for that piece is 10. For length of 8, the price is 20.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eSay, you have to obtain a rod of length x. By cutting the rod in which way will give you the maximum price.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor instance, say x=4. you can cut the rod in pieces like (1,3)/(3,1), (2,2), (1,1,1,1), (1,1,2)/(1,2,1)/... or (4).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe maximum revenue that you can get here is when you cut the rod in (2,2) pieces to get length x =\u0026gt; 5+5=10. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor (1,3)=\u0026gt;9; (1,1,1,1)=\u0026gt; 4; (1,1,2)=\u0026gt;7, (4)=\u0026gt;9. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIn this problem, you have to return the maximum reveneue you can obtain by cutting the rod of size x.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = rod_cut(x,p)\r\n y = x;\r\nend","test_suite":"%%\r\np=[1,5,8,9,10,17,17,20];\r\nx=4;\r\ny_correct = 10;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n%%\r\np=[1,5,8,9,10,17,17,20];\r\nx=8;\r\ny_correct = 22;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n%%\r\np=[1,5,8,9,10,17,17,20];\r\nx=7;\r\ny_correct = 18;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n%%\r\np=[1,5,8,9,10,17,17,20];\r\nx=6;\r\ny_correct = 17;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n%%\r\np=[10,5,3,18];\r\nx=4;\r\ny_correct = 40;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n\r\n%%\r\np=[10,5,3,18];\r\nx=2;\r\ny_correct = 20;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n\r\n%%\r\np=[10,5,36,18,36];\r\nx=4;\r\ny_correct = 46;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n%%\r\np=[10,5,36,18,36];\r\nx=5;\r\ny_correct = 56;\r\nassert(isequal(rod_cut(x,p),y_correct))","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":363598,"edited_by":363598,"edited_at":"2022-08-13T23:35:18.000Z","deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2022-08-13T23:35:18.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-08-13T23:28:51.000Z","updated_at":"2026-01-06T08:34:18.000Z","published_at":"2022-08-13T23:35:18.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA rod of length n can be cut in different sizes. Different price is associated with different length of cuts. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elength, len= [1, 2, 3, 4, 5, 6, 7, 8]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eprice, p = [1, 5, 8, 9,10,17,17,20]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, if you cut a piece of length 5, the price for that piece is 10. For length of 8, the price is 20.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSay, you have to obtain a rod of length x. By cutting the rod in which way will give you the maximum price.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor instance, say x=4. you can cut the rod in pieces like (1,3)/(3,1), (2,2), (1,1,1,1), (1,1,2)/(1,2,1)/... or (4).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe maximum revenue that you can get here is when you cut the rod in (2,2) pieces to get length x =\u0026gt; 5+5=10. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor (1,3)=\u0026gt;9; (1,1,1,1)=\u0026gt; 4; (1,1,2)=\u0026gt;7, (4)=\u0026gt;9. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem, you have to return the maximum reveneue you can obtain by cutting the rod of size x.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1483,"title":"Number of paths on a grid","description":"\r\nConsider a grid formed by n vertices vertically down, and m vertices horizontally right. Your starting point is at the top left vertex. \r\nYour destination is the bottom right vertex. You are permitted at each vertex to choose to move down or right, that is in the direction towards the destination. You are not to move on what constitutes a back step like moving left or up. If you hit the bottom boundary, or right boundary take it to be given there is only 1 way to the destination, that is following along the boundary. \r\n\r\nEx: in a 2x2 grid there are two ways. One way: First down, then right. The other way: First right, then down. \r\n\r\n4x3 has 10 ways\r\n\r\n6x5 has 126 ways\r\n\r\nThis problem can be solved using dynamic programming but there are other methods too. \r\n\r\nProblem 7)\r\nPrev: \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1482 1482\u003e\r\nNext: \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1484 1484\u003e","description_html":"\u003cdiv style = \"text-align: start; line-height: 20px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: normal; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"display: block; min-width: 0px; padding-top: 0px; transform-origin: 332px 148.5px; vertical-align: baseline; perspective-origin: 332px 148.5px; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 63px; white-space: pre-wrap; perspective-origin: 309px 63px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eConsider a grid formed by n vertices vertically down, and m vertices horizontally right. Your starting point is at the top left vertex. Your destination is the bottom right vertex. You are permitted at each vertex to choose to move down or right, that is in the direction towards the destination. You are not to move on what constitutes a back step like moving left or up. If you hit the bottom boundary, or right boundary take it to be given there is only 1 way to the destination, that is following along the boundary.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 21px; white-space: pre-wrap; perspective-origin: 309px 21px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eEx: in a 2x2 grid there are two ways. One way: First down, then right. The other way: First right, then down.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 10.5px; white-space: pre-wrap; perspective-origin: 309px 10.5px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e4x3 has 10 ways\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 10.5px; white-space: pre-wrap; perspective-origin: 309px 10.5px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e6x5 has 126 ways\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 10.5px; white-space: pre-wrap; perspective-origin: 309px 10.5px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThis problem can be solved using dynamic programming but there are other methods too.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 10.5px; white-space: pre-wrap; perspective-origin: 309px 10.5px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eProblem 7) Prev:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"http://www.mathworks.com/matlabcentral/cody/problems/1482\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e1482\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e Next:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"http://www.mathworks.com/matlabcentral/cody/problems/1484\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e1484\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = paths2dest_ongrid(n,m)\r\n y = n+m;\r\nend","test_suite":"%%\r\nm = 1; n = 1 ;\r\ny_correct = 1;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 2; n = 2 ;\r\ny_correct = 2;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 4; n = 3 ;\r\ny_correct = 10;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 6; n = 5 ;\r\ny_correct = 126;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 5; n = 5 ;\r\ny_correct = 70;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 1; n = 100 ;\r\ny_correct = 1;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 100; n = 1 ;\r\ny_correct = 1;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 2; n = 100 ;\r\ny_correct = 100;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 100; n = 2 ;\r\ny_correct = 100;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 15; n = 20 ;\r\ny_correct = 818809200;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":5,"created_by":11275,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":81,"test_suite_updated_at":"2020-09-28T20:02:06.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-05-01T14:58:23.000Z","updated_at":"2026-03-19T08:10:31.000Z","published_at":"2013-05-01T14:58:23.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider a grid formed by n vertices vertically down, and m vertices horizontally right. Your starting point is at the top left vertex. Your destination is the bottom right vertex. You are permitted at each vertex to choose to move down or right, that is in the direction towards the destination. You are not to move on what constitutes a back step like moving left or up. If you hit the bottom boundary, or right boundary take it to be given there is only 1 way to the destination, that is following along the boundary.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEx: in a 2x2 grid there are two ways. One way: First down, then right. The other way: First right, then down.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e4x3 has 10 ways\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e6x5 has 126 ways\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem can be solved using dynamic programming but there are other methods too.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 7) Prev:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1482\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e1482\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1484\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e1484\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45449,"title":"Quarantine Days","description":"In these quarantine days, a list of what Max may do on a typical day is given in a table with the starting hour to ending hour.\r\n\r\n Task Start End\r\nSleep 0 6\r\nWalk 6 7\r\nBreakfast 8 9\r\nMovie 10 16\r\nStudy 15 21\r\nDinner 22 23\r\n\r\nNow Max wants to perform the maximum amount of tasks in a single day. How should he make his routine?\r\n\r\nFor this problem -- just return the number of tasks.","description_html":"\u003cp\u003eIn these quarantine days, a list of what Max may do on a typical day is given in a table with the starting hour to ending hour.\u003c/p\u003e\u003cpre\u003e Task Start End\r\nSleep 0 6\r\nWalk 6 7\r\nBreakfast 8 9\r\nMovie 10 16\r\nStudy 15 21\r\nDinner 22 23\u003c/pre\u003e\u003cp\u003eNow Max wants to perform the maximum amount of tasks in a single day. How should he make his routine?\u003c/p\u003e\u003cp\u003eFor this problem -- just return the number of tasks.\u003c/p\u003e","function_template":"function yy = activity_2(x)\r\nend","test_suite":"%%\r\nStart=[0,6,8,10,15,22]';\r\nFinish=[6,7,9,16,21,23]';\r\nx=table(Start,Finish);\r\nassert(isequal(activity_2(x),5))\r\n\r\n%%\r\nStart=[0,5,8,10,15,22,23,3,11,16]';\r\nFinish=[6,8,11,11,17,24,24,4,16,18]';\r\nx=table(Start,Finish);\r\nassert(isequal(activity_2(x),6))\r\n\r\n%%\r\nStart=[0,5,8,10,15,22,23,3,11,16,5,12,14]';\r\nFinish=[6,8,11,11,17,24,24,4,16,18,10,13,16]';\r\nx=table(Start,Finish);\r\nassert(isequal(activity_2(x),7))\r\n\r\n%%\r\nStart=[1,12,16,21,11]';\r\nFinish=[11,13,18,24,17]';\r\nx=table(Start,Finish);\r\nassert(isequal(activity_2(x),4))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-13T02:15:41.000Z","updated_at":"2026-02-09T22:19:31.000Z","published_at":"2020-04-13T02:31:01.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn these quarantine days, a list of what Max may do on a typical day is given in a table with the starting hour to ending hour.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Task Start End\\nSleep 0 6\\nWalk 6 7\\nBreakfast 8 9\\nMovie 10 16\\nStudy 15 21\\nDinner 22 23]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow Max wants to perform the maximum amount of tasks in a single day. How should he make his routine?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this problem -- just return the number of tasks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45433,"title":"The Dark Knight","description":" The current position of the knight is x \r\n The desired destination is y\r\n The size of the chessboard is n.\r\n\r\nFind the minimum number of moves required by the knight to reach the destination.\r\n\r\nFor example, \r\n \r\n x=[2,2] y=[3,3] -- moves required = 2 \r\n [2,2] \u003e [1,4] \u003e [3,3]\r\n","description_html":"\u003cpre class=\"language-matlab\"\u003eThe current position of the knight is x \r\nThe desired destination is y\r\nThe size of the chessboard is n.\r\n\u003c/pre\u003e\u003cp\u003eFind the minimum number of moves required by the knight to reach the destination.\u003c/p\u003e\u003cp\u003eFor example,\u003c/p\u003e\u003cpre\u003e x=[2,2] y=[3,3] -- moves required = 2 \r\n [2,2] \u0026gt; [1,4] \u0026gt; [3,3]\u003c/pre\u003e","function_template":"function out = knight_step(x,y,n)","test_suite":"%%\r\nassert(isequal(knight_step([2,2],[3,3],8),2))\r\n%%\r\nassert(isequal(knight_step([2,2],[1,1],20),4))\r\n\r\n%%\r\nassert(isequal(knight_step([2,2],[8,8],12),4))\r\n\r\n%%\r\nassert(isequal(knight_step([2,2],[12,11],12),7))\r\n\r\n%%\r\nassert(isequal(knight_step([1,3],[8,3],8),5))\r\n%%\r\nassert(isequal(knight_step([1,3],[5,4],8),3))\r\n%%\r\nassert(isequal(knight_step([8,2],[1,2],8),5))\r\n\r\n%%\r\nassert(isequal(knight_step([8,7],[21,32],50),14))\r\n%%\r\nassert(isequal(knight_step([5,19],[5,19],20),0))\r\n%%\r\nassert(isequal(knight_step([5,19],[19,5],20),10))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":21,"test_suite_updated_at":"2020-04-10T06:28:31.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-10T06:20:09.000Z","updated_at":"2026-01-21T12:55:02.000Z","published_at":"2020-04-10T06:28:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[The current position of the knight is x \\nThe desired destination is y\\nThe size of the chessboard is n.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the minimum number of moves required by the knight to reach the destination.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x=[2,2] y=[3,3] -- moves required = 2 \\n [2,2] \u003e [1,4] \u003e [3,3]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45343,"title":"How lucky r u ?","description":"Find the nth number in the following sequence\r\n\r\n\u003chttps://oeis.org/A264940\u003e","description_html":"\u003cp\u003eFind the nth number in the following sequence\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://oeis.org/A264940\"\u003ehttps://oeis.org/A264940\u003c/a\u003e\u003c/p\u003e","function_template":"function y = lucky(n)\r\n\r\n y = x;\r\nend","test_suite":"%%\r\nassert(isequal(lucky(1),0))\r\n%%\r\nassert(isequal(lucky(5),3))\r\n%%\r\nassert(isequal(lucky(12),2))\r\n%%\r\nassert(isequal(lucky(19),7))\r\n%%\r\nassert(isequal(lucky(27),9))\r\n%%\r\nassert(isequal(lucky(31),0))\r\n%%\r\nassert(isequal(lucky(37),0))\r\n%%\r\nassert(isequal(lucky(45),13))\r\n%%\r\nassert(isequal(lucky(55),15))\r\n%%\r\nassert(isequal(lucky(85),21))\r\n%%\r\nassert(isequal(lucky(185),3))\r\n%%\r\nassert(isequal(lucky(303),0))\r\n%%\r\nassert(isequal(lucky(555),13))\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-02-19T07:42:17.000Z","updated_at":"2025-11-21T23:33:51.000Z","published_at":"2020-02-19T07:42:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the nth number in the following sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://oeis.org/A264940\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://oeis.org/A264940\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1049,"title":"Path of least resistance","description":"Find the length of the shortest path through the matrix from the top left to bottom right corner. You may move right, down, or diagonally right-down one element at a time. The length of the path is the sum of the elements you pass through.\r\nE.g.\r\n M = [*8 6 10 10 4 7 7 7\r\n 9 *1 10 5 9 0 8 2\r\n 1 3 *2 8 8 8 7 7\r\n 9 5 10 *1 *10 *9 *4 *0 ]; \r\n\r\n\u003e\u003e shortest_path(M)\r\n\r\nans =\r\n\r\n 35\r\nThe shortest path through this matrix has length 35. Each element along the path is marked with a *","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 317.333px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 158.667px; transform-origin: 407px 158.667px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381.5px 8px; transform-origin: 381.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFind the length of the shortest path through the matrix from the top left to bottom right corner. You may move right, down, or diagonally right-down one element at a time. The length of the path is the sum of the elements you pass through.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12.5px 8px; transform-origin: 12.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE.g.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 204.333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 102.167px; transform-origin: 404px 102.167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 224px 8.5px; tab-size: 4; transform-origin: 224px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e M = [*8 6 10 10 4 7 7 7\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 224px 8.5px; tab-size: 4; transform-origin: 224px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 9 *1 10 5 9 0 8 2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 224px 8.5px; tab-size: 4; transform-origin: 224px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 3 *2 8 8 8 7 7\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 240px 8.5px; tab-size: 4; transform-origin: 240px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 9 5 10 *1 *10 *9 *4 *0 ]; \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 76px 8.5px; tab-size: 4; transform-origin: 76px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u0026gt;\u0026gt; shortest_path(M)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eans =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 32px 8.5px; tab-size: 4; transform-origin: 32px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 35\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 308px 8px; transform-origin: 308px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe shortest path through this matrix has length 35. Each element along the path is marked with a\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e *\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function p = shortest_path(M)\r\n p = sum(M(1:end,1)) + sum(M(end,2:end));\r\nend","test_suite":"%%\r\nM = [8 6 10 10 4 7 7 7\r\n 9 1 10 5 9 0 8 2\r\n 1 3 2 8 8 8 7 7\r\n 9 5 10 1 10 9 4 0 ];\r\ny_correct = 35;\r\nassert(isequal(shortest_path(M),y_correct))\r\n\r\n%%\r\nM = [6 8 5 5 3\r\n 5 4 8 0 5\r\n 9 6 9 3 2\r\n 3 1 1 2 6\r\n 8 1 6 8 3 ];\r\ny_correct = 22;\r\nassert(isequal(shortest_path(M),y_correct))\r\n\r\n%% \r\nM = hadamard(8);\r\ny_correct = -5;\r\nassert(isequal(shortest_path(M),y_correct))\r\n\r\n%% \r\nassert(isequal(arrayfun(@(x) shortest_path(eye(x)), 2:5),[2 2 2 2]))\r\n","published":true,"deleted":false,"likes_count":9,"comments_count":1,"created_by":450,"edited_by":223089,"edited_at":"2022-05-09T17:30:33.000Z","deleted_by":null,"deleted_at":null,"solvers_count":80,"test_suite_updated_at":"2022-05-09T17:30:33.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-11-22T20:58:51.000Z","updated_at":"2026-01-06T08:40:37.000Z","published_at":"2012-11-22T21:17:01.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the length of the shortest path through the matrix from the top left to bottom right corner. You may move right, down, or diagonally right-down one element at a time. The length of the path is the sum of the elements you pass through.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ M = [*8 6 10 10 4 7 7 7\\n 9 *1 10 5 9 0 8 2\\n 1 3 *2 8 8 8 7 7\\n 9 5 10 *1 *10 *9 *4 *0 ]; \\n\\n\u003e\u003e shortest_path(M)\\n\\nans =\\n\\n 35]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe shortest path through this matrix has length 35. Each element along the path is marked with a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e *\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45415,"title":"Find an optimal placement of coolers on a grid","description":"In a certain chemical plant, 6 new pieces of cooling equipment (coolers) are to be installed in a vacant space. This vacant space was divided into a grid of 6 cells by 6 cells. Your task is to assign the 6 coolers to 6 cells in this grid with the following requirements:\r\n\r\n# There must be one cooler for every column in the grid.\r\n# If you decide to install a cooler at row R, column C, then the cooler at column C+1 must be installed either on row R-1, R, or R+1 only. This is done to ease the connection of coolers by piping.\r\n# The total installation cost for the 6 coolers must be minimum.\r\n\r\nFor this problem, you are given a cost matrix (COST) in relation to the third requirement above. COST is a 6 x 6 matrix where the _r,c_-th element is the cost of assigning any cooler to row _r_, column _c_ (All the coolers are identical). Write a function that accepts the matrix COST, and output the value of the minimum cost of installation. You are ensured that all elements of COST are integers in the range [1,1000].\r\n\r\nIn the sample test case below, the optimal placement is at the following rows: 4,3,3,2,2,2.\r\n\r\n \u003e\u003e COST = [695 766 710 119 752 548;\r\n 318 796 755 499 256 139;\r\n 951 187 277 960 506 150;\r\n 35 490 680 341 700 258;\r\n 439 446 656 586 891 841;\r\n 382 647 163 224 960 255];\r\n \u003e\u003e coolers(COST)\r\n ans = \r\n 1393\r\n \r\nMeanwhile, the optimal placement for the case below is at rows: 5,6,5,6,5,6\r\n\r\n \u003e\u003e COST = [815 617 918 76 569 312;\r\n 244 474 286 54 470 529;\r\n 930 352 758 531 455 988;\r\n 350 831 754 780 12 602;\r\n 197 586 381 935 163 263;\r\n 252 550 568 130 795 100];\r\n \u003e\u003e coolers(COST)\r\n ans = \r\n 1521\r\n","description_html":"\u003cp\u003eIn a certain chemical plant, 6 new pieces of cooling equipment (coolers) are to be installed in a vacant space. This vacant space was divided into a grid of 6 cells by 6 cells. Your task is to assign the 6 coolers to 6 cells in this grid with the following requirements:\u003c/p\u003e\u003col\u003e\u003cli\u003eThere must be one cooler for every column in the grid.\u003c/li\u003e\u003cli\u003eIf you decide to install a cooler at row R, column C, then the cooler at column C+1 must be installed either on row R-1, R, or R+1 only. This is done to ease the connection of coolers by piping.\u003c/li\u003e\u003cli\u003eThe total installation cost for the 6 coolers must be minimum.\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eFor this problem, you are given a cost matrix (COST) in relation to the third requirement above. COST is a 6 x 6 matrix where the \u003ci\u003er,c\u003c/i\u003e-th element is the cost of assigning any cooler to row \u003ci\u003er\u003c/i\u003e, column \u003ci\u003ec\u003c/i\u003e (All the coolers are identical). Write a function that accepts the matrix COST, and output the value of the minimum cost of installation. You are ensured that all elements of COST are integers in the range [1,1000].\u003c/p\u003e\u003cp\u003eIn the sample test case below, the optimal placement is at the following rows: 4,3,3,2,2,2.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; COST = [695 766 710 119 752 548;\r\n 318 796 755 499 256 139;\r\n 951 187 277 960 506 150;\r\n 35 490 680 341 700 258;\r\n 439 446 656 586 891 841;\r\n 382 647 163 224 960 255];\r\n\u0026gt;\u0026gt; coolers(COST)\r\nans = \r\n 1393\r\n\u003c/pre\u003e\u003cp\u003eMeanwhile, the optimal placement for the case below is at rows: 5,6,5,6,5,6\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; COST = [815 617 918 76 569 312;\r\n 244 474 286 54 470 529;\r\n 930 352 758 531 455 988;\r\n 350 831 754 780 12 602;\r\n 197 586 381 935 163 263;\r\n 252 550 568 130 795 100];\r\n\u0026gt;\u0026gt; coolers(COST)\r\nans = \r\n 1521\r\n\u003c/pre\u003e","function_template":"function y = coolers(COST)\r\n y = x;\r\nend","test_suite":"%%\r\nfiletext = fileread('coolers.m')\r\nassert(isempty(strfind(filetext, 'rand')))\r\n%%\r\nCOST = [815 617 918 76 569 312;\r\n 244 474 286 54 470 529;\r\n 930 352 758 531 455 988;\r\n 350 831 754 780 12 602;\r\n 197 586 381 935 163 263;\r\n 252 550 568 130 795 100];\r\nassert(isequal(coolers(COST),1521))\r\n%%\r\nCOST = [690 153 107 85 182 550;\r\n 749 826 962 400 264 145;\r\n 451 539 5 260 146 854;\r\n 84 997 775 801 137 623;\r\n 229 79 818 432 870 351;\r\n 914 443 869 911 580 514];\r\nassert(isequal(coolers(COST),1179))\r\n%%\r\nCOST = [402 418 338 242 576 44;\r\n 76 50 901 404 60 169;\r\n 240 903 370 97 235 650;\r\n 124 945 112 132 354 732;\r\n 184 491 781 943 822 648;\r\n 240 490 390 957 16 451];\r\nassert(isequal(coolers(COST),697))\r\n%%\r\nCOST = [1 1 1000 1000 1000 1;\r\n 1 1000 1 1000 1000 1000;\r\n 1 1000 1000 1000 1 1000;\r\n 1 1000 1000 1 1000 1000;\r\n 1 1000 1000 1000 1000 1000;\r\n 1 1000 1000 1000 1000 1000];\r\nassert(isequal(coolers(COST),2004))\r\n%%\r\nCOST = [548 369 487 818 351 208;\r\n 297 626 436 795 940 302;\r\n 745 781 447 645 876 471;\r\n 189 82 307 379 551 231;\r\n 687 930 509 812 623 845;\r\n 184 776 511 533 588 195];\r\nassert(isequal(coolers(COST),1739))\r\n%%\r\nCOST = [226 431 259 222 86 489;\r\n 171 185 409 118 263 579;\r\n 228 905 595 297 802 238;\r\n 436 980 263 319 30 459;\r\n 312 439 603 425 929 964;\r\n 924 112 712 508 731 547];\r\nassert(isequal(coolers(COST),1234))\r\n%%\r\nCOST = [522 368 99 107 891 501;\r\n 232 988 262 654 335 480;\r\n 489 38 336 495 699 905;\r\n 625 886 680 780 198 610;\r\n 680 914 137 716 31 618;\r\n 396 797 722 904 745 860];\r\nassert(isequal(coolers(COST),1454))\r\n%%\r\nCOST = [806 490 60 819 973 84;\r\n 577 168 682 818 649 134;\r\n 183 979 43 723 801 174;\r\n 240 713 72 150 454 391;\r\n 887 501 522 660 433 832;\r\n 29 472 97 519 826 804];\r\nassert(isequal(coolers(COST),1172))\r\n%%\r\nCOST = [61 292 373 53 418 699;\r\n 400 432 199 738 984 667;\r\n 527 16 490 270 302 179;\r\n 417 985 340 423 702 129;\r\n 657 168 952 548 667 1000;\r\n 628 107 921 943 540 172];\r\nassert(isequal(coolers(COST),1253))\r\n%%\r\nCOST = [33 461 191 385 825 907;\r\n 562 982 429 583 983 880;\r\n 882 157 483 252 731 818;\r\n 670 856 121 291 344 261;\r\n 191 645 590 618 585 595;\r\n 369 377 227 266 108 23];\r\nassert(isequal(coolers(COST),1192))\r\n%%\r\nCOST = [426 599 69 719 779 441;\r\n 313 471 320 969 424 528;\r\n 162 696 531 532 91 458;\r\n 179 700 655 326 267 876;\r\n 423 639 408 106 154 519;\r\n 95 34 820 611 282 944];\r\nassert(isequal(coolers(COST),1316))\r\n%%\r\nCOST = [638 696 345 916 323 474;\r\n 958 68 781 2 785 153;\r\n 241 255 676 463 472 342;\r\n 677 225 7 425 36 608;\r\n 290 668 603 461 176 192;\r\n 672 845 387 771 722 739];\r\nassert(isequal(coolers(COST),1126))\r\n%%\r\nCOST = [243 92 648 237 771 257;\r\n 918 577 680 120 351 614;\r\n 270 684 636 608 663 583;\r\n 766 547 946 451 417 541;\r\n 189 426 209 459 842 870;\r\n 288 645 710 662 833 265];\r\nassert(isequal(coolers(COST),1711))\r\n%%\r\nCOST = [319 545 219 366 193 862;\r\n 120 648 106 764 139 485;\r\n 940 544 110 628 697 394;\r\n 646 722 64 772 94 672;\r\n 480 523 405 933 526 742;\r\n 640 994 449 973 531 521];\r\nassert(isequal(coolers(COST),1669))\r\n%%\r\nCOST = [674 622 842 636 885 896;\r\n 575 722 853 599 699 975;\r\n 794 844 722 911 905 664;\r\n 632 680 510 715 878 836;\r\n 523 869 667 944 689 720;\r\n 878 698 713 696 609 917];\r\nassert(isequal(coolers(COST),3837))\r\n%%\r\nCOST = [82 122 681 602 355 371;...\r\n 483 544 417 347 776 384;...\r\n 129 315 643 365 237 862;...\r\n 253 383 215 172 845 464;...\r\n 884 792 618 796 817 571;...\r\n 197 840 676 493 847 696];\r\nassert(isequal(coolers(COST),1452))\r\n%%\r\nCOST = [961 170 710 563 536 327;...\r\n 547 179 176 177 199 603;...\r\n 637 244 859 514 624 362;...\r\n 571 752 910 549 27 135;...\r\n 928 200 962 166 319 914;...\r\n 864 983 571 494 533 641];\r\nassert(isequal(coolers(COST),1569))\r\n%%\r\nCOST = [659 381 223 112 267 869;...\r\n 676 822 1000 425 292 529;...\r\n 745 172 64 614 189 915;...\r\n 843 330 426 989 23 974;...\r\n 517 967 405 220 450 586;...\r\n 152 807 401 355 244 119];\r\nassert(isequal(coolers(COST),1835))\r\n%%\r\nCOST = [927 925 215 554 571 679;...\r\n 594 643 249 631 336 213;...\r\n 884 105 227 986 958 82;...\r\n 425 701 704 635 440 275;...\r\n 608 396 755 601 602 868;...\r\n 71 85 548 910 721 560];\r\nassert(isequal(coolers(COST),1751))\r\n%%\r\nCOST = [465 433 384 904 809 299;...\r\n 431 506 712 219 180 769;...\r\n 774 376 481 874 166 502;...\r\n 654 481 730 83 182 910;...\r\n 658 343 938 466 692 58;...\r\n 162 778 518 22 214 437];\r\nassert(isequal(coolers(COST),1317))\r\n%%\r\nCOST = [573 952 497 860 78 228;...\r\n 566 767 809 627 339 710;...\r\n 824 752 633 181 581 149;...\r\n 127 139 689 574 476 659;...\r\n 301 350 640 164 806 634;...\r\n 3 152 730 907 531 230];\r\nassert(isequal(coolers(COST),1568))\r\n%%\r\nCOST = [183 958 897 179 998 879;...\r\n 167 26 190 747 5 747;...\r\n 150 972 661 50 543 118;...\r\n 203 298 942 72 862 510;...\r\n 955 526 976 490 910 169;...\r\n 16 863 108 850 846 832];\r\nassert(isequal(coolers(COST),539))\r\n%%\r\nCOST = [929 868 245 81 27 760;...\r\n 170 742 641 361 786 926;...\r\n 884 448 809 829 923 833;...\r\n 388 710 854 215 493 260;...\r\n 383 945 399 792 835 214;...\r\n 272 175 116 655 132 523];\r\nassert(isequal(coolers(COST),1564))\r\n%%\r\nCOST = [398 438 798 217 378 972;...\r\n 480 773 656 812 168 361;...\r\n 994 745 33 139 541 645;...\r\n 605 443 558 882 102 68;...\r\n 945 54 720 924 40 208;...\r\n 491 88 111 13 934 40];\r\nassert(isequal(coolers(COST),749))\r\n%%\r\nCOST = [470 581 9 642 470 879;...\r\n 151 541 825 106 220 189;...\r\n 992 706 768 269 923 760;...\r\n 428 6 998 764 321 32;...\r\n 956 783 228 806 858 643;...\r\n 725 927 920 105 260 567];\r\nassert(isequal(coolers(COST),1216))\r\n%%\r\nCOST = [377 187 35 595 562 603;...\r\n 213 486 489 499 634 474;...\r\n 793 839 972 568 931 357;...\r\n 146 142 113 427 978 476;...\r\n 490 733 744 77 94 672;...\r\n 13 692 639 291 662 960];\r\nassert(isequal(coolers(COST),1048))\r\n%%\r\nCOST = [90 52 454 629 707 46;...\r\n 798 505 738 133 536 886;...\r\n 591 769 510 619 194 840;...\r\n 913 283 383 384 690 119;...\r\n 102 226 906 992 51 411;...\r\n 294 332 966 287 185 121];\r\nassert(isequal(coolers(COST),1042))\r\n%%\r\nCOST = [573 666 232 754 549 464;...\r\n 950 974 53 622 461 590;...\r\n 257 623 902 395 646 188;...\r\n 990 64 794 360 514 612;...\r\n 350 374 374 89 815 52;...\r\n 209 167 833 342 98 576];\r\nassert(isequal(coolers(COST),934))\r\n%%\r\nCOST = [843 797 666 67 652 382;...\r\n 500 294 961 898 134 301;...\r\n 440 116 944 498 639 341;...\r\n 150 376 113 772 385 919;...\r\n 29 829 649 61 766 457;...\r\n 757 842 481 263 653 443];\r\nassert(isequal(coolers(COST),1166))\r\n%%\r\nCOST = [455 767 602 56 365 673;...\r\n 946 343 650 99 677 203;...\r\n 220 619 343 650 376 869;...\r\n 883 454 494 765 864 752;...\r\n 20 11 702 988 292 420;...\r\n 342 600 888 126 134 1];\r\nassert(isequal(coolers(COST),994))\r\n%%\r\nCOST = [150 436 24 66 150 132;...\r\n 274 904 575 924 351 887;...\r\n 873 926 47 535 336 675;...\r\n 602 506 423 367 785 836;...\r\n 322 628 468 364 487 657;...\r\n 285 720 23 152 465 984];\r\nassert(isequal(coolers(COST),958))\r\n%%\r\nCOST = [980 191 386 245 842 952;...\r\n 251 125 311 804 79 966;...\r\n 625 3 4 824 238 766;...\r\n 729 153 816 853 818 575;...\r\n 499 535 639 468 406 916;...\r\n 850 511 449 971 467 496];\r\nassert(isequal(coolers(COST),1655))\r\n%%\r\nCOST = [167 167 988 896 65 887;...\r\n 326 948 151 835 264 421;...\r\n 297 812 959 3 103 284;...\r\n 559 711 531 641 484 49;...\r\n 68 971 75 804 419 220;...\r\n 69 999 312 246 382 240];\r\nassert(isequal(coolers(COST),640))\r\n%%\r\nCOST = [30 653 667 689 433 469;...\r\n 703 321 848 321 905 546;...\r\n 8 104 763 532 631 180;...\r\n 611 536 808 874 984 635;...\r\n 409 165 633 55 586 963;...\r\n 249 884 711 501 841 535];\r\nassert(isequal(coolers(COST),2007))\r\n%%\r\nCOST = [480 679 683 689 62 13;...\r\n 794 566 947 148 220 217;...\r\n 93 479 100 778 83 12;...\r\n 881 321 512 400 951 643;...\r\n 4 602 111 899 17 517;...\r\n 512 914 546 308 115 246];\r\nassert(isequal(coolers(COST),648))\r\n%%\r\nCOST = [194 309 342 580 56 396;...\r\n 91 745 840 329 35 170;...\r\n 369 840 983 269 287 431;...\r\n 8 263 627 551 78 417;...\r\n 603 515 182 181 901 729;...\r\n 479 447 124 679 847 407];\r\nassert(isequal(coolers(COST),1129))\r\n%%\r\nCOST = [952 211 79 334 443 924;...\r\n 912 131 934 693 633 153;...\r\n 952 521 603 204 930 406;...\r\n 347 906 378 959 530 313;...\r\n 291 403 665 712 627 694;...\r\n 887 216 793 167 681 891];\r\nassert(isequal(coolers(COST),2052))\r\n%%\r\nCOST = [491 677 35 27 661 34;...\r\n 806 829 437 501 330 407;...\r\n 327 111 937 828 660 717;...\r\n 550 280 263 259 14 922;...\r\n 389 768 570 46 719 985;...\r\n 897 217 360 247 392 984];\r\nassert(isequal(coolers(COST),1266))\r\n%%\r\nCOST = [897 655 269 739 879 390;...\r\n 866 864 153 13 903 300;...\r\n 801 275 631 606 153 735;...\r\n 555 841 317 577 193 105;...\r\n 419 71 960 808 791 793;...\r\n 128 379 499 655 61 783];\r\nassert(isequal(coolers(COST),1254))\r\n%%\r\nCOST = [533 130 314 596 463 399;...\r\n 254 451 642 537 368 478;...\r\n 71 673 787 331 680 67;...\r\n 626 857 290 412 568 412;...\r\n 25 499 498 795 652 970;...\r\n 63 49 819 344 492 781];\r\nassert(isequal(coolers(COST),1580))\r\n%%\r\nCOST = [730 112 727 411 679 798;...\r\n 766 397 148 143 254 712;...\r\n 757 493 148 799 844 784;...\r\n 844 259 705 931 294 624;...\r\n 771 37 381 5 27 826;...\r\n 979 975 77 651 94 36];\r\nassert(isequal(coolers(COST),953))\r\n%%\r\nCOST = [406 27 672 377 507 436;...\r\n 250 156 53 114 329 158;...\r\n 481 834 735 965 754 601;...\r\n 881 195 500 433 837 938;...\r\n 281 830 944 85 254 108;...\r\n 600 339 290 717 535 900];\r\nassert(isequal(coolers(COST),931))\r\n%%\r\nCOST = [551 355 642 427 787 943;...\r\n 428 774 128 34 512 97;...\r\n 153 882 497 930 563 846;...\r\n 248 735 311 925 685 910;...\r\n 448 407 579 359 93 12;...\r\n 533 605 944 260 873 524];\r\nassert(isequal(coolers(COST),1430))\r\n%%\r\nCOST = [651 279 401 430 885 568;...\r\n 386 840 555 38 256 895;...\r\n 650 427 444 976 910 215;...\r\n 763 632 91 523 895 4;...\r\n 576 834 745 910 399 881;...\r\n 632 271 33 384 626 236];\r\nassert(isequal(coolers(COST),1575))\r\n%%\r\nCOST = [245 391 532 153 716 903;...\r\n 641 802 889 231 281 290;...\r\n 305 158 264 658 413 500;...\r\n 826 626 235 563 363 784;...\r\n 884 699 840 292 782 678;...\r\n 946 86 496 623 136 150];\r\nassert(isequal(coolers(COST),1276))\r\n%%\r\nCOST = [697 977 429 793 902 349;...\r\n 130 126 15 420 52 166;...\r\n 946 753 326 533 809 29;...\r\n 887 828 135 926 335 956;...\r\n 516 782 451 900 229 681;...\r\n 680 191 573 545 823 861];\r\nassert(isequal(coolers(COST),772))\r\n%%\r\nCOST = [940 301 652 887 695 895;...\r\n 681 74 170 115 207 842;...\r\n 918 768 532 443 555 131;...\r\n 257 85 634 660 880 190;...\r\n 886 729 15 295 558 154;...\r\n 921 448 471 951 753 29];\r\nassert(isequal(coolers(COST),1239))\r\n%%\r\nCOST = [10 496 746 686 237 463;...\r\n 597 259 338 268 196 926;...\r\n 610 733 585 970 706 216;...\r\n 919 117 469 184 181 2;...\r\n 734 747 88 300 523 907;...\r\n 302 810 829 412 297 681];\r\nassert(isequal(coolers(COST),1182))\r\n%%\r\nCOST = [515 394 842 779 399 396;...\r\n 523 8 49 728 671 399;...\r\n 103 546 317 651 441 752;...\r\n 997 510 784 665 133 523;...\r\n 359 247 973 939 440 491;...\r\n 626 46 587 536 548 89];\r\nassert(isequal(coolers(COST),1435))\r\n%%\r\nCOST = [251 93 71 159 652 1;...\r\n 448 954 301 63 499 641;...\r\n 638 163 814 702 285 8;...\r\n 710 971 77 87 831 107;...\r\n 993 598 355 617 819 107;...\r\n 933 241 133 174 939 368];\r\nassert(isequal(coolers(COST),771))\r\n%%\r\nCOST = [240 788 66 813 71 954;...\r\n 347 270 611 815 242 209;...\r\n 250 844 702 90 732 117;...\r\n 388 741 112 732 41 647;...\r\n 422 827 96 904 425 109;...\r\n 641 183 598 453 541 984];\r\nassert(isequal(coolers(COST),1343))\r\n%%\r\nCOST = [249 916 747 918 880 282;...\r\n 607 901 476 471 799 169;...\r\n 817 215 584 270 325 746;...\r\n 831 548 261 763 670 478;...\r\n 490 785 85 773 297 654;...\r\n 761 195 299 22 930 967];\r\nassert(isequal(coolers(COST),1567))\r\n%%\r\nCOST = [314 77 818 283 736 557;...\r\n 77 154 767 639 157 274;...\r\n 792 827 375 593 435 133;...\r\n 366 301 190 326 833 700;...\r\n 586 384 647 989 360 486;...\r\n 184 651 4 124 77 183];\r\nassert(isequal(coolers(COST),956))\r\n%%\r\nCOST = [102 248 381 378 851 968;...\r\n 202 438 749 973 257 477;...\r\n 135 670 157 606 286 995;...\r\n 324 548 59 339 780 491;...\r\n 951 610 340 928 702 504;...\r\n 533 864 818 899 493 769];\r\nassert(isequal(coolers(COST),1799))\r\n%%\r\nCOST = [389 799 340 268 52 543;...\r\n 454 221 273 177 628 809;...\r\n 133 858 171 432 30 794;...\r\n 759 905 665 476 137 502;...\r\n 566 293 536 786 695 277;...\r\n 649 726 830 131 516 120];\r\nassert(isequal(coolers(COST),1234))\r\n%%\r\nCOST = [887 183 567 504 84 962;...\r\n 971 32 378 261 74 467;...\r\n 943 725 822 733 770 787;...\r\n 639 145 305 163 818 423;...\r\n 91 636 320 922 741 944;...\r\n 75 790 785 223 759 2];\r\nassert(isequal(coolers(COST),1447))\r\n%%\r\nCOST = [982 666 329 847 415 7;...\r\n 571 685 928 902 662 767;...\r\n 347 793 757 596 784 22;...\r\n 558 349 289 69 248 394;...\r\n 300 251 607 219 555 253;...\r\n 160 346 767 870 230 205];\r\nassert(isequal(coolers(COST),1039))\r\n%%\r\nCOST = [663 975 947 135 372 260;...\r\n 915 120 967 806 227 134;...\r\n 7 519 68 525 447 420;...\r\n 747 822 438 945 267 507;...\r\n 800 638 321 989 460 325;...\r\n 908 954 135 410 433 685];\r\nassert(isequal(coolers(COST),1081))\r\n%%\r\nCOST = [444 502 305 970 394 701;...\r\n 436 556 767 690 459 110;...\r\n 794 631 268 718 209 7;...\r\n 816 98 40 560 758 598;...\r\n 753 246 297 534 547 660;...\r\n 790 616 557 876 358 581];\r\nassert(isequal(coolers(COST),1667))\r\n%%\r\nCOST = [910 165 870 241 503 143;...\r\n 637 281 788 9 568 381;...\r\n 526 260 970 672 189 397;...\r\n 260 548 181 905 325 577;...\r\n 52 542 931 573 717 20;...\r\n 732 789 46 156 553 578];\r\nassert(isequal(coolers(COST),1369))\r\n%%\r\nCOST = [933 303 817 55 540 946;...\r\n 107 460 451 638 938 377;...\r\n 733 49 807 425 661 68;...\r\n 971 386 791 906 395 182;...\r\n 609 362 283 418 259 576;...\r\n 720 288 69 155 848 186];\r\nassert(isequal(coolers(COST),1495))\r\n%%\r\nCOST = [292 28 747 842 871 890;...\r\n 462 659 747 511 212 66;...\r\n 347 159 174 166 837 510;...\r\n 319 803 118 715 860 621;...\r\n 460 409 175 908 524 734;...\r\n 236 328 628 219 478 230];\r\nassert(isequal(coolers(COST),1040))\r\n%%\r\nCOST = [22 327 315 114 208 74;...\r\n 139 138 160 701 785 595;...\r\n 770 385 153 180 527 862;...\r\n 970 563 137 804 572 449;...\r\n 387 634 710 514 423 653;...\r\n 994 542 465 549 722 304];\r\nassert(isequal(coolers(COST),716))\r\n%%\r\nCOST = [608 457 948 406 340 75;...\r\n 279 600 453 439 896 664;...\r\n 800 843 811 679 546 704;...\r\n 797 32 929 466 750 919;...\r\n 955 188 673 954 125 661;...\r\n 445 944 373 355 454 691];\r\nassert(isequal(coolers(COST),2010))\r\n%%\r\nCOST = [854 26 473 324 731 29;...\r\n 468 57 679 802 774 128;...\r\n 459 143 115 300 901 134;...\r\n 807 172 237 776 139 129;...\r\n 825 626 290 553 795 936;...\r\n 191 30 173 555 190 274];\r\nassert(isequal(coolers(COST),1199))\r\n%%\r\nCOST = [943 546 850 417 382 152;...\r\n 639 256 8 518 723 437;...\r\n 873 306 635 887 96 13;...\r\n 368 16 360 150 668 230;...\r\n 237 588 115 435 297 264;...\r\n 188 963 541 60 599 512];\r\nassert(isequal(coolers(COST),627))\r\n%%\r\nCOST = [216 475 261 618 402 95;...\r\n 347 826 590 145 15 376;...\r\n 748 304 480 717 75 546;...\r\n 414 822 199 402 592 112;...\r\n 56 566 240 463 447 905;...\r\n 391 55 781 708 927 634];\r\nassert(isequal(coolers(COST),940))\r\n%%\r\nCOST = [906 61 814 762 771 880;...\r\n 631 674 316 876 876 374;...\r\n 15 478 312 872 68 767;...\r\n 317 306 345 173 647 169;...\r\n 112 517 667 851 325 520;...\r\n 630 708 862 960 641 628];\r\nassert(isequal(coolers(COST),1043))\r\n%%\r\nCOST = [714 628 766 404 544 135;...\r\n 307 193 319 751 411 541;...\r\n 264 777 253 488 901 858;...\r\n 917 865 201 385 57 199;...\r\n 616 334 70 62 444 156;...\r\n 94 136 552 214 538 62];\r\nassert(isequal(coolers(COST),575))\r\n%%\r\nCOST = [662 683 487 77 915 603;...\r\n 19 138 680 445 92 466;...\r\n 292 630 705 166 994 299;...\r\n 974 858 461 399 97 134;...\r\n 765 900 365 921 314 296;...\r\n 244 349 281 512 786 167];\r\nassert(isequal(coolers(COST),1112))\r\n%%\r\nCOST = [318 40 863 414 595 658;...\r\n 110 617 277 415 310 685;...\r\n 833 670 532 984 902 474;...\r\n 972 38 523 58 94 142;...\r\n 219 4 568 397 320 951;...\r\n 707 143 334 792 887 883];\r\nassert(isequal(coolers(COST),1040))\r\n%%\r\nCOST = [438 952 914 477 226 187;...\r\n 835 2 534 251 568 647;...\r\n 326 296 805 308 999 129;...\r\n 368 49 563 967 132 82;...\r\n 795 443 751 209 955 660;...\r\n 100 790 10 521 124 28];\r\nassert(isequal(coolers(COST),914))\r\n%%\r\nCOST = [986 693 226 415 41 625;...\r\n 540 603 797 499 583 296;...\r\n 374 776 997 950 565 75;...\r\n 707 592 282 954 356 294;...\r\n 948 377 711 733 881 235;...\r\n 383 851 665 385 625 346];\r\nassert(isequal(coolers(COST),1955))\r\n%%\r\nCOST = [849 636 448 593 844 465;...\r\n 161 844 327 69 424 31;...\r\n 158 783 280 206 545 435;...\r\n 509 265 932 724 528 558;...\r\n 604 315 400 576 186 639;...\r\n 162 184 380 201 82 35];\r\nassert(isequal(coolers(COST),1044))\r\n%%\r\nCOST = [710 855 367 351 91 968;...\r\n 170 385 350 193 258 434;...\r\n 594 400 631 920 428 785;...\r\n 609 326 665 289 578 526;...\r\n 773 556 993 551 900 332;...\r\n 57 296 945 920 219 432];\r\nassert(isequal(coolers(COST),1623))\r\n%%\r\nCOST = [718 654 235 805 97 557;...\r\n 917 41 201 104 600 840;...\r\n 891 505 381 730 234 205;...\r\n 135 895 595 649 33 622;...\r\n 120 386 269 475 580 175;...\r\n 894 293 623 933 843 290];\r\nassert(isequal(coolers(COST),1365))\r\n%%\r\nCOST = [19 283 206 904 234 347;...\r\n 702 245 435 541 247 298;...\r\n 953 287 143 818 171 405;...\r\n 750 964 376 709 236 303;...\r\n 757 231 794 44 276 758;...\r\n 543 538 813 146 952 360];\r\nassert(isequal(coolers(COST),1417))\r\n%%\r\nCOST = [125 458 201 22 719 780;...\r\n 618 78 960 483 450 568;...\r\n 356 905 666 808 660 77;...\r\n 363 282 542 737 754 252;...\r\n 69 614 869 573 805 134;...\r\n 868 662 558 9 30 565];\r\nassert(isequal(coolers(COST),953))\r\n%%\r\nCOST = [541 43 122 494 863 421;...\r\n 69 528 592 856 685 488;...\r\n 989 257 360 725 635 461;...\r\n 252 409 720 200 142 516;...\r\n 316 948 524 158 80 272;...\r\n 301 920 261 371 877 232];\r\nassert(isequal(coolers(COST),1198))\r\n%%\r\nCOST = [900 895 226 336 740 434;...\r\n 909 88 105 620 889 290;...\r\n 604 540 10 993 860 632;...\r\n 366 429 60 649 598 296;...\r\n 599 618 323 540 655 623;...\r\n 669 559 780 233 916 48];\r\nassert(isequal(coolers(COST),2054))\r\n%%\r\nCOST = [995 67 543 281 171 63;...\r\n 207 928 781 599 372 407;...\r\n 608 88 522 37 40 464;...\r\n 348 333 932 64 710 203;...\r\n 718 527 148 323 642 870;...\r\n 28 247 417 99 175 598];\r\nassert(isequal(coolers(COST),730))\r\n%%\r\nCOST = [24 867 571 301 445 250;...\r\n 900 616 326 522 983 956;...\r\n 453 27 451 562 579 143;...\r\n 59 323 578 242 235 513;...\r\n 107 464 75 913 811 972;...\r\n 999 100 58 826 452 649];\r\nassert(isequal(coolers(COST),902))\r\n%%\r\nCOST = [615 424 874 119 114 462;...\r\n 470 274 301 99 491 864;...\r\n 578 445 401 891 600 263;...\r\n 912 628 518 34 91 824;...\r\n 377 535 62 840 979 329;...\r\n 229 386 232 508 654 942];\r\nassert(isequal(coolers(COST),1065))\r\n%%\r\nCOST = [245 314 524 145 800 565;...\r\n 958 58 893 245 209 218;...\r\n 511 45 406 379 897 858;...\r\n 565 813 605 271 746 862;...\r\n 994 413 95 216 537 314;...\r\n 771 385 336 634 970 327];\r\nassert(isequal(coolers(COST),1381))\r\n%%\r\nCOST = [841 334 286 610 505 816;...\r\n 493 367 657 384 8 910;...\r\n 52 795 232 31 920 778;...\r\n 779 39 622 858 411 758;...\r\n 427 727 76 604 733 406;...\r\n 280 873 967 848 152 702];\r\nassert(isequal(coolers(COST),1140))\r\n%%\r\nCOST = [566 563 904 539 781 176;...\r\n 585 547 605 738 537 417;...\r\n 345 551 927 182 770 740;...\r\n 705 695 117 427 639 893;...\r\n 161 928 341 99 894 26;...\r\n 2 945 37 304 61 138];\r\nassert(isequal(coolers(COST),1153))\r\n%%\r\nCOST = [425 619 192 805 191 892;...\r\n 765 7 715 860 504 674;...\r\n 525 741 178 567 51 686;...\r\n 755 992 987 755 57 696;...\r\n 170 129 17 520 336 800;...\r\n 673 361 40 586 631 661];\r\nassert(isequal(coolers(COST),1579))\r\n%%\r\nCOST = [520 255 928 227 995 523;...\r\n 332 846 588 2 984 274;...\r\n 935 539 102 894 965 719;...\r\n 247 911 367 454 667 779;...\r\n 512 351 276 579 727 82;...\r\n 741 936 267 316 335 222];\r\nassert(isequal(coolers(COST),1598))\r\n%%\r\nCOST = [204 470 675 379 889 587;...\r\n 625 379 552 618 563 969;...\r\n 726 341 52 563 195 582;...\r\n 835 64 308 831 222 100;...\r\n 19 762 966 958 706 167;...\r\n 203 403 932 76 597 103];\r\nassert(isequal(coolers(COST),993))\r\n%%\r\nCOST = [147 800 210 627 782 774;...\r\n 672 400 481 412 936 748;...\r\n 640 756 113 639 740 319;...\r\n 373 296 133 857 254 511;...\r\n 163 641 65 764 720 774;...\r\n 390 886 80 977 694 573];\r\nassert(isequal(coolers(COST),1784))\r\n%%\r\nCOST = [954 463 111 359 365 588;...\r\n 172 130 200 135 400 778;...\r\n 908 550 163 999 927 658;...\r\n 753 970 37 514 496 529;...\r\n 287 443 273 388 610 826;...\r\n 628 591 231 250 5 963];\r\nassert(isequal(coolers(COST),1501))\r\n%%\r\nCOST = [314 306 715 905 664 101;...\r\n 798 19 460 387 603 287;...\r\n 286 163 920 604 657 355;...\r\n 15 444 989 561 310 536;...\r\n 95 767 933 846 332 991;...\r\n 329 682 462 285 189 29];\r\nassert(isequal(coolers(COST),1729))\r\n%%\r\nCOST = [710 876 77 818 359 455;...\r\n 906 865 377 174 364 30;...\r\n 866 356 150 677 268 638;...\r\n 120 632 35 876 338 60;...\r\n 956 865 783 758 87 170;...\r\n 441 21 328 230 452 685];\r\nassert(isequal(coolers(COST),1098))\r\n%%\r\nCOST = [555 461 15 279 896 625;...\r\n 7 371 5 645 888 207;...\r\n 289 825 167 288 394 110;...\r\n 377 538 366 322 676 566;...\r\n 147 818 719 156 253 275;...\r\n 75 461 160 388 950 72];\r\nassert(isequal(coolers(COST),1175))\r\n%%\r\nCOST = [159 896 447 617 948 551;...\r\n 50 608 812 304 334 161;...\r\n 681 15 145 90 390 118;...\r\n 782 345 975 522 151 399;...\r\n 808 534 834 826 334 832;...\r\n 266 628 341 764 554 186];\r\nassert(isequal(coolers(COST),569))\r\n%%\r\nCOST = [501 491 579 185 613 662;...\r\n 127 887 776 198 738 896;...\r\n 865 906 662 862 304 275;...\r\n 767 499 470 126 43 998;...\r\n 565 530 220 646 836 835;...\r\n 390 910 603 438 370 792];\r\nassert(isequal(coolers(COST),1584))\r\n%%\r\nCOST = [657 988 206 794 242 185;...\r\n 544 789 495 682 210 88;...\r\n 387 853 32 651 273 309;...\r\n 823 486 828 238 776 231;...\r\n 596 874 265 478 332 910;...\r\n 782 334 678 937 604 937];\r\nassert(isequal(coolers(COST),1504))\r\n%%\r\nCOST = [32 626 369 811 16 974;...\r\n 594 256 338 724 95 373;...\r\n 44 905 619 675 515 321;...\r\n 425 768 87 914 953 784;...\r\n 522 563 376 821 844 213;...\r\n 841 898 188 549 936 960];\r\nassert(isequal(coolers(COST),1717))\r\n%%\r\nCOST = [931 594 600 844 914 151;...\r\n 366 98 227 771 577 947;...\r\n 345 560 714 510 288 651;...\r\n 693 44 725 707 267 983;...\r\n 717 758 345 792 891 194;...\r\n 801 660 33 331 367 829];\r\nassert(isequal(coolers(COST),1626))\r\n%%\r\nCOST = [494 60 648 497 429 28;...\r\n 591 516 624 763 42 906;...\r\n 45 54 954 180 226 472;...\r\n 565 844 177 692 667 911;...\r\n 634 595 475 915 872 328;...\r\n 499 692 843 163 345 692];\r\nassert(isequal(coolers(COST),526))\r\n%%\r\nCOST = [18 334 151 424 616 681;...\r\n 513 633 839 529 102 394;...\r\n 332 416 727 276 670 392;...\r\n 337 577 109 828 348 207;...\r\n 954 960 162 793 670 725;...\r\n 751 616 298 274 296 820];\r\nassert(isequal(coolers(COST),1421))\r\n%%\r\nCOST = [927 854 101 228 459 109;...\r\n 515 430 413 358 952 652;...\r\n 194 926 925 925 133 147;...\r\n 603 374 56 503 71 694;...\r\n 199 699 385 804 707 626;...\r\n 411 589 241 351 932 44];\r\nassert(isequal(coolers(COST),1345))\r\n%%\r\nCOST = [798 582 93 769 788 680;...\r\n 987 602 334 283 125 10;...\r\n 437 253 652 650 158 129;...\r\n 345 655 504 923 415 764;...\r\n 49 61 531 331 688 842;...\r\n 226 941 380 814 658 747];\r\nassert(isequal(coolers(COST),1350))\r\n%%\r\nCOST = [945 618 338 215 587 443;...\r\n 920 559 552 293 939 692;...\r\n 224 731 744 450 619 760;...\r\n 510 64 413 241 251 400;...\r\n 788 493 508 264 103 568;...\r\n 523 230 129 892 547 206];\r\nassert(isequal(coolers(COST),1251))\r\n%%\r\nCOST = [944 708 393 379 767 434;...\r\n 513 657 243 634 337 103;...\r\n 277 779 282 32 626 237;...\r\n 230 581 905 921 327 972;...\r\n 953 504 703 470 312 699;...\r\n 690 879 854 520 647 660];\r\nassert(isequal(coolers(COST),1565))\r\n%%\r\nCOST = [327 450 172 573 377 40;...\r\n 20 769 722 832 394 565;...\r\n 571 628 90 348 429 50;...\r\n 37 315 918 780 424 873;...\r\n 265 293 430 709 219 861;...\r\n 191 79 80 591 497 339];\r\nassert(isequal(coolers(COST),1224))\r\n%%\r\nCOST = [975 347 346 214 761 636;...\r\n 955 234 903 936 580 803;...\r\n 989 756 13 970 622 670;...\r\n 158 226 133 376 490 472;...\r\n 3 449 389 985 221 945;...\r\n 249 260 841 123 145 592];\r\nassert(isequal(coolers(COST),1311))\r\n%%\r\nCOST = [227 932 883 581 327 592;...\r\n 684 697 780 627 288 1;...\r\n 321 96 19 112 497 904;...\r\n 749 246 771 214 182 683;...\r\n 999 935 686 37 934 74;...\r\n 270 480 868 445 941 997];\r\nassert(isequal(coolers(COST),804))\r\n%%\r\nCOST = [315 176 835 189 254 788;...\r\n 810 588 997 578 287 438;...\r\n 462 155 973 629 126 551;...\r\n 588 446 746 365 529 987;...\r\n 478 538 200 637 821 690;...\r\n 553 462 37 892 300 971];\r\nassert(isequal(coolers(COST),2037))\r\n%%\r\nCOST = [96 320 833 203 939 1;...\r\n 899 796 571 493 975 484;...\r\n 59 302 71 730 608 148;...\r\n 124 608 267 955 958 315;...\r\n 631 462 171 763 331 172;...\r\n 371 293 561 81 902 593];\r\nassert(isequal(coolers(COST),1341))\r\n%%\r\nCOST = [658 16 262 188 656 56;...\r\n 349 974 894 744 782 820;...\r\n 54 735 973 719 863 107;...\r\n 48 753 995 189 786 7;...\r\n 56 34 347 434 434 276;...\r\n 72 118 293 46 348 943];\r\nassert(isequal(coolers(COST),862))\r\n%%\r\nCOST = [675 962 293 837 855 788;...\r\n 386 823 439 42 174 175;...\r\n 857 11 399 424 536 720;...\r\n 675 142 138 76 295 233;...\r\n 609 483 953 314 903 208;...\r\n 440 581 361 946 82 217];\r\nassert(isequal(coolers(COST),1114))\r\n%%\r\nCOST = [977 461 237 907 842 269;...\r\n 382 4 832 993 684 981;...\r\n 636 465 438 391 172 313;...\r\n 47 825 508 827 238 572;...\r\n 233 872 186 955 435 612;...\r\n 875 302 152 894 26 902];\r\nassert(isequal(coolers(COST),1700))\r\n%%\r\nCOST = [397 297 634 847 995 269;...\r\n 693 572 965 41 429 758;...\r\n 205 317 201 330 268 610;...\r\n 63 810 599 710 473 967;...\r\n 623 366 84 861 161 774;...\r\n 689 699 545 501 159 573];\r\nassert(isequal(coolers(COST),1320))\r\n%%\r\nCOST = [451 355 996 87 199 597;...\r\n 288 581 84 520 657 323;...\r\n 753 299 469 553 699 831;...\r\n 95 714 94 686 460 123;...\r\n 107 361 726 300 158 252;...\r\n 735 719 913 602 422 938];\r\nassert(isequal(coolers(COST),1069))\r\n\r\n","published":true,"deleted":false,"likes_count":7,"comments_count":2,"created_by":255320,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":56,"test_suite_updated_at":"2020-04-03T23:22:52.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-01T22:04:29.000Z","updated_at":"2026-02-26T16:11:43.000Z","published_at":"2020-04-01T22:55:22.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn a certain chemical plant, 6 new pieces of cooling equipment (coolers) are to be installed in a vacant space. This vacant space was divided into a grid of 6 cells by 6 cells. Your task is to assign the 6 coolers to 6 cells in this grid with the following requirements:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere must be one cooler for every column in the grid.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you decide to install a cooler at row R, column C, then the cooler at column C+1 must be installed either on row R-1, R, or R+1 only. This is done to ease the connection of coolers by piping.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe total installation cost for the 6 coolers must be minimum.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this problem, you are given a cost matrix (COST) in relation to the third requirement above. COST is a 6 x 6 matrix where the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er,c\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e-th element is the cost of assigning any cooler to row\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, column\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (All the coolers are identical). Write a function that accepts the matrix COST, and output the value of the minimum cost of installation. You are ensured that all elements of COST are integers in the range [1,1000].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn the sample test case below, the optimal placement is at the following rows: 4,3,3,2,2,2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e COST = [695 766 710 119 752 548;\\n 318 796 755 499 256 139;\\n 951 187 277 960 506 150;\\n 35 490 680 341 700 258;\\n 439 446 656 586 891 841;\\n 382 647 163 224 960 255];\\n\u003e\u003e coolers(COST)\\nans = \\n 1393]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMeanwhile, the optimal placement for the case below is at rows: 5,6,5,6,5,6\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e COST = [815 617 918 76 569 312;\\n 244 474 286 54 470 529;\\n 930 352 758 531 455 988;\\n 350 831 754 780 12 602;\\n 197 586 381 935 163 263;\\n 252 550 568 130 795 100];\\n\u003e\u003e coolers(COST)\\nans = \\n 1521]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45321,"title":"Kolakoski Sequence","description":"This is a modified version of the kolakoski sequence. \r\n\r\n\r\nRefer to the problem \u003chttps://www.mathworks.com/matlabcentral/cody/problems/2371\u003e for the original one.\r\n\r\nfor this case,\r\n\r\n\r\nGiven a particular initial vector a and length x,\r\ngenerate the kolakoski sequence (first x terms) from that vector.\r\n\r\n\r\nFor example if a=[2,1] and x=10 ; the sequence would look like --\r\n\r\n\r\n 2 2 1 1 2 1 2 2 1 2 2","description_html":"\u003cp\u003eThis is a modified version of the kolakoski sequence.\u003c/p\u003e\u003cp\u003eRefer to the problem \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/2371\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/2371\u003c/a\u003e for the original one.\u003c/p\u003e\u003cp\u003efor this case,\u003c/p\u003e\u003cp\u003eGiven a particular initial vector a and length x,\r\ngenerate the kolakoski sequence (first x terms) from that vector.\u003c/p\u003e\u003cp\u003eFor example if a=[2,1] and x=10 ; the sequence would look like --\u003c/p\u003e\u003cpre\u003e 2 2 1 1 2 1 2 2 1 2 2\u003c/pre\u003e","function_template":"function y = kolakoski_seq_3(a,x)\r\n y = x;\r\nend","test_suite":"%%\r\na = [2,1];\r\nx=10;\r\ny_correct = [2 2 1 1 2 1 2 2 1 2 ];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [2,1];\r\nx=15;\r\ny_correct = [2 2 1 1 2 1 2 2 1 2 2 1 1 2 1 ];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [1,3,1,2];\r\nx=30;\r\ny_correct = [1, 3, 3, 3, 1, 1, 1, 2, 2, 2, 1, 3, 1, 2, 2, 1, 1, 3, 3, 1, 2, 2, 2, 1, 3, 3, 1, 1, 2, 1];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [4,2];\r\nx=15;\r\ny_correct = [ 4 4 4 4 2 2 2 2 4 4 4 4 2 2 2];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [1,3,2];\r\nx=15;\r\ny_correct = [1 3 3 3 2 2 2 1 1 1 3 3 2 2 1];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [1,3,2,1];\r\nx=30;\r\ny_correct = [1, 3, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 3, 3, 2, 2, 1, 1, 3, 2, 1, 1, 1, 1, 3, 3, 3, 2, 2, 1];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [3,12,1,5];\r\nx=40;\r\ny_correct = [3 3 3 12 12 12 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 12 12 12 12 12 12 12];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":5,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-02-11T22:04:19.000Z","updated_at":"2026-02-10T02:18:16.000Z","published_at":"2020-02-11T22:17:59.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a modified version of the kolakoski sequence.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRefer to the problem\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/2371\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/2371\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt; for the original one.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003efor this case,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a particular initial vector a and length x, generate the kolakoski sequence (first x terms) from that vector.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example if a=[2,1] and x=10 ; the sequence would look like --\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 2 2 1 1 2 1 2 2 1 2 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2126,"title":"Split bread like the Pharaohs - Egyptian fractions and greedy algorithm","description":"How would you split 5 loaves of bread among 8 people in all fairness? Get a hint from the Pharaohs. 5/8 = 4/8 + 1/8 , i.e. each receives 1/2 of loaf plus 1/8 - splitting 4 loaves into 1/2 and then the last loaf into 8 pieces.\r\nEgyptian fraction is writing any rational number p/q in terms of unity fractions;\r\ne.g. \r\n\r\n 3/4 = 1/2 + 1/4, OR \r\n = 1/3 + 1/4 + 1/6\r\n\r\n 13/48 = 1/4 + 1/48\r\nWrite a program to return the Egyptian fraction of a given rational number p, q. You outputs in the above cases should be the series of denominator values,\r\n i.e. egyptian_fraction( 13, 48) should return [4,48],\r\nYou can use simple greedy algorithm or alternatives.\r\nReferences\r\nhttp://en.wikipedia.org/wiki/Egyptian_fraction\r\nAMS blog post by Tyler Clark (@tylermath12) http://blogs.ams.org/jmm2014/2014/01/17/friday-morning-math-fun/\r\nBonus points if you can enumerate all possible Egyptian fractions of (p,q), but thats a problem for another day.\r\nP.S: Updated test suite to check for proper solutions only","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 460.333px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 230.167px; transform-origin: 407px 230.167px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHow would you split 5 loaves of bread among 8 people in all fairness? Get a hint from the Pharaohs. 5/8 = 4/8 + 1/8 , i.e. each receives 1/2 of loaf plus 1/8 - splitting 4 loaves into 1/2 and then the last loaf into 8 pieces.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 242px 8px; transform-origin: 242px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEgyptian fraction is writing any rational number p/q in terms of unity fractions;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.6px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 61.3px; transform-origin: 404px 61.3px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ee.g. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 112px 8.5px; tab-size: 4; transform-origin: 112px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 3/4 = 1/2 + 1/4, OR \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 116px 8.5px; tab-size: 4; transform-origin: 116px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e = 1/3 + 1/4 + 1/6\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 13/48 = 1/4 + 1/48\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 383.5px 8px; transform-origin: 383.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a program to return the Egyptian fraction of a given rational number p, q. You outputs in the above cases should be the series of denominator values,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 236px 8.5px; tab-size: 4; transform-origin: 236px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 180px 8.5px; transform-origin: 180px 8.5px; \"\u003e i.e. egyptian_fraction( 13, 48) should \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 52px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 52px 8.5px; \"\u003ereturn [4,48]\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 165px 8px; transform-origin: 165px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou can use simple greedy algorithm or alternatives.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 41px 8px; transform-origin: 41px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eReferences\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 61.3px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ehttp://en.wikipedia.org/wiki/Egyptian_fraction\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 142px 8px; transform-origin: 142px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAMS blog post by Tyler Clark (@tylermath12)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ehttp://blogs.ams.org/jmm2014/2014/01/17/friday-morning-math-fun/\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 347px 8px; transform-origin: 347px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBonus points if you can enumerate all possible Egyptian fractions of (p,q), but thats a problem for another day.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 178px 8px; transform-origin: 178px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eP.S: Updated test suite to check for proper solutions only\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function denoms = egyptian_fraction(p,q)\r\n denoms = [];\r\n \r\n if ( p == 1 )\r\n denoms(end+1) = q;\r\n return;\r\n end\r\n % treat other cases..\r\nend","test_suite":"%%\r\n% Updated test suite to remove trivial solutions;\r\n\r\n%%\r\n Vmin = 13; Vmax = 48;\r\n arr = egyptian_fraction(Vmin,Vmax);\r\n assert(isequal(arr, [4 48]))\r\n \r\n%%\r\n Vmin = 3; Vmax = 4;\r\n arr = egyptian_fraction(Vmin,Vmax);\r\n assert(isequal(arr, [2 4]) || isequal(arr, [3 4 6]))\r\n \r\n%%\r\n Vmin = 2;\r\n in = primes(20);\r\n Vmax = in(randi(numel(in)));\r\n arr = egyptian_fraction(Vmin,Vmax);\r\n l=lcm(arr(1),arr(2));\r\n for k=3:numel(arr)\r\n l=lcm(l,arr(k));\r\n end\r\n assert(isequal(sum(arr),sum(l./arr)/(Vmax/l)))\r\n \r\n \r\n%%\r\n% Small\r\n Vmin = 10; Vmax = 55;\r\n denom = floor(unique(egyptian_fraction(Vmin,Vmax)));\r\n\r\n egyptian_value = sum(1./denom);\r\n\r\n rel_tol = Vmin/Vmax*1e-6;\r\n actual_error = abs( egyptian_value - Vmin/Vmax );\r\n assert(isequal(actual_error \u003c rel_tol ,true))\r\n\r\n%%\r\n% Pie\r\n Vmin = 113; Vmax = 355;\r\n denom = floor(unique(egyptian_fraction(Vmin,Vmax)));\r\n\r\n egyptian_value = sum(1./denom);\r\n\r\n rel_tol = Vmin/Vmax*1e-6;\r\n actual_error = abs( egyptian_value - Vmin/Vmax );\r\n assert(isequal(actual_error \u003c rel_tol ,true))\r\n\r\n%%\r\n% Ramanujan\r\n Vmin = 1023; Vmax = 1729;\r\n denom = floor(unique(egyptian_fraction(Vmin,Vmax)));\r\n\r\n egyptian_value = sum(1./denom);\r\n\r\n rel_tol = Vmin/Vmax*1e-6;\r\n actual_error = abs( egyptian_value - Vmin/Vmax );\r\n assert(isequal(actual_error \u003c rel_tol ,true))\r\n\r\n%%\r\n% E\r\n Vmin = 27; Vmax = 183;\r\n denom = floor(unique(egyptian_fraction(Vmin,Vmax)));\r\n egyptian_value = sum(1./denom);\r\n\r\n rel_tol = Vmin/Vmax*1e-6;\r\n actual_error = abs( egyptian_value - Vmin/Vmax );\r\n assert(isequal(actual_error \u003c rel_tol ,true))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":5,"created_by":3378,"edited_by":223089,"edited_at":"2023-03-29T14:08:28.000Z","deleted_by":null,"deleted_at":null,"solvers_count":96,"test_suite_updated_at":"2023-03-29T14:08:28.000Z","rescore_all_solutions":false,"group_id":38,"created_at":"2014-01-19T06:15:39.000Z","updated_at":"2026-03-31T17:38:29.000Z","published_at":"2014-01-19T17:44:14.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHow would you split 5 loaves of bread among 8 people in all fairness? Get a hint from the Pharaohs. 5/8 = 4/8 + 1/8 , i.e. each receives 1/2 of loaf plus 1/8 - splitting 4 loaves into 1/2 and then the last loaf into 8 pieces.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEgyptian fraction is writing any rational number p/q in terms of unity fractions;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[e.g. \\n\\n 3/4 = 1/2 + 1/4, OR \\n = 1/3 + 1/4 + 1/6\\n\\n 13/48 = 1/4 + 1/48]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a program to return the Egyptian fraction of a given rational number p, q. You outputs in the above cases should be the series of denominator values,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ i.e. egyptian_fraction( 13, 48) should return [4,48],]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can use simple greedy algorithm or alternatives.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eReferences\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://en.wikipedia.org/wiki/Egyptian_fraction\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAMS blog post by Tyler Clark (@tylermath12)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://blogs.ams.org/jmm2014/2014/01/17/friday-morning-math-fun/\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBonus points if you can enumerate all possible Egyptian fractions of (p,q), but thats a problem for another day.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eP.S: Updated test suite to check for proper solutions only\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45463,"title":"Word Ladder","description":"Given a set of words, and two other words - start and destination,\r\n\r\nFind the smallest chain from start to the destination such that adjacent words in the chain only differ by one character and each word in the chain exists in the set. \r\n\r\nAll the words are of the same length.\r\nThe starting word is not in the set but destination word would be.\r\n\r\nFor example, \r\n\r\n Start = 'COLD'\r\n Destination = 'WARM'\r\n set ={ CORD CARD DART FORT WARM FARM WARD}\r\n\r\n COLD → CORD → CARD → WARD → WARM","description_html":"\u003cp\u003eGiven a set of words, and two other words - start and destination,\u003c/p\u003e\u003cp\u003eFind the smallest chain from start to the destination such that adjacent words in the chain only differ by one character and each word in the chain exists in the set.\u003c/p\u003e\u003cp\u003eAll the words are of the same length.\r\nThe starting word is not in the set but destination word would be.\u003c/p\u003e\u003cp\u003eFor example,\u003c/p\u003e\u003cpre\u003e Start = 'COLD'\r\n Destination = 'WARM'\r\n set ={ CORD CARD DART FORT WARM FARM WARD}\u003c/pre\u003e\u003cpre\u003e COLD → CORD → CARD → WARD → WARM\u003c/pre\u003e","function_template":"function out2 = word_lad_2(ary,st,des)","test_suite":"%%\r\nary={ 'CORD', 'CARD', 'DART', 'FORT', 'WARM', 'FARM', 'WARD'}\r\nst='COLD'\r\ndes='WARM'\r\ny_correct = {'CORD','CARD','WARD','WARM'};\r\nassert(isequal(word_lad_2(ary,st,des),y_correct))\r\n\r\n%%\r\nary={'pan','can','fan','pat','mat','fat','lot','opt','apt','act','ape','put','aut'}\r\nst='man'\r\ndes='ape'\r\ny_correct = {'pan'\t'pat'\t'put'\t'aut'\t'apt'\t'ape'};\r\nassert(isequal(word_lad_2(ary,st,des),y_correct))\r\n\r\n%%\r\nary={'leg','hot','dot','dog','lot','log','cog','hog','zog','fog'}\r\nst='hit'\r\ndes='fog'\r\ny_correct = {'hot','hog','fog'};\r\nassert(isequal(word_lad_2(ary,st,des),y_correct))\r\n\r\n%%\r\nary={'safer', 'rifer','upper','rifre', 'rider','tider' ,'cider','coder','loder', 'cooer', 'cooey', 'gooey', 'goosy' , 'goose' ,'loose','nosey','doose'}\r\nst='refer'\r\ndes='loose'\r\ny_correct = {'rifer'\t'rider'\t'cider'\t'coder'\t'cooer'\t'cooey'\t'gooey'\t'goosy'\t'goose'\t'loose'};\r\nassert(isequal(word_lad_2(ary,st,des),y_correct))\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2020-04-16T07:12:17.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-16T07:11:43.000Z","updated_at":"2026-02-10T01:03:52.000Z","published_at":"2020-04-16T07:12:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a set of words, and two other words - start and destination,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the smallest chain from start to the destination such that adjacent words in the chain only differ by one character and each word in the chain exists in the set.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll the words are of the same length. The starting word is not in the set but destination word would be.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Start = 'COLD'\\n Destination = 'WARM'\\n set ={ CORD CARD DART FORT WARM FARM WARD}\\n\\n COLD → CORD → CARD → WARD → WARM]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45473,"title":"Sub-sequence - 01","description":"Find the length of the longest increasing subsequence in the given array.\r\n\r\n a=[2,4,2,1,3,5,6]\r\n longest increasing subsequence = [2,4,5,6]\r\n = [1,3,5,6]\r\n = [2,3,5,6]\r\n so, length = 4","description_html":"\u003cp\u003eFind the length of the longest increasing subsequence in the given array.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ea=[2,4,2,1,3,5,6]\r\nlongest increasing subsequence = [2,4,5,6]\r\n = [1,3,5,6]\r\n = [2,3,5,6]\r\nso, length = 4\r\n\u003c/pre\u003e","function_template":"function yy = longest_sub_2(x)","test_suite":"%%\r\nx =[2,4,2,1,3,5,6];\r\nassert(isequal(longest_sub_2(x),4))\r\n\r\n%%\r\nx=[15,27,14,38,26,55,46,65,85];\r\nassert(isequal(longest_sub_2(x),6))\r\n\r\n%%\r\nx =[2,4,2,1,3,5,6,12,0,2,1,1,2,1,4,5,6,12,3,2];\r\nassert(isequal(longest_sub_2(x),7))\r\n\r\n%%\r\nx =[82\t91\t13\t92\t64\t10\t28\t55\t96\t97\t16\t98\t96\t49\t81\t15\t43\t92\t80\t96\t66\t4\t85\t94\t68];\r\nassert(isequal(longest_sub_2(x),6))\r\n\r\n%%\r\nx =[758\t744\t393\t656\t172\t707\t32\t277\t47\t98\t824\t695\t318\t951\t35\t439\t382\t766\t796\t187\t490\t446\t647\t710\t755\t277\t680\t656\t163\t119\t499\t960\t341\t586\t224\t752\t256\t506\t700\t891\t960\t548\t139\t150\t258\t841\t255\t815\t244\t930];\r\nassert(isequal(longest_sub_2(x),11))\r\n\r\n%%\r\nx =ones(1,1000);\r\nassert(isequal(longest_sub_2(x),1))\r\n\r\n%%\r\nx =[ones(1,1000),zeros(1,50),2*ones(1,20)];\r\nassert(isequal(longest_sub_2(x),2))\r\n\r\n%%\r\nx=[34999 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9\t48268\t37602\t52379\t26488\t6836\t43633\t17386\t2611\t95468\t43060\t96156\t76242\t735\t68004\t70596\t64513\t55231\t21811\t77237\t22803\t37087\t89093\t85638\t40244\t31802\t60864\t91020\t90910\t59160\t33258\t85307\t44240\t90436\t3318\t53243\t71650\t17931\t33654\t18772\t32193\t40386\t54857\t4874\t55274\t27482\t24151\t24315\t15416\t95642];\r\nassert(isequal(longest_sub_2(x),72))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-23T15:37:38.000Z","updated_at":"2026-02-10T06:04:32.000Z","published_at":"2020-04-23T15:37:38.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the length of the longest increasing subsequence in the given array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[a=[2,4,2,1,3,5,6]\\nlongest increasing subsequence = [2,4,5,6]\\n = [1,3,5,6]\\n = [2,3,5,6]\\nso, length = 4]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":55305,"title":"Chain multiplication - 02","description":"Following up on the problem in 55295, you found the number of multiplications needed to multiply two matrices.\r\nNow, you are given a sequence of matrices. There are many different ways you can multiply the matrices. For example, \r\nsay, you are given 4 matrix - A, B, C, D. They can be multiplied as follows - A(B(CD)), A((BC)D), ((AB)C)D, (AB)(CD), (A(BC))D.\r\n\r\nyou have to figure out which is the optimal way of multiplying those matrices based on the mininum number of multiplications required. For example, consider a simple 3 matrix case.\r\nA(1,2), B(2,3), C(3,2)\r\nA(BC) =\u003e BC requires 12 multiplications; multiplying A matrix with the result requires 4 multiplications. Total = 12+4= 16.\r\n(AB)C =\u003e AB requires 6 multiplications; multiplying the result with the C matrix requires 6 multiplications. Total = 6+6= 12.\r\nTherefore, to multiply ABC - the optimal way is (AB)C requiring 12 multiplications in total.\r\n\r\nHere, you will be given an array 'a' containing the size of consequtive matrices. The output is the minimum number of multiplications required to multiply those matrices.\r\nhere, a = [2, 4, 6, 1] represents 3 matrices -- A(2,4), B(4,6), and C(6,1)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 414px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 207px; transform-origin: 407px 207px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFollowing up on the problem in \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/55295-chain-multiplication-01\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e55295\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, you found the number of multiplications needed to multiply two matrices.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eNow, you are given a sequence of matrices. There are many different ways you can multiply the matrices. For example, \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003esay, you are given 4 matrix - A, B, C, D. They can be multiplied as follows - A(B(CD)), A((BC)D), ((AB)C)D, (AB)(CD), (A(BC))D.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eyou have to figure out which is the optimal way of multiplying those matrices based on the mininum number of multiplications required. For example, consider a simple 3 matrix case.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA(1,2), B(2,3), C(3,2)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA(BC) =\u0026gt; BC requires 12 multiplications; multiplying A matrix with the result requires 4 multiplications. Total = 12+4= 16.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e(AB)C =\u0026gt; AB requires 6 multiplications; multiplying the result with the C matrix requires 6 multiplications. Total = 6+6= 12.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eTherefore, to multiply ABC - the optimal way is (AB)C requiring 12 multiplications in total.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eHere, you will be given an array 'a' containing the size of consequtive matrices. The output is the minimum number of multiplications required to multiply those matrices.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003ehere, a = [2, 4, 6, 1] represents 3 matrices -- A(2,4), B(4,6), and C(6,1)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = chain_mul_02(a)\r\n y = x;\r\nend","test_suite":"%%\r\na=[1,2,3,2];\r\ny_correct = 12;\r\nassert(isequal(chain_mul_02(a),y_correct))\r\n\r\n%%\r\na=[4,10,3,12,20,7];\r\ny_correct = 1344;\r\nassert(isequal(chain_mul_02(a),y_correct))\r\n\r\n\r\n%%\r\na=[1,2,3,4];\r\ny_correct = 18;\r\nassert(isequal(chain_mul_02(a),y_correct))\r\n\r\n\r\n%%\r\na=[81,213,78,96,2,1,98,102, 1200,4];\r\ny_correct = 179067;\r\nassert(isequal(chain_mul_02(a),y_correct))\r\n\r\n%%\r\na=[40, 20, 30, 10, 30];\r\ny_correct = 26000;\r\nassert(isequal(chain_mul_02(a),y_correct))\r\n\r\n%%\r\na=[7,1,5,4,2];\r\ny_correct = 42;\r\nassert(isequal(chain_mul_02(a),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":363598,"edited_at":"2022-08-14T21:58:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":21,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-08-14T20:23:49.000Z","updated_at":"2026-02-10T20:51:38.000Z","published_at":"2022-08-14T21:45:21.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFollowing up on the problem in \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/55295-chain-multiplication-01\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e55295\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, you found the number of multiplications needed to multiply two matrices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow, you are given a sequence of matrices. There are many different ways you can multiply the matrices. For example, \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003esay, you are given 4 matrix - A, B, C, D. They can be multiplied as follows - A(B(CD)), A((BC)D), ((AB)C)D, (AB)(CD), (A(BC))D.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eyou have to figure out which is the optimal way of multiplying those matrices based on the mininum number of multiplications required. For example, consider a simple 3 matrix case.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(1,2), B(2,3), C(3,2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(BC) =\u0026gt; BC requires 12 multiplications; multiplying A matrix with the result requires 4 multiplications. Total = 12+4= 16.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(AB)C =\u0026gt; AB requires 6 multiplications; multiplying the result with the C matrix requires 6 multiplications. Total = 6+6= 12.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTherefore, to multiply ABC - the optimal way is (AB)C requiring 12 multiplications in total.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, you will be given an array 'a' containing the size of consequtive matrices. The output is the minimum number of multiplications required to multiply those matrices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ehere, a = [2, 4, 6, 1] represents 3 matrices -- A(2,4), B(4,6), and C(6,1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45352,"title":"Number generator","description":"In this problem, a number will be given.\r\n\r\nstarting with the first digit, keep on adding all subsequent digits till the state (even or odd) of the sum of the digits is opposite to the state of the first digit. \r\n\r\nContinue this way till the last digit of the number is reached. \r\n\r\nConcatenate all the sums to generate the new number.\r\n\r\n for example, n=74561. \r\n we start with 7(odd); 7+4==11(odd); 11+5=16(even); y=16\r\n remaining 6(even); 6+1=7(odd); y=167\r\n","description_html":"\u003cp\u003eIn this problem, a number will be given.\u003c/p\u003e\u003cp\u003estarting with the first digit, keep on adding all subsequent digits till the state (even or odd) of the sum of the digits is opposite to the state of the first digit.\u003c/p\u003e\u003cp\u003eContinue this way till the last digit of the number is reached.\u003c/p\u003e\u003cp\u003eConcatenate all the sums to generate the new number.\u003c/p\u003e\u003cpre\u003e for example, n=74561. \r\n we start with 7(odd); 7+4==11(odd); 11+5=16(even); y=16\r\n remaining 6(even); 6+1=7(odd); y=167\u003c/pre\u003e","function_template":"function y=number_gen(x)\r\n y = x;\r\nend","test_suite":"%%\r\nassert(isequal(number_gen(74562),168))\r\n%%\r\nassert(isequal(number_gen(74561562),16713))\r\n%%\r\nassert(isequal(number_gen(111111111111),222222))\r\n%%\r\nassert(isequal(number_gen(1111111111111),2222221))\r\n%%\r\nassert(isequal(number_gen(3456696524532), 122111115))\r\n%%\r\nassert(isequal(number_gen(100000024008),15))\r\n%%\r\nassert(isequal(number_gen(288624888862),70))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-02-22T13:23:25.000Z","updated_at":"2025-12-04T12:36:29.000Z","published_at":"2020-02-22T16:12:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem, a number will be given.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003estarting with the first digit, keep on adding all subsequent digits till the state (even or odd) of the sum of the digits is opposite to the state of the first digit.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eContinue this way till the last digit of the number is reached.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConcatenate all the sums to generate the new number.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ for example, n=74561. \\n we start with 7(odd); 7+4==11(odd); 11+5=16(even); y=16\\n remaining 6(even); 6+1=7(odd); y=167]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45428,"title":"Minimal Path - 01","description":"Given a matrix, find the minimal path sum from the top left to the bottom right by only moving to the right and down.\r\n\r\nFor example,\r\n \r\n x=[ 2 2 2 2 2\r\n 10 10 10 1 2\r\n 20 20 20 1 2\r\n 30 30 30 30 2]\r\n\r\nHere, the minimum path sum is 14 [ 2+2+2+2+1+1+2+2 ]","description_html":"\u003cp\u003eGiven a matrix, find the minimal path sum from the top left to the bottom right by only moving to the right and down.\u003c/p\u003e\u003cp\u003eFor example,\u003c/p\u003e\u003cpre\u003e x=[ 2 2 2 2 2\r\n 10 10 10 1 2\r\n 20 20 20 1 2\r\n 30 30 30 30 2]\u003c/pre\u003e\u003cp\u003eHere, the minimum path sum is 14 [ 2+2+2+2+1+1+2+2 ]\u003c/p\u003e","function_template":"function y = minimal_path(x)","test_suite":"%%\r\nx = [2 2 2 2 2\r\n 10 10 10 1 2\r\n 20 20 20 1 2\r\n 30 30 30 30 2];\r\nassert(isequal(minimal_path(x),14))\r\n\r\n%%\r\nx = [2 2 2 2 2\r\n 0 0 10 1 2\r\n 20 0 20 1 2\r\n 30 0 0 3 2];\r\nassert(isequal(minimal_path(x),7))\r\n\r\n%%\r\nx = [100 20 30 40 50\r\n 60 70 80 90 100];\r\nassert(isequal(minimal_path(x),340))\r\n\r\n%%\r\nx = [11 111 23 45 67 -500 34 23\r\n 22 32 432 1234 12 1244 -544 44\r\n 1 2 3 4 5 6 7 8\r\n -12000 45 6 7 8 433 664 2344];\r\nassert(isequal(minimal_path(x),-8459))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-08T09:32:44.000Z","updated_at":"2026-01-06T08:45:42.000Z","published_at":"2020-04-08T09:32:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a matrix, find the minimal path sum from the top left to the bottom right by only moving to the right and down.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x=[ 2 2 2 2 2\\n 10 10 10 1 2\\n 20 20 20 1 2\\n 30 30 30 30 2]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, the minimum path sum is 14 [ 2+2+2+2+1+1+2+2 ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":795,"title":"Joining Ranges","description":"You are given a n-by-2 matrix. Each row represents a numeric range, e.g.\r\n\r\n x = [0 5; 10 3; 20 15; 16 19; 25 25]\r\n \r\ncontains ranges [0 5], [10 3], [20 15], [16 19], and [25 25]. Note that the first column does not always contain the smaller number. Join all overlapping ranges and return the sorted (both columns and all rows must be sorted) matrix of joined ranges\r\n\r\n y = [0 10; 15 20; 25 25]\r\n\r\ni.e. ranges [0 5] and [10 3] are combined to [0 10], range [16 19] is completely overlapped by [15 20] and [25 25] is kept because it is a separate range.","description_html":"\u003cp\u003eYou are given a n-by-2 matrix. Each row represents a numeric range, e.g.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ex = [0 5; 10 3; 20 15; 16 19; 25 25]\r\n\u003c/pre\u003e\u003cp\u003econtains ranges [0 5], [10 3], [20 15], [16 19], and [25 25]. Note that the first column does not always contain the smaller number. Join all overlapping ranges and return the sorted (both columns and all rows must be sorted) matrix of joined ranges\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ey = [0 10; 15 20; 25 25]\r\n\u003c/pre\u003e\u003cp\u003ei.e. ranges [0 5] and [10 3] are combined to [0 10], range [16 19] is completely overlapped by [15 20] and [25 25] is kept because it is a separate range.\u003c/p\u003e","function_template":"function y = joinRanges(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = [0 5; 10 3; 20 15; 16 19; 25 25];\r\ny_correct = [0 10;15 20;25 25];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n%%\r\nx = [-10 -5; 0 -8; -1 5]; \r\ny_correct = [-10 5];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n%%\r\nx = [-50 0; 0 50; 100 50; -50 -100]; \r\ny_correct = [-100 100];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n\r\n%%\r\nx = [99 51; -49 -1; -51 -99; 1 49]; \r\ny_correct = [-99 -51;-49 -1;1 49;51 99];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n\r\n%%\r\nx = [-inf inf]; \r\ny_correct = x;\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n%%\r\nx = [0 -42; -inf -10; inf 42]; \r\ny_correct = [-Inf 0;42 Inf];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n%%\r\nx = [36.154 63.178; 12.007 -5.156; -0.519 17.651]; \r\ny_correct = [-5.156 17.651;36.154 63.178];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n%%\r\nassert(isempty(strfind(evalc('type joinRanges'), 'regexp')));","published":true,"deleted":false,"likes_count":9,"comments_count":3,"created_by":4976,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":400,"test_suite_updated_at":"2013-10-20T11:57:08.000Z","rescore_all_solutions":false,"group_id":12,"created_at":"2012-06-27T16:04:34.000Z","updated_at":"2026-04-10T23:50:24.000Z","published_at":"2012-06-27T16:10:37.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are given a n-by-2 matrix. Each row represents a numeric range, e.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[x = [0 5; 10 3; 20 15; 16 19; 25 25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003econtains ranges [0 5], [10 3], [20 15], [16 19], and [25 25]. Note that the first column does not always contain the smaller number. Join all overlapping ranges and return the sorted (both columns and all rows must be sorted) matrix of joined ranges\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[y = [0 10; 15 20; 25 25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ei.e. ranges [0 5] and [10 3] are combined to [0 10], range [16 19] is completely overlapped by [15 20] and [25 25] is kept because it is a separate range.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45389,"title":"Knight's Watch","description":" \"Night gathers, and now my watch begins\"\r\n\r\nA knight is placed on an n-by-n sized chessboard at the position x. Find the probability that after k steps, the knight will remain within the chessboard.\r\n\r\nAny knight's move that places him outside the board should be considered invalid.\r\n\r\n For simplicity, the knight's position on the chessboard is defined with the numeric\r\n notation instead of algebraic notation. so 'Ka1' is represented as (1,1).\r\n\r\nBrief explanation:\r\n\r\n Say the knight is placed in pos-(1,1). A knight has 8 possible moves. So in the next move, \r\nthe Knight can go to 8 different positions in the chessboard. But among them, only 2\r\n positions are valid i.e. the knight remains within the chessboard and they are -\r\n(3,2) \u0026 (2,3). So the prob. is 2/8 after 1 move. What will be the probability after k moves?\r\n\r\n","description_html":"\u003cpre class=\"language-matlab\"\u003e\"Night gathers, and now my watch begins\"\r\n\u003c/pre\u003e\u003cp\u003eA knight is placed on an n-by-n sized chessboard at the position x. Find the probability that after k steps, the knight will remain within the chessboard.\u003c/p\u003e\u003cp\u003eAny knight's move that places him outside the board should be considered invalid.\u003c/p\u003e\u003cpre\u003e For simplicity, the knight's position on the chessboard is defined with the numeric\r\n notation instead of algebraic notation. so 'Ka1' is represented as (1,1).\u003c/pre\u003e\u003cp\u003eBrief explanation:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eSay the knight is placed in pos-(1,1). A knight has 8 possible moves. So in the next move, \r\nthe Knight can go to 8 different positions in the chessboard. But among them, only 2\r\npositions are valid i.e. the knight remains within the chessboard and they are -\r\n(3,2) \u0026 (2,3). So the prob. is 2/8 after 1 move. What will be the probability after k moves?\r\n\u003c/pre\u003e","function_template":"function prob = knights_watch(x,n,k)","test_suite":"%%\r\nx =[1,1];\r\nassert(isequal(knights_watch(x,3,2),0.0625))\r\n%%\r\nx =[1,1];\r\nassert(isequal(knights_watch(x,4,4),0.0176))\r\n%%\r\nx =[6,4];\r\nassert(isequal(knights_watch(x,6,9),0.012))\r\n%%\r\nx =[6,4];\r\nassert(isequal(knights_watch(x,8,25),0.0011))\r\n%%\r\nx =[8,8];\r\nassert(isequal(knights_watch(x,8,15),0.0042))\r\n%%\r\nx =[8,8];\r\nassert(isequal(knights_watch(x,16,15),0.4666))\r\n%%\r\nx =[3,1];\r\nassert(isequal(knights_watch(x,16,50),0.0037))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-03-25T18:55:22.000Z","updated_at":"2026-01-23T12:14:39.000Z","published_at":"2020-03-25T18:55:22.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\\\"Night gathers, and now my watch begins\\\"]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA knight is placed on an n-by-n sized chessboard at the position x. Find the probability that after k steps, the knight will remain within the chessboard.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAny knight's move that places him outside the board should be considered invalid.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ For simplicity, the knight's position on the chessboard is defined with the numeric\\n notation instead of algebraic notation. so 'Ka1' is represented as (1,1).]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBrief explanation:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Say the knight is placed in pos-(1,1). A knight has 8 possible moves. So in the next move, \\nthe Knight can go to 8 different positions in the chessboard. But among them, only 2\\npositions are valid i.e. the knight remains within the chessboard and they are -\\n(3,2) \u0026 (2,3). So the prob. is 2/8 after 1 move. What will be the probability after k moves?]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45426,"title":"The Tortoise and the Hare - 02","description":"Previous problem \u003chttps://www.mathworks.com/matlabcentral/cody/problems/45425-the-tortoise-and-the-hare-01\u003e\r\n\r\nSuppose in an infinitely long line, the tortoise is standing in position 0.\r\n\r\nFrom that place, it can move in both +ve and -ve direction. The condition is that, in i-th jump, it can move i step forward or backward. \r\n\r\nSo one possible scenario can be -\r\n\r\n 0 [i=1] --- 1 step forward\r\n 1 [i=2] --- 2 step forward\r\n 3 [i=3] --- 3 step forward\r\n 6 [i=4] --- 4 step backward\r\n 2 [i=5] --- 5 step forward\r\n 7 [i=6] --- 6 step backward\r\n 1 [i=7] --- 7 step forward\r\n 8\r\n\r\nIf you look carefully, you'll find that -- If the tortoise moves this way, it'll always be able to reach any destination (x). \r\n\r\nThe question is what is the minimum number of moves it'll take to reach destination x.\r\n\r\nFor example -- \r\n\r\n if x=8\r\n \u003e\u003e in the above example, it takes 7 steps\r\n \u003e\u003e but if it moves this way -- [0,-1,1,4,8] -- steps required = 4.\r\n\r\nSo 4 is the optimum way.\r\n","description_html":"\u003cp\u003ePrevious problem \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45425-the-tortoise-and-the-hare-01\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45425-the-tortoise-and-the-hare-01\u003c/a\u003e\u003c/p\u003e\u003cp\u003eSuppose in an infinitely long line, the tortoise is standing in position 0.\u003c/p\u003e\u003cp\u003eFrom that place, it can move in both +ve and -ve direction. The condition is that, in i-th jump, it can move i step forward or backward.\u003c/p\u003e\u003cp\u003eSo one possible scenario can be -\u003c/p\u003e\u003cpre\u003e 0 [i=1] --- 1 step forward\r\n 1 [i=2] --- 2 step forward\r\n 3 [i=3] --- 3 step forward\r\n 6 [i=4] --- 4 step backward\r\n 2 [i=5] --- 5 step forward\r\n 7 [i=6] --- 6 step backward\r\n 1 [i=7] --- 7 step forward\r\n 8\u003c/pre\u003e\u003cp\u003eIf you look carefully, you'll find that -- If the tortoise moves this way, it'll always be able to reach any destination (x).\u003c/p\u003e\u003cp\u003eThe question is what is the minimum number of moves it'll take to reach destination x.\u003c/p\u003e\u003cp\u003eFor example --\u003c/p\u003e\u003cpre\u003e if x=8\r\n \u0026gt;\u0026gt; in the above example, it takes 7 steps\r\n \u0026gt;\u0026gt; but if it moves this way -- [0,-1,1,4,8] -- steps required = 4.\u003c/pre\u003e\u003cp\u003eSo 4 is the optimum way.\u003c/p\u003e","function_template":"function y = rabbit(n)","test_suite":"%%\r\nassert(isequal(rabbit(8),4))\r\n%%\r\nassert(isequal(rabbit(18),7))\r\n%%\r\nassert(isequal(rabbit(-600),35))\r\n%%\r\nassert(isequal(rabbit(6600),115))\r\n%%\r\nassert(isequal(rabbit(99999),449))\r\n%%\r\nassert(isequal(rabbit(-16),7))\r\n%%\r\nassert(isequal(rabbit(45237929),9513))\r\n%%\r\nassert(isequal(rabbit(46),11))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":5,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":24,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-07T05:20:53.000Z","updated_at":"2026-03-30T18:12:01.000Z","published_at":"2020-04-07T05:20:53.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrevious problem\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45425-the-tortoise-and-the-hare-01\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45425-the-tortoise-and-the-hare-01\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSuppose in an infinitely long line, the tortoise is standing in position 0.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFrom that place, it can move in both +ve and -ve direction. The condition is that, in i-th jump, it can move i step forward or backward.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo one possible scenario can be -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 0 [i=1] --- 1 step forward\\n 1 [i=2] --- 2 step forward\\n 3 [i=3] --- 3 step forward\\n 6 [i=4] --- 4 step backward\\n 2 [i=5] --- 5 step forward\\n 7 [i=6] --- 6 step backward\\n 1 [i=7] --- 7 step forward\\n 8]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you look carefully, you'll find that -- If the tortoise moves this way, it'll always be able to reach any destination (x).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe question is what is the minimum number of moves it'll take to reach destination x.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example --\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ if x=8\\n \u003e\u003e in the above example, it takes 7 steps\\n \u003e\u003e but if it moves this way -- [0,-1,1,4,8] -- steps required = 4.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo 4 is the optimum way.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45422,"title":"Coin Distribution - 02 ","description":"Prev prob \u003chttps://www.mathworks.com/matlabcentral/cody/problems/45385-coin-distribution\u003e\r\n\r\nGiven a set of coins and an amount, find out how many ways the amount can be made using the coins given.\r\nAssume, there is an infinite supply of all the coins.\r\n\r\nFor instance,\r\n\r\n Amount = 10\r\n Coins = [ 2,3,5]\r\n\r\n possible ways are - [2,2,2,2,2],[2,3,5],[5,5],[2,2,3,3]\r\n so total no. of ways = 4.","description_html":"\u003cp\u003ePrev prob \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45385-coin-distribution\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45385-coin-distribution\u003c/a\u003e\u003c/p\u003e\u003cp\u003eGiven a set of coins and an amount, find out how many ways the amount can be made using the coins given.\r\nAssume, there is an infinite supply of all the coins.\u003c/p\u003e\u003cp\u003eFor instance,\u003c/p\u003e\u003cpre\u003e Amount = 10\r\n Coins = [ 2,3,5]\u003c/pre\u003e\u003cpre\u003e possible ways are - [2,2,2,2,2],[2,3,5],[5,5],[2,2,3,3]\r\n so total no. of ways = 4.\u003c/pre\u003e","function_template":"function out = coin_lev(coins,amount)","test_suite":"%%\r\ncoins= [ 2,3,5];\r\namount = 10;\r\nassert(isequal(coin_lev(coins,amount),4))\r\n\r\n%%\r\ncoins= [2,3,5,10];\r\namount = 15;\r\nassert(isequal(coin_lev(coins,amount),9))\r\n\r\n%%\r\ncoins= [ 2,3,5];\r\namount = 50;\r\nassert(isequal(coin_lev(coins,amount),51))\r\n\r\n%%\r\ncoins= [2,5,10,1,20];\r\namount = 1225;\r\nassert(isequal(coin_lev(coins,amount),49884828))\r\n\r\n%%\r\ncoins= [ 11,19,23];\r\namount = 12252;\r\nassert(isequal(coin_lev(coins,amount),15681))\r\n%%\r\ncoins= [ 11,19,23,100];\r\namount = 50;\r\nassert(isequal(coin_lev(coins,amount),0))\r\n\r\n%%\r\ncoins= [1,2,3,4,5,8,10,15,20,25];\r\namount = 200;\r\nassert(isequal(coin_lev(coins,amount),119495730))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":"2020-04-02T19:38:33.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-02T19:03:04.000Z","updated_at":"2026-02-09T20:05:28.000Z","published_at":"2020-04-02T19:38:33.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev prob\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45385-coin-distribution\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45385-coin-distribution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a set of coins and an amount, find out how many ways the amount can be made using the coins given. Assume, there is an infinite supply of all the coins.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor instance,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Amount = 10\\n Coins = [ 2,3,5]\\n\\n possible ways are - [2,2,2,2,2],[2,3,5],[5,5],[2,2,3,3]\\n so total no. of ways = 4.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45474,"title":"Sub-sequence - 02","description":"Given two sequences, find the length of the longest common subsequence.\r\n\r\n a=[1,1,1,1,1,2,3,1,4]\r\n b=[2,3,0,0,9,5,4,1]\r\n longest Common subsequence = [2,3,4]\r\n = [2,3,1]\r\nso,length=3","description_html":"\u003cp\u003eGiven two sequences, find the length of the longest common subsequence.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ea=[1,1,1,1,1,2,3,1,4]\r\nb=[2,3,0,0,9,5,4,1]\r\nlongest Common subsequence = [2,3,4]\r\n = [2,3,1]\r\nso,length=3\r\n\u003c/pre\u003e","function_template":"function y = longest_sub_common(a,b)","test_suite":"%%\r\na=[1,1,1,1,1,2,3,1,4]\r\nb=[2,3,0,0,9,5,4,1]\r\nassert(isequal(longest_sub_common(a,b),3))\r\n\r\n%%\r\na=[1,1,1,1,1,2,3]\r\nb=[2,3,0,0,9,5,4,1]\r\nassert(isequal(longest_sub_common(a,b),2))\r\n\r\n%%\r\na=[1,1,1,1,1,2,3,1,4]\r\nb=zeros(1,500);\r\nassert(isequal(longest_sub_common(a,b),0))\r\n\r\n%%\r\na=[1,1,1,1,1,2,3,1,4]\r\nb=[zeros(1,50),ones(1,200),ones(1,20)*3]\r\nassert(isequal(longest_sub_common(a,b),6))\r\n\r\n%%\r\na='aaabbbcccxyz'\r\nb='abcyycbaabc'\r\nassert(isequal(longest_sub_common(a,b),5))\r\n\r\n%%\r\na=[10\t9\t8\t2\t4\t2\t1\t4\t7\t7\t6\t5\t3\t6\t8\t8\t6\t8\t7\t2\t6\t4\t1\t2\t2\t7\t5\t7\t3\t1\t6\t3\t10\t10\t4\t1\t7\t9\t10\t1\t5\t6\t7\t8\t7\t8\t4\t6\t2\t1\t10\t3\t6\t10\t2\t2\t4\t10\t4\t3\t2\t4\t4\t2\t5\t1\t7\t1\t6\t8\t3\t5\t6\t1\t5\t7\t3\t9\t10\t9\t6\t3\t8\t3\t10\t7\t7\t2\t1\t3\t9\t10\t7\t8\t3\t6\t9\t5\t10\t1\t4\t6\t1\t8\t6\t6\t9\t9\t8\t4\t5\t8\t2\t2\t3\t6\t10\t8\t4\t3\t9\t10\t7\t3\t1\t9\t6\t10\t1\t6\t3\t9\t2\t5\t4\t9\t7\t3\t4\t2\t7\t6\t2\t2\t5\t10\t6\t1\t1\t9\t5\t4\t8\t4\t6\t8\t9\t4\t7\t10\t1\t6\t5\t4\t3\t8\t10\t2\t8\t2\t10\t9\t5\t8\t5\t9\t4\t1\t6\t10\t2\t5\t8\t1\t10\t8\t6\t2\t5\t6\t10\t9\t10\t7\t5\t10\t5\t3\t4\t8\t6\t8\t10\t10\t6\t10\t2\t1\t4\t6\t6\t10\t6\t5\t6\t8\t1\t9\t2\t5\t3\t4\t7\t2\t3\t2\t2\t4\t9\t5\t5\t2\t10\t5\t9\t7\t4\t9\t8\t5\t9\t9\t5\t4\t6\t10\t8\t4\t8\t10\t6\t6\t4\t1\t2\t1\t5\t1\t10\t7\t1\t1\t3\t5\t2\t1\t8\t4\t8\t5\t5\t1\t1\t1\t6\t3\t9\t9\t10\t5\t3\t3\t6\t8\t4\t5\t7\t10\t2\t8\t6\t5\t9\t4\t2\t7\t7\t4\t9\t10\t10\t2\t3\t1\t7\t2\t5\t9\t6\t4\t3\t5\t10\t2\t5\t9\t1\t7\t10\t3\t2\t7\t10\t7\t9\t2\t8\t4\t5\t2\t9\t7\t8\t9\t1\t10\t5\t8\t8\t9\t2\t5\t7\t10\t9\t9\t6\t6\t9\t1\t9\t5\t1\t8\t2\t2\t7\t3\t4\t5\t5\t4\t7\t2\t2\t1\t4\t8\t3\t8\t7\t9\t9\t3\t4\t6\t4\t9\t9\t6\t3\t7\t3\t5\t4\t6\t10\t8\t10\t3\t6\t1\t8\t7\t9\t10\t10\t5\t1\t6\t3\t3\t4\t1\t8\t8\t6\t4\t9\t6\t10\t9\t4\t6\t4\t7\t8\t8\t2\t9\t1\t5\t8\t8\t4\t8\t9\t3\t2\t3\t4\t3\t10\t1\t6\t2\t9\t2\t6\t10\t4\t1\t3\t4\t4\t3\t10\t7\t10\t5\t10\t1\t7\t9\t3\t10\t8\t9\t6\t8\t4\t3\t4\t6\t9\t3\t5\t9\t7\t10\t3\t9\t7\t3\t5\t4\t6\t9\t2\t9\t9\t4\t5\t6\t8\t8\t8\t4\t5\t10\t6\t9\t3\t7\t6\t10\t1\t6\t6\t1]\r\nb=[14\t14\t7\t12\t3\t10\t4\t7\t13\t3\t5\t8\t6\t12\t15\t3\t4\t11\t6\t15\t15\t10\t13\t7\t10\t15\t9\t15\t11\t8\t10\t14\t3\t6\t15\t7\t10\t14\t15\t10\t2\t1\t10\t9\t15\t12\t10\t8\t4\t15\t9\t1\t11\t8\t1\t14\t5\t4\t2\t5\t4\t10\t1\t5\t5\t14\t7\t12\t10\t12\t2\t15\t13\t1\t7\t5\t10\t4\t9\t10\t9\t7\t1\t8\t7\t2\t7\t7\t9\t13\t11\t14\t1\t4\t7\t15\t12\t7\t6\t1\t12\t8\t3\t7\t3\t12\t6\t15\t1\t13\t10\t9\t10\t11\t2\t14\t1\t5\t3\t14\t2\t9\t10\t10\t13\t1\t13\t8\t11\t4\t9\t11\t15\t7\t2\t1\t10\t12\t11\t6\t15\t8\t15\t2\t4\t12\t14\t12\t5\t3\t13\t12\t5\t4\t5\t13\t13\t9\t9\t5\t11\t12\t7\t7\t7\t5\t11\t14\t14\t12\t4\t11\t2\t2\t3\t3\t9\t2\t13\t11\t14\t8\t10\t14\t9\t5\t15\t1\t5\t15\t6\t5\t2\t14\t3\t5\t14\t8\t10\t9\t11\t1\t8\t1\t13\t6\t13\t4\t9\t15\t1\t1\t1\t11\t9\t2\t12\t10\t2\t2\t3\t12\t2\t4\t4\t2\t13\t11\t12\t10\t8\t5\t10\t2\t3\t1\t15\t15\t2\t8\t10\t5\t12\t9\t7\t5\t12\t14\t11\t7]\r\nassert(isequal(longest_sub_common(a,b),121))\r\n\r\n%%\r\na='aaabaaabaaabaaa'\r\nb='abababababa'\r\nassert(isequal(longest_sub_common(a,b),9))","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":"2020-04-23T16:07:08.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-23T15:59:45.000Z","updated_at":"2026-02-10T12:11:50.000Z","published_at":"2020-04-23T16:07:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven two sequences, find the length of the longest common subsequence.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[a=[1,1,1,1,1,2,3,1,4]\\nb=[2,3,0,0,9,5,4,1]\\nlongest Common subsequence = [2,3,4]\\n = [2,3,1]\\nso,length=3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45475,"title":"Sub-sequence - 03","description":"Given three sequences, find the length of the longest common subsequence.\r\n\r\nIt is similar to the previous problem -- \r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/groups/1/problems/45474-sub-sequence-02\u003e","description_html":"\u003cp\u003eGiven three sequences, find the length of the longest common subsequence.\u003c/p\u003e\u003cp\u003eIt is similar to the previous problem --\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45474-sub-sequence-02\"\u003ehttps://www.mathworks.com/matlabcentral/cody/groups/1/problems/45474-sub-sequence-02\u003c/a\u003e\u003c/p\u003e","function_template":"function y = longest_sub_common_3d(a,b,c)","test_suite":"%%\r\na=[1,1,1,1,1,2,3,1,4]\r\nb=[2,3,0,0,9,5,4,1]\r\nc=[10,10,9,9,4,4,5]\r\nassert(isequal(longest_sub_common_3d(a,b,c),1))\r\n\r\n%%\r\na=[1,1,1,1,1,2,3,1,4]\r\nb=[2,3,0,1,0,9,5,4,1,10]\r\nc=[10,10,9,9,4,4,5,1,1,3]\r\nassert(isequal(longest_sub_common_3d(a,b,c),2))\r\n\r\n%%\r\na=' arthur'\r\nb='ser arthur '\r\nc='ser arthur dayne'\r\nassert(isequal(longest_sub_common_3d(a,b,c),7))\r\n\r\n%%\r\na=[105\t29\t60\t106\t30\t28\t103\t8\t34\t66\t23\t71\t89\t56\t73\t89\t26\t67\t13\t58\t94\t103\t56\t31\t73\t102\t57\t109\t22\t13\t34\t45\t47\t35\t78\t11\t45\t33\t35\t12\t66\t32\t18\t1\t32\t62\t97\t5\t101\t15\t93\t89\t102\t16\t57\t45\t20\t64\t68\t24\t58\t110\t55\t78\t46\t4\t33\t89\t39\t10\t57\t41\t83\t59\t90\t91\t22\t14\t92\t71\t2\t100\t58\t61\t68\t85\t95\t43\t10\t82\t37\t94\t42\t92\t20\t15\t98\t5\t77\t82\t49\t43\t109\t45\t49\t18\t37\t35\t100\t28\t35]\r\nb=[46\t79\t16\t97\t10\t52\t4\t84\t78\t24\t76\t62\t95\t63\t101\t47\t40\t55\t29\t104\t52\t29\t48\t78\t45\t21\t96\t65\t42\t25\t25\t58\t49\t83\t8\t95\t76\t16\t96\t23\t68\t61\t19\t1\t86\t85\t47\t7\t66\t20\t81\t60\t29\t102\t85\t99\t8\t21\t82\t78\t87\t56\t48\t68\t95\t75\t59\t34\t79\t43\t64\t99\t94\t100\t105\t91\t1\t1\t10\t29\t3\t48\t38\t61\t103\t34\t38\t96\t38\t16\t57\t96\t43\t78\t70\t50\t53\t106\t10\t32\t50\t66\t98\t53\t49\t83\t52\t96\t52\t56\t55]\r\n%c=[46\t79\t16\t97\t10\t52\t4\t84\t78\t24\t76\t62\t95\t63\t101\t47\t40\t55\t29\t104\t52\t29\t48\t78\t45\t21\t96\t65\t42\t25\t25\t58\t49\t83\t8\t95\t76\t16\t96\t23\t68\t61\t19\t1\t86\t85\t47\t7\t66\t20\t81\t60\t29\t102\t85\t99\t8\t21\t82\t78\t87\t56\t48\t68\t95\t75\t59\t34\t79\t43\t64\t99\t94\t100\t105\t91\t1\t1\t10\t29\t3\t48\t38\t61\t103\t34\t38\t96\t38\t16\t57\t96\t43\t78\t70\t50\t53\t106\t10\t32\t50\t66\t98\t53\t49\t83\t52\t96\t52\t56\t55]\r\nc=[26\t10\t8\t99\t26\t96\t80\t97\t105\t16\t44\t109\t72\t100\t54\t2\t70\t26\t59\t81\t68\t66\t49\t28\t48\t2\t68\t107\t11\t4\t99\t28\t1\t91\t16\t98\t11\t40\t66\t65\t75\t72\t49\t16\t84\t27\t73\t96\t10\t108\t4\t93\t93\t6\t61\t105\t36\t90\t67\t88\t89\t6\t32\t73\t55\t108\t84\t64\t34\t29\t99\t50\t91\t11\t96\t4\t100\t100\t59\t14\t20\t79\t93\t4\t85\t107\t39\t71\t39\t25\t88\t81\t31\t65\t47\t11\t3\t55\t31\t38\t32\t19\t45\t78\t23\t74\t50\t49\t20\t22\t69]\r\nassert(isequal(longest_sub_common_3d(a,b,c),6))\r\n\r\n%%\r\na=repelem('abc',1,50)\r\nb=repelem('acb',1,50)\r\nc=repelem('acdb',1,50)\r\nassert(isequal(longest_sub_common_3d(a,b,c),100))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2020-04-23T16:36:03.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-23T16:29:45.000Z","updated_at":"2026-03-18T19:22:50.000Z","published_at":"2020-04-23T16:36:03.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven three sequences, find the length of the longest common subsequence.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is similar to the previous problem --\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45474-sub-sequence-02\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45474-sub-sequence-02\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2523,"title":"longest common substring : Skipped character version","description":"Two strings are given. Find the longest common substring between them. The substring characters need not be adjacent. They, however, must appear in same order in both input strings.\r\n\r\nExample:\r\n\r\n str1 = 'abcdefghi';\r\n str2 = 'zazbzczd';\r\n \r\n output = 'abcd'","description_html":"\u003cp\u003eTwo strings are given. Find the longest common substring between them. The substring characters need not be adjacent. They, however, must appear in same order in both input strings.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003estr1 = 'abcdefghi';\r\nstr2 = 'zazbzczd';\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eoutput = 'abcd'\r\n\u003c/pre\u003e","function_template":"function y = skipped(str1,str2)\r\n y = x;\r\nend","test_suite":"%%\r\nstr1 = 'abcdefghi';\r\nstr2 = 'zazbzczd';\r\ny_correct = 'abcd';\r\nassert(isequal(skipped(str1,str2),y_correct))\r\n\r\n%%\r\nstr1 = 'abcdefghi';\r\nstr2 = 'zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz';\r\ny_correct = '';\r\nassert(isequal(skipped(str1,str2),y_correct))\r\n\r\n\r\n\r\n%%\r\nstr1 = 'catcatcat';\r\nstr2 = 'catcatcat';\r\ny_correct = str1;\r\nassert(isequal(skipped(str1,str2),y_correct))\r\n\r\n\r\n%%\r\nstr1 = 'an example of a string';\r\nstr2 = 'the example z a s t r i i i n ssss';\r\ny_correct = ' example a strin';\r\nassert(isequal(skipped(str1,str2),y_correct))\r\n\r\n\r\n%%\r\nstr1 = 'a string with many characters';\r\nstr2 = 'zzz zzz zzz zzz zzz';\r\ny_correct = ' ';\r\nassert(isequal(skipped(str1,str2),y_correct))\r\n\r\n\r\n%%\r\nstr1 = 'lets!not!use!spaces';\r\nstr2 = 'z!zzzzzzzzZZZzZzZ!zzzzz!zzzzzzzzz!!!!!!!!!zzz';\r\ny_correct = '!!!';\r\nassert(isequal(skipped(str1,str2),y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":4,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":53,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":32,"created_at":"2014-08-20T10:44:21.000Z","updated_at":"2026-04-08T08:28:14.000Z","published_at":"2014-08-20T10:44:21.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTwo strings are given. Find the longest common substring between them. The substring characters need not be adjacent. They, however, must appear in same order in both input strings.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[str1 = 'abcdefghi';\\nstr2 = 'zazbzczd';\\n\\noutput = 'abcd']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45416,"title":"Don't be Greedy!","description":"A list of assignments is given to the students along with the submission deadlines. Each of\r\n the assignment contains particular marks.\r\n\r\nIf the student can submit a particular assignment within its deadline, he'll get marks.\r\n\r\nBut he can submit only one assignment per day.\r\n\r\nFor instance,\r\n\r\n Assignment = [ a1, a2, a3, a4, a5, a6]\r\n Marks = [ 60,100, 20, 40, 20, 10]\r\n Deadline = [ 2, 1, 3, 2, 1, 3]\r\n\r\nNow, on the 1st day - he can submit one among all the assignments. But in the 2nd day, he can no longer submit a2 \u0026 a5.\r\n\r\nHe wants to achieve maximum marks by carefully submitting those assignments within the deadlines.\r\nCan u help him?\r\n\r\nThe answer should be - [a2,a1,a3]. Since by submitting in this sequence, he can get the maximum marks.","description_html":"\u003cp\u003eA list of assignments is given to the students along with the submission deadlines. Each of\r\n the assignment contains particular marks.\u003c/p\u003e\u003cp\u003eIf the student can submit a particular assignment within its deadline, he'll get marks.\u003c/p\u003e\u003cp\u003eBut he can submit only one assignment per day.\u003c/p\u003e\u003cp\u003eFor instance,\u003c/p\u003e\u003cpre\u003e Assignment = [ a1, a2, a3, a4, a5, a6]\r\n Marks = [ 60,100, 20, 40, 20, 10]\r\n Deadline = [ 2, 1, 3, 2, 1, 3]\u003c/pre\u003e\u003cp\u003eNow, on the 1st day - he can submit one among all the assignments. But in the 2nd day, he can no longer submit a2 \u0026 a5.\u003c/p\u003e\u003cp\u003eHe wants to achieve maximum marks by carefully submitting those assignments within the deadlines.\r\nCan u help him?\u003c/p\u003e\u003cp\u003eThe answer should be - [a2,a1,a3]. Since by submitting in this sequence, he can get the maximum marks.\u003c/p\u003e","function_template":"function yy=greedy_01(marks,deadline)","test_suite":"%%\r\nmarks = [ 60,100, 20, 40, 20, 10]\r\ndeadline = [ 2, 1, 3, 2, 1, 3]\r\nassert(isequal(greedy_01(marks,deadline),[2,1,3]))\r\n\r\n%%\r\nmarks = [10,10,50,40,30,100,20,10]\r\ndeadline = [1,2,3,3,5,5,1,3]\r\nassert(isequal(greedy_01(marks,deadline),[7,4,3,5,6]))\r\n\r\n%%\r\nmarks = [50,100,40,80,200,220,10]\r\ndeadline = [2,1,2,1,1,1,4]\r\nassert(isequal(greedy_01(marks,deadline),[6,1,7]))\r\n\r\n%%\r\nmarks = [50,100,40,80,200,220,10,150]\r\ndeadline = [2,1,3,6,2,2,6,7]\r\nassert(isequal(greedy_01(marks,deadline),[5 6 3 7 4 8]))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":15,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":19,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-02T00:18:30.000Z","updated_at":"2026-03-10T13:00:11.000Z","published_at":"2020-04-02T00:18:30.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA list of assignments is given to the students along with the submission deadlines. Each of the assignment contains particular marks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the student can submit a particular assignment within its deadline, he'll get marks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBut he can submit only one assignment per day.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor instance,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Assignment = [ a1, a2, a3, a4, a5, a6]\\n Marks = [ 60,100, 20, 40, 20, 10]\\n Deadline = [ 2, 1, 3, 2, 1, 3]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow, on the 1st day - he can submit one among all the assignments. But in the 2nd day, he can no longer submit a2 \u0026amp; a5.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHe wants to achieve maximum marks by carefully submitting those assignments within the deadlines. Can u help him?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe answer should be - [a2,a1,a3]. Since by submitting in this sequence, he can get the maximum marks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45451,"title":"Don't be Too Greedy!!","description":"Refer to the prev problem \u003chttps://www.mathworks.com/matlabcentral/cody/problems/45450-don-t-be-too-greedy\u003e\r\n\r\nFor this time, calculate the maximum number of marks he can achieve after finishing all his assignments.","description_html":"\u003cp\u003eRefer to the prev problem \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45450-don-t-be-too-greedy\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45450-don-t-be-too-greedy\u003c/a\u003e\u003c/p\u003e\u003cp\u003eFor this time, calculate the maximum number of marks he can achieve after finishing all his assignments.\u003c/p\u003e","function_template":"function y = greedy_3(p,t,mark)","test_suite":"%%\r\np=[ 10, 4, 1, 2, 5, 2];\r\nt=[ 2, 4, 3, 8, 1, 10];\r\nassert(isequal(greedy_3(p,t,100),515))\r\n\r\n%%\r\n p=[4,2,1, 5, 3, 2, 6];\r\n t=[1,8,2, 3, 1, 4, 2];\r\nassert(isequal(greedy_3(p,t,10),-16))\r\n\r\n%%\r\np=[9,10,2,10,7,1,3,6,10,10,2,10];\r\nt=[48 25 41 8 22 46 40 48 33 2 43 47];\r\nassert(isequal(greedy_3(p,t,200),-4008))\r\n\r\n%%\r\np=[1,3,1,2,4];\r\nt=[1,4,10,2,5];\r\nassert(isequal(greedy_3(p,t,200),950))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-13T12:04:50.000Z","updated_at":"2026-02-24T09:23:57.000Z","published_at":"2020-04-13T12:04:50.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRefer to the prev problem\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45450-don-t-be-too-greedy\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45450-don-t-be-too-greedy\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this time, calculate the maximum number of marks he can achieve after finishing all his assignments.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42888,"title":"Frobenius McNugget Factorization","description":"\r\nMr. Frobenius McNugget is a peculiar man.\r\nAs you might expect, he likes to eat Chicken McNuggets. But his love of number theory influences his appetite in strange ways. On any given day he wakes up with a desire for a specific number of McNuggets. If that number is 19, then no other number of McNuggets will do. But the McFastFood restaurant down the street serves McNuggets only in quantities of 6, 9, or 20 to a box. So he can be satisfied on a 21 nugget day, but on a 19 nugget day he must go hungry. To make matters more interesting, the restaurant often changes the quantities in their boxed McNuggets.\r\nGiven the box counts nuggets, what is the highest number frob for which Frobenius must go hungry? nuggets is a vector of positive integers with two or more elements.\r\nExamples\r\n nuggets = [2 5]\r\n frob = 3\r\n\r\n nuggets = [6 9 20]\r\n frob = 43","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 787.419px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408.495px 393.704px; transform-origin: 408.495px 393.71px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 450.197px; font-family: Helvetica, Arial, sans-serif; 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0069px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385.498px 10.4977px; text-align: left; transform-origin: 385.498px 10.5035px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMr. Frobenius McNugget is a peculiar man.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105.035px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385.498px 52.5116px; text-align: left; transform-origin: 385.498px 52.5174px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAs you might expect, he likes to eat Chicken McNuggets. But his love of number theory influences his appetite in strange ways. On any given day he wakes up with a desire for a specific number of McNuggets. If that number is 19, then no other number of McNuggets will do. But the McFastFood restaurant down the street serves McNuggets only in quantities of 6, 9, or 20 to a box. So he can be satisfied on a 21 nugget day, but on a 19 nugget day he must go hungry. To make matters more interesting, the restaurant often changes the quantities in their boxed McNuggets.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.0139px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385.498px 21.0069px; text-align: left; transform-origin: 385.498px 21.0069px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven the box counts\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003enuggets\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, what is the highest number\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003efrob\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e for which Frobenius must go hungry?\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003enuggets\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is a vector of positive integers with two or more elements.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0069px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385.498px 10.4977px; text-align: left; transform-origin: 385.498px 10.5035px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eExamples\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.199px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 405.498px 51.0995px; transform-origin: 405.498px 51.0995px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4398px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.740741px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.740741px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.740741px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.740741px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405.498px 10.2199px; text-wrap-mode: nowrap; transform-origin: 405.498px 10.2199px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e nuggets = [2 5]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4398px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.740741px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.740741px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.740741px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.740741px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405.498px 10.2199px; text-wrap-mode: nowrap; transform-origin: 405.498px 10.2199px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e frob = 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4398px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.740741px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.740741px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.740741px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.740741px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405.498px 10.2199px; text-wrap-mode: nowrap; transform-origin: 405.498px 10.2199px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4398px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.740741px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.740741px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.740741px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.740741px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405.498px 10.2199px; text-wrap-mode: nowrap; transform-origin: 405.498px 10.2199px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e nuggets = [6 9 20]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4398px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.740741px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.740741px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.740741px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.740741px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405.498px 10.2199px; text-wrap-mode: nowrap; transform-origin: 405.498px 10.2199px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e frob = 43\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function frob = hungerGame(nuggets)\r\n frob = 0;\r\nend","test_suite":"%%\r\nnuggets = [2 5];\r\nfrob_correct = 3;\r\nassert(isequal(hungerGame(nuggets),frob_correct))\r\n\r\n%%\r\nnuggets = [6 9 20];\r\nfrob_correct = 43;\r\nassert(isequal(hungerGame(nuggets),frob_correct))\r\n\r\n\r\n%%\r\nnuggets = [17 19 32];\r\nfrob_correct = 175;\r\nassert(isequal(hungerGame(nuggets),frob_correct))\r\n\r\n\r\n%%\r\nnuggets = [13 14 15];\r\nfrob_correct = 77;\r\nassert(isequal(hungerGame(nuggets),frob_correct))\r\n","published":true,"deleted":false,"likes_count":16,"comments_count":4,"created_by":7,"edited_by":7,"edited_at":"2025-03-31T15:09:16.000Z","deleted_by":null,"deleted_at":null,"solvers_count":42,"test_suite_updated_at":"2016-06-07T17:02:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2016-06-06T19:10:29.000Z","updated_at":"2026-02-09T14:49:48.000Z","published_at":"2016-06-06T19:49:01.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMr. Frobenius McNugget is a peculiar man.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs you might expect, he likes to eat Chicken McNuggets. But his love of number theory influences his appetite in strange ways. On any given day he wakes up with a desire for a specific number of McNuggets. If that number is 19, then no other number of McNuggets will do. But the McFastFood restaurant down the street serves McNuggets only in quantities of 6, 9, or 20 to a box. So he can be satisfied on a 21 nugget day, but on a 19 nugget day he must go hungry. To make matters more interesting, the restaurant often changes the quantities in their boxed McNuggets.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the box counts\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enuggets\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, what is the highest number\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efrob\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for which Frobenius must go hungry?\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enuggets\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is a vector of positive integers with two or more elements.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ nuggets = [2 5]\\n frob = 3\\n\\n nuggets = [6 9 20]\\n frob = 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2309,"title":"Calculate the Damerau-Levenshtein distance between two strings.","description":"\u003chttp://www.mathworks.co.uk/matlabcentral/cody/problems/2303-compute-hamming-distances-between-each-pair-of-rows-from-two-input-matrices Problem 2303\u003e reminded me a few older ones dealing with metrics between strings, problems \u003chttp://www.mathworks.co.uk/matlabcentral/cody/problems/93-calculate-the-levenshtein-distance-between-two-strings 93\u003e,\r\n\u003chttp://www.mathworks.co.uk/matlabcentral/cody/problems/846-calculate-the-hamming-distance-between-two-strings 846\u003e or\r\n\u003chttp://www.mathworks.co.uk/matlabcentral/cody/problems/848-calculate-a-modified-levenshtein-distance-between-two-strings 848\u003e\r\n about Hamming and Levenshtein distances.\r\n\r\n\u003chttp://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance Damerau-Levenshtein distance\u003e is an extension to Levenshtein distance. It is also defined as minimum number of simple edit operations on string to change it into another, but the list of allowed operations is extended.\r\n\r\nAs it is written on Wikipedia there are 4 allowed edits: deletion, insertion and substitution of an single character and an transposition of two adjacent characters.\r\n\r\nExample. Such defined distance between words _gifts_ and _profit_ is 5:\r\n\r\n gifts =\u003e pgifts (insertion of 'p')\r\n pgifts =\u003e prgifts (insertion of 'r')\r\n prgifts =\u003e proifts (substitution of 'g' to 'o')\r\n proifts =\u003e profits (transposition of 'if' to 'fi')\r\n profits =\u003e profit (deletion of 's')\r\n \r\n\r\n","description_html":"\u003cp\u003e\u003ca href = \"http://www.mathworks.co.uk/matlabcentral/cody/problems/2303-compute-hamming-distances-between-each-pair-of-rows-from-two-input-matrices\"\u003eProblem 2303\u003c/a\u003e reminded me a few older ones dealing with metrics between strings, problems \u003ca href = \"http://www.mathworks.co.uk/matlabcentral/cody/problems/93-calculate-the-levenshtein-distance-between-two-strings\"\u003e93\u003c/a\u003e, \u003ca href = \"http://www.mathworks.co.uk/matlabcentral/cody/problems/846-calculate-the-hamming-distance-between-two-strings\"\u003e846\u003c/a\u003e or \u003ca href = \"http://www.mathworks.co.uk/matlabcentral/cody/problems/848-calculate-a-modified-levenshtein-distance-between-two-strings\"\u003e848\u003c/a\u003e\r\n about Hamming and Levenshtein distances.\u003c/p\u003e\u003cp\u003e\u003ca href = \"http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance\"\u003eDamerau-Levenshtein distance\u003c/a\u003e is an extension to Levenshtein distance. It is also defined as minimum number of simple edit operations on string to change it into another, but the list of allowed operations is extended.\u003c/p\u003e\u003cp\u003eAs it is written on Wikipedia there are 4 allowed edits: deletion, insertion and substitution of an single character and an transposition of two adjacent characters.\u003c/p\u003e\u003cp\u003eExample. Such defined distance between words \u003ci\u003egifts\u003c/i\u003e and \u003ci\u003eprofit\u003c/i\u003e is 5:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003egifts =\u0026gt; pgifts (insertion of 'p')\r\npgifts =\u0026gt; prgifts (insertion of 'r')\r\nprgifts =\u0026gt; proifts (substitution of 'g' to 'o')\r\nproifts =\u0026gt; profits (transposition of 'if' to 'fi')\r\nprofits =\u0026gt; profit (deletion of 's')\r\n\u003c/pre\u003e","function_template":"function distance = Damerau_Levenshtein(str1,str2)\r\n distance = 0;\r\nend","test_suite":"%%\r\nassert(isequal(Damerau_Levenshtein('mom','dad'),3));\r\n% 3 substitutions\r\n%%\r\nassert(isequal(Damerau_Levenshtein('dogs','dog'),1));\r\n% 1 deletion\r\n%%\r\nassert(isequal(Damerau_Levenshtein('true','true'),0));\r\n% identity\r\n%%\r\nassert(isequal(Damerau_Levenshtein('true','false'),4));\r\n% 1 insertion, 3 substitutions\r\n%%\r\nassert(isequal(Damerau_Levenshtein('abc','ca'),2));\r\n% 1 deletion, 1 transposition\r\n%%\r\nassert(isequal(Damerau_Levenshtein('tee','tree'),1));\r\n%%\r\nassert(isequal(Damerau_Levenshtein('email','mails'),2));\r\n%%\r\nassert(isequal(Damerau_Levenshtein('profit','gifts'),5));\r\n%%\r\n% symmetry\r\nrnd=@()char(randi([97 122],1,randi([4 10])));\r\nfor k=1:10\r\n str1=rnd();\r\n str2=rnd();\r\n a=Damerau_Levenshtein(str1,str2);\r\n b=Damerau_Levenshtein(str2,str1);\r\n assert(isequal(a,b))\r\nend\r\n%%\r\n% trinagle inequality\r\nrnd=@()char(randi([97 122],1,randi([4 10])));\r\nfor k=1:50\r\n str1=rnd();\r\n str2=rnd();\r\n str3=rnd();\r\n a=Damerau_Levenshtein(str2,str3);\r\n b=Damerau_Levenshtein(str3,str1);\r\n c=Damerau_Levenshtein(str1,str2);\r\n assert(a+b\u003e=c)\r\nend","published":true,"deleted":false,"likes_count":1,"comments_count":8,"created_by":14358,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":46,"test_suite_updated_at":"2014-05-07T12:40:15.000Z","rescore_all_solutions":false,"group_id":32,"created_at":"2014-05-07T12:34:17.000Z","updated_at":"2026-04-08T09:10:54.000Z","published_at":"2014-05-07T12:40:15.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.co.uk/matlabcentral/cody/problems/2303-compute-hamming-distances-between-each-pair-of-rows-from-two-input-matrices\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 2303\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e reminded me a few older ones dealing with metrics between strings, problems\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.co.uk/matlabcentral/cody/problems/93-calculate-the-levenshtein-distance-between-two-strings\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e93\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.co.uk/matlabcentral/cody/problems/846-calculate-the-hamming-distance-between-two-strings\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e846\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.co.uk/matlabcentral/cody/problems/848-calculate-a-modified-levenshtein-distance-between-two-strings\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e848\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e about Hamming and Levenshtein distances.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eDamerau-Levenshtein distance\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is an extension to Levenshtein distance. It is also defined as minimum number of simple edit operations on string to change it into another, but the list of allowed operations is extended.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs it is written on Wikipedia there are 4 allowed edits: deletion, insertion and substitution of an single character and an transposition of two adjacent characters.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample. Such defined distance between words\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003egifts\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eprofit\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is 5:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[gifts =\u003e pgifts (insertion of 'p')\\npgifts =\u003e prgifts (insertion of 'r')\\nprgifts =\u003e proifts (substitution of 'g' to 'o')\\nproifts =\u003e profits (transposition of 'if' to 'fi')\\nprofits =\u003e profit (deletion of 's')]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1394,"title":"Prime Ladders","description":"A \u003chttp://en.wikipedia.org/wiki/Word_ladder word ladder\u003e transforms one word to another by means of single-letter mutations. So COLD can become WARM like so (there are often multiple solutions):\r\n\r\n COLD\r\n CORD\r\n CARD\r\n WARD\r\n WARM\r\n\r\nA number ladder does much the same thing, changing one digit at a time. A *prime ladder* is a number ladder with the additional constraint that each element is a prime number. Here is a prime ladder that connects 757 and 139\r\n\r\n 757 \r\n 157\r\n 137\r\n 139\r\n\r\nGiven two numbers p1 and p2, construct a prime ladder column vector in which p1 is the first element, p2 is the last element, and each successive row differs by exactly one digit from the preceding element. \r\n\r\nTo restate the above example, consider\r\n\r\n p1 = 757\r\n p2 = 139\r\n\r\nfor which an acceptable answer is\r\n\r\n ladder = [757; 157; 137; 139]\r\n\r\nYou can assume that p1 and p2 contain the same number of digits. I am not looking for a unique answer. I will only check that the conditions of a prime ladder are met.\r\n\r\n","description_html":"\u003cp\u003eA \u003ca href = \"http://en.wikipedia.org/wiki/Word_ladder\"\u003eword ladder\u003c/a\u003e transforms one word to another by means of single-letter mutations. So COLD can become WARM like so (there are often multiple solutions):\u003c/p\u003e\u003cpre\u003e COLD\r\n CORD\r\n CARD\r\n WARD\r\n WARM\u003c/pre\u003e\u003cp\u003eA number ladder does much the same thing, changing one digit at a time. A \u003cb\u003eprime ladder\u003c/b\u003e is a number ladder with the additional constraint that each element is a prime number. Here is a prime ladder that connects 757 and 139\u003c/p\u003e\u003cpre\u003e 757 \r\n 157\r\n 137\r\n 139\u003c/pre\u003e\u003cp\u003eGiven two numbers p1 and p2, construct a prime ladder column vector in which p1 is the first element, p2 is the last element, and each successive row differs by exactly one digit from the preceding element.\u003c/p\u003e\u003cp\u003eTo restate the above example, consider\u003c/p\u003e\u003cpre\u003e p1 = 757\r\n p2 = 139\u003c/pre\u003e\u003cp\u003efor which an acceptable answer is\u003c/p\u003e\u003cpre\u003e ladder = [757; 157; 137; 139]\u003c/pre\u003e\u003cp\u003eYou can assume that p1 and p2 contain the same number of digits. I am not looking for a unique answer. I will only check that the conditions of a prime ladder are met.\u003c/p\u003e","function_template":"function ladder = prime_ladder(p1,p2)\r\n ladder = 0;\r\nend","test_suite":"%%\r\n\r\np1 = 13;\r\np2 = 29;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 389;\r\np2 = 269;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 761;\r\np2 = 397;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 983;\r\np2 = 239;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 271;\r\np2 = 439;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 877;\r\np2 = 733;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 2267;\r\np2 = 1153;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n","published":true,"deleted":false,"likes_count":10,"comments_count":3,"created_by":7,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":55,"test_suite_updated_at":"2013-03-27T21:24:26.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-03-26T22:51:16.000Z","updated_at":"2026-01-03T14:28:57.000Z","published_at":"2013-03-27T15:28:59.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Word_ladder\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eword ladder\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e transforms one word to another by means of single-letter mutations. So COLD can become WARM like so (there are often multiple solutions):\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ COLD\\n CORD\\n CARD\\n WARD\\n WARM]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA number ladder does much the same thing, changing one digit at a time. A\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eprime ladder\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is a number ladder with the additional constraint that each element is a prime number. Here is a prime ladder that connects 757 and 139\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 757 \\n 157\\n 137\\n 139]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven two numbers p1 and p2, construct a prime ladder column vector in which p1 is the first element, p2 is the last element, and each successive row differs by exactly one digit from the preceding element.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo restate the above example, consider\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ p1 = 757\\n p2 = 139]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003efor which an acceptable answer is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ ladder = [757; 157; 137; 139]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can assume that p1 and p2 contain the same number of digits. I am not looking for a unique answer. I will only check that the conditions of a prime ladder are met.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1885,"title":"Minimum Sum thru a Lower Triangle","description":"This Challenge is to find the minimum cumulative sum that traverses from row-1 thru row-N via vertical/diagonal adjacent elements of adjacent rows.\r\n\r\nThe input is a series of values of length n*(n+1)/2.\r\n\r\n*Input:* S [Series that can be converted into a lower triangle]\r\n\r\n*Output:* MinSum [Minimum cost path from top to bottom]\r\n\r\n*Example:*\r\n\r\n[5 7 6 3 2 5] becomes\r\n\r\n 5 0 0\r\n 7 6 0\r\n 3 2 5\r\n\r\nCreates a MinSum of 13 [5+6+2]. The 5 can only see 6, 3 sees 7 and 6, while 2 sees 7 6 0.\r\n ","description_html":"\u003cp\u003eThis Challenge is to find the minimum cumulative sum that traverses from row-1 thru row-N via vertical/diagonal adjacent elements of adjacent rows.\u003c/p\u003e\u003cp\u003eThe input is a series of values of length n*(n+1)/2.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e S [Series that can be converted into a lower triangle]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e MinSum [Minimum cost path from top to bottom]\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cp\u003e[5 7 6 3 2 5] becomes\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e5 0 0\r\n7 6 0\r\n3 2 5\r\n\u003c/pre\u003e\u003cp\u003eCreates a MinSum of 13 [5+6+2]. The 5 can only see 6, 3 sees 7 and 6, while 2 sees 7 6 0.\u003c/p\u003e","function_template":"function MinSum=Find_MinSum(s)\r\n MinSum=0;\r\nend","test_suite":"%%\r\ns=[5 7 6 3 2 5];\r\nMinSum=Find_MinSum(s);\r\nexp=13;\r\nassert(exp==MinSum)\r\n%%\r\ns=[7 9 8 3 2 6 7 1 5 5 9 4 8 2 4 6 3 2 9 7 5 7 2 4 8 5 1 9 4 2 9 3 8 5 2 8 8 2 4 8 5 9 2 7 3];\r\nMinSum=Find_MinSum(s);\r\nexp=30;\r\nassert(exp==MinSum)\r\n%%\r\ns=[21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1];\r\nMinSum=Find_MinSum(s);\r\nexp=76;\r\nassert(exp==MinSum)\r\n%%\r\ns=1:28;\r\nMinSum=Find_MinSum(s);\r\nexp=63;\r\nassert(exp==MinSum)\r\n%%\r\ns=[82 91 13 92 64 10 28 55 96 97 16 98 96 49 81 15 43 92 80 96 66 4 85 94 68 76 75 40 66 18 71 4 28 5 10 83 ];\r\nMinSum=Find_MinSum(s);\r\nexp=213;\r\nassert(exp==MinSum)\r\n%%\r\ns=[348 159 476 18 220 191 383 398 94 245 223 324 355 378 139 340 328 82 60 250 480 171 293 112 376 128 253 350 446 480 274 70 75 129 421 128 ];\r\nMinSum=Find_MinSum(s);\r\nexp=1409;\r\nassert(exp==MinSum)\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-09-21T02:45:50.000Z","updated_at":"2026-04-08T08:33:22.000Z","published_at":"2013-09-21T03:00:18.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to find the minimum cumulative sum that traverses from row-1 thru row-N via vertical/diagonal adjacent elements of adjacent rows.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input is a series of values of length n*(n+1)/2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e S [Series that can be converted into a lower triangle]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e MinSum [Minimum cost path from top to bottom]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[5 7 6 3 2 5] becomes\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[5 0 0\\n7 6 0\\n3 2 5]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreates a MinSum of 13 [5+6+2]. The 5 can only see 6, 3 sees 7 and 6, while 2 sees 7 6 0.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":93,"title":"Calculate the Levenshtein distance between two strings","description":"This problem description is lifted from http://en.wikipedia.org/wiki/Levenshtein_distance.\r\nThe Levenshtein distance between two strings is defined as the minimum number of edits needed to transform one string into the other, with the allowable edit operations being insertion, deletion, or substitution of a single character.\r\nFor example, the Levenshtein distance between \"kitten\" and \"sitting\" is 3, since the following three edits change one into the other, and there is no way to do it with fewer than three edits:\r\n kitten =\u003e sitten (substitution of 's' for 'k')\r\n sitten =\u003e sittin (substitution of 'e' for 'i')\r\n sittin =\u003e sitting (insert 'g' at the end).\r\nSo when\r\n s1 = 'kitten'\r\nand\r\n s2 = 'sitting'\r\nthen the distance d is equal to 3.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 348.167px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 174.083px; transform-origin: 407px 174.083px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 117.5px 8px; transform-origin: 117.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis problem description is lifted from\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Levenshtein_distance\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ehttp://en.wikipedia.org/wiki/Levenshtein_distance\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381.5px 8px; transform-origin: 381.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Levenshtein distance between two strings is defined as the minimum number of edits needed to transform one string into the other, with the allowable edit operations being insertion, deletion, or substitution of a single character.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379px 8px; transform-origin: 379px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, the Levenshtein distance between \"kitten\" and \"sitting\" is 3, since the following three edits change one into the other, and there is no way to do it with fewer than three edits:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 61.3px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 30.65px; transform-origin: 404px 30.65px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 192px 8.5px; tab-size: 4; transform-origin: 192px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 144px 8.5px; transform-origin: 144px 8.5px; \"\u003e kitten =\u0026gt; sitten (substitution of \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003e's' \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); perspective-origin: 16px 8.5px; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); transform-origin: 16px 8.5px; \"\u003efor \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 12px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 12px 8.5px; \"\u003e'k'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 192px 8.5px; tab-size: 4; transform-origin: 192px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 144px 8.5px; transform-origin: 144px 8.5px; \"\u003e sitten =\u0026gt; sittin (substitution of \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003e'e' \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); perspective-origin: 16px 8.5px; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); transform-origin: 16px 8.5px; \"\u003efor \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 12px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 12px 8.5px; \"\u003e'i'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 172px 8.5px; tab-size: 4; transform-origin: 172px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 108px 8.5px; transform-origin: 108px 8.5px; \"\u003e sittin =\u0026gt; sitting (insert \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003e'g' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 48px 8.5px; transform-origin: 48px 8.5px; \"\u003eat the end).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27px 8px; transform-origin: 27px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSo when\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 56px 8.5px; tab-size: 4; transform-origin: 56px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 24px 8.5px; transform-origin: 24px 8.5px; \"\u003e s1 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 32px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 32px 8.5px; \"\u003e'kitten'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eand\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 60px 8.5px; tab-size: 4; transform-origin: 60px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 24px 8.5px; transform-origin: 24px 8.5px; \"\u003e s2 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 36px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 36px 8.5px; \"\u003e'sitting'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 103px 8px; transform-origin: 103px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ethen the distance d is equal to 3.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function d = levenshtein(s1,s2)\r\n d = 0;\r\nend","test_suite":"%%\r\nfiletext = fileread('levenshtein.m');\r\nassert(isempty(strfind(filetext, 'switch')))\r\nassert(isempty(strfind(filetext, 'elseif')))\r\n\r\n\r\n%%\r\n\r\ns1 = 'kitten';\r\ns2 = 'sitting';\r\nd_correct = 3;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%% \r\n\r\ns1 = 'Saturday';\r\ns2 = 'Sunday';\r\nd_correct = 3;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%% \r\n\r\ns1 = 'MATLAB rocks!';\r\ns2 = 'MathWorks';\r\nd_correct = 9;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%% \r\n\r\ns1 = 'Four score and seven years ago';\r\ns2 = 'Eighty seven years before today';\r\nd_correct = 25;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%% \r\n\r\ns1 = 'Row row row your boat';\r\ns2 = 'Gently down the stream';\r\nd_correct = 18;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%%\r\n\r\ns1 = 'ninety-nine bottles of beer on the wall';\r\ns2 = 'eighty-six bottles of beer on the wall';\r\nd_correct = 6;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%%\r\n\r\ns1 = 'these are the times that try men''s souls';\r\ns2 = 'soulwise, these are trying times';\r\nd_correct = 27;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%%\r\n\r\ns1 = 'abc';\r\ns2 = 'abc';\r\nd_correct = 0;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%%\r\n\r\ns1 = 'god';\r\ns2 = 'dog';\r\nd_correct = 2;\r\nassert(isequal(levenshtein(s1,s2),d_correct))","published":true,"deleted":false,"likes_count":25,"comments_count":3,"created_by":1,"edited_by":223089,"edited_at":"2022-11-28T06:34:46.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1507,"test_suite_updated_at":"2022-11-28T06:34:46.000Z","rescore_all_solutions":false,"group_id":2,"created_at":"2012-01-18T01:00:30.000Z","updated_at":"2026-03-02T01:10:11.000Z","published_at":"2012-01-18T01:00:30.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem description is lifted from\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Levenshtein_distance\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://en.wikipedia.org/wiki/Levenshtein_distance\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Levenshtein distance between two strings is defined as the minimum number of edits needed to transform one string into the other, with the allowable edit operations being insertion, deletion, or substitution of a single character.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, the Levenshtein distance between \\\"kitten\\\" and \\\"sitting\\\" is 3, since the following three edits change one into the other, and there is no way to do it with fewer than three edits:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ kitten =\u003e sitten (substitution of 's' for 'k')\\n sitten =\u003e sittin (substitution of 'e' for 'i')\\n sittin =\u003e sitting (insert 'g' at the end).]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo when\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ s1 = 'kitten']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eand\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ s2 = 'sitting']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethen the distance d is equal to 3.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"}],"problem_search":{"errors":[],"problems":[{"id":45427,"title":"King's Cage","description":"Given the position of the king on the chessboard, determine the minimum number of steps it'll require to reach the destination.\r\n\r\n\u003chttps://en.wikipedia.org/wiki/Chess#Movement\u003e\r\n\r\nFor simplicity, numerical notation is used to represent the positions.","description_html":"\u003cp\u003eGiven the position of the king on the chessboard, determine the minimum number of steps it'll require to reach the destination.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://en.wikipedia.org/wiki/Chess#Movement\"\u003ehttps://en.wikipedia.org/wiki/Chess#Movement\u003c/a\u003e\u003c/p\u003e\u003cp\u003eFor simplicity, numerical notation is used to represent the positions.\u003c/p\u003e","function_template":"function i = king(x,y)","test_suite":"%%\r\nx=[1,1];\r\ny=[5,5];\r\nassert(isequal( king(x,y),4))\r\n\r\n%%\r\nx=[6,6];\r\ny=[2,3];\r\nassert(isequal( king(x,y),4))\r\n\r\n%%\r\nx=[5,1];\r\ny=[5,5];\r\nassert(isequal( king(x,y),4))\r\n\r\n%%\r\nx=[2,3];\r\ny=[8,8];\r\nassert(isequal( king(x,y),6))\r\n\r\n%%\r\nx=[2,8];\r\ny=[7,1];\r\nassert(isequal( king(x,y),7))\r\n\r\n%%\r\nx=[1,4];\r\ny=[8,3];\r\nassert(isequal( king(x,y),7))\r\n\r\n\r\n%%\r\nx=[5,8];\r\ny=[5,8];\r\nassert(isequal( king(x,y),0))\r\n\r\n\r\n%%\r\nx=[1,4];\r\ny=[3,4];\r\nassert(isequal( king(x,y),2))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":58,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-07T06:55:52.000Z","updated_at":"2026-04-12T10:12:31.000Z","published_at":"2020-04-07T06:55:52.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the position of the king on the chessboard, determine the minimum number of steps it'll require to reach the destination.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Chess#Movement\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://en.wikipedia.org/wiki/Chess#Movement\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor simplicity, numerical notation is used to represent the positions.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":590,"title":"Greed is good - Simple partition P[n].","description":"Find a simple partition P[n]. E.g. P[10] = 4 + 3 + 2 + 1.\r\nThere are many solutions, compute just one set.\r\nDon't repeat numbers.\r\nBe Greedy ;-)\r\nTo check against trivial solutions, E.g. [x-k, k] etc; but I'll provide you with one to start.\r\nShow me how you write the whole solution.\r\nBonus points if you solve the general problem of producing all unique partitions of [n].","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 174.167px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 87.0833px; transform-origin: 407px 87.0833px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 169px 8px; transform-origin: 169px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFind a simple partition P[n]. E.g. P[10] = 4 + 3 + 2 + 1.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 102.167px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 51.0833px; transform-origin: 391px 51.0833px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 152px 8px; transform-origin: 152px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThere are many solutions, compute just one set.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 70.5px 8px; transform-origin: 70.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eDon't repeat numbers.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 43px 8px; transform-origin: 43px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBe Greedy ;-)\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 267px 8px; transform-origin: 267px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eTo check against trivial solutions, E.g. [x-k, k] etc; but I'll provide you with one to start.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 133.5px 8px; transform-origin: 133.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eShow me how you write the whole solution.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 269px 8px; transform-origin: 269px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBonus points if you solve the general problem of producing all unique partitions of [n].\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = simplepartition(x)\r\n y = [x-1,1]; %trivial\r\nend\r\n","test_suite":"%%\r\nx = 10000;\r\nz = simplepartition(x); zL = length(z);\r\nassert(isequal(sum(unique(z)),x) \u0026\u0026 zL \u003e= ceil(log2(x)/2) \u0026 all(unique(z)\u003e0))\r\n\r\n%%\r\nx = 40190;\r\nz = simplepartition(x); zL = length(z);\r\nassert(isequal(sum(unique(z)),x) \u0026\u0026 zL \u003e= ceil(log2(x)/2) \u0026 all(unique(z)\u003e0))\r\n\r\n%%\r\nx = 149;\r\nz = simplepartition(x); zL = length(z);\r\nassert(isequal(sum(unique(z)),x) \u0026\u0026 zL \u003e= ceil(log2(x)/2) \u0026 all(unique(z)\u003e0))\r\n\r\n%%\r\nx = 20;\r\nz = simplepartition(x); zL = length(z);\r\nassert(isequal(sum(unique(z)),x) \u0026\u0026 zL \u003e= ceil(log2(x)/2) \u0026 all(unique(z)\u003e0))","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":3378,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":33,"test_suite_updated_at":"2022-01-05T17:27:44.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2012-04-17T03:13:18.000Z","updated_at":"2026-01-06T08:27:01.000Z","published_at":"2012-04-17T03:14:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind a simple partition P[n]. E.g. P[10] = 4 + 3 + 2 + 1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are many solutions, compute just one set.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eDon't repeat numbers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBe Greedy ;-)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo check against trivial solutions, E.g. [x-k, k] etc; but I'll provide you with one to start.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eShow me how you write the whole solution.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBonus points if you solve the general problem of producing all unique partitions of [n].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45342,"title":"Sieve of Eratosthenes","description":"Find the nth lucky prime number.\r\n\r\n\u003chttps://planetmath.org/luckyprime\u003e\r\n\r\ncan u find a way for large n?","description_html":"\u003cp\u003eFind the nth lucky prime number.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://planetmath.org/luckyprime\"\u003ehttps://planetmath.org/luckyprime\u003c/a\u003e\u003c/p\u003e\u003cp\u003ecan u find a way for large n?\u003c/p\u003e","function_template":"function y = lucky_prime(n)\r\n y = x;\r\nend","test_suite":"%%\r\nassert(isequal(lucky_prime(4),31))\r\n%%\r\nassert(isequal(lucky_prime(10),127))\r\n%%\r\nassert(isequal(lucky_prime(20),349))\r\n%%\r\nassert(isequal(lucky_prime(27),541))\r\n%%\r\nassert(isequal(lucky_prime(39),823))\r\n%%\r\nassert(isequal(lucky_prime(50),1123))\r\n%%\r\nassert(isequal(lucky_prime(60),1579))\r\n%%\r\nassert(isequal(lucky_prime(70),1987))\r\n%%\r\nassert(isequal(lucky_prime(90),2971))\r\n%%\r\nassert(isequal(lucky_prime(80),2473))\r\n%%\r\nassert(isequal(lucky_prime(200),9403))\r\n%%\r\nassert(isequal(lucky_prime(260),12799))\r\n%%\r\nassert(isequal(lucky_prime(440),25237))\r\n%%\r\nassert(isequal(lucky_prime(600),38461))\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":1,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":"2020-04-02T00:55:41.000Z","rescore_all_solutions":true,"group_id":1,"created_at":"2020-02-18T19:05:49.000Z","updated_at":"2026-01-19T18:26:06.000Z","published_at":"2020-02-18T19:39:25.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the nth lucky prime number.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://planetmath.org/luckyprime\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://planetmath.org/luckyprime\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ecan u find a way for large n?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45425,"title":"The Tortoise and the Hare - 01","description":"Suppose in an infinitely long line, the hare is standing in position 0.\r\n\r\nFrom that place, it can jump either in the +ve direction or in the -ve [but not both].\r\n\r\nOne condition is that, in i-th jump, it can move i step. Meaning -\r\n \r\n 0\r\n1 [1st step \u003e\u003e so 0+1]\r\n3 [2nd step \u003e\u003e so 1+2]\r\n6\r\n10\r\n\r\nGiven a position x, determine whether the hare will be in that position or not. \r\n\r\nFor example, \r\n\r\n if x=15 then true \r\n if x=14 then false.\r\n\r\nSimilar problem \u003chttps://www.mathworks.com/matlabcentral/cody/problems/45347-cat-s-paw-01\u003e","description_html":"\u003cp\u003eSuppose in an infinitely long line, the hare is standing in position 0.\u003c/p\u003e\u003cp\u003eFrom that place, it can jump either in the +ve direction or in the -ve [but not both].\u003c/p\u003e\u003cp\u003eOne condition is that, in i-th jump, it can move i step. Meaning -\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e0\r\n1 [1st step \u0026gt;\u0026gt; so 0+1]\r\n3 [2nd step \u0026gt;\u0026gt; so 1+2]\r\n6\r\n10\r\n\u003c/pre\u003e\u003cp\u003eGiven a position x, determine whether the hare will be in that position or not.\u003c/p\u003e\u003cp\u003eFor example,\u003c/p\u003e\u003cpre\u003e if x=15 then true \r\n if x=14 then false.\u003c/pre\u003e\u003cp\u003eSimilar problem \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45347-cat-s-paw-01\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45347-cat-s-paw-01\u003c/a\u003e\u003c/p\u003e","function_template":"function tf = rabbit_02(x)","test_suite":"%%\r\nassert(isequal(rabbit_02(15),1))\r\n%%\r\nassert(isequal(rabbit_02(45),1))\r\n%%\r\nassert(isequal(rabbit_02(555),0))\r\n%%\r\nassert(isequal(rabbit_02(25651),1))\r\n%%\r\nassert(isequal(rabbit_02(-256514),0))\r\n%%\r\nassert(isequal(rabbit_02(-2566245),1))\r\n%%\r\nassert(isequal(rabbit_02(10011),1))\r\n%%\r\nassert(isequal(rabbit_02(9191),0))\r\n%%\r\nassert(isequal(rabbit_02(9191985078),1))\r\n%%\r\nassert(isequal(rabbit_02(-111222333),0))\r\n%%\r\nassert(isequal(rabbit_02(-111236070),1))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":2,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":53,"test_suite_updated_at":"2020-04-07T04:52:58.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-07T04:44:29.000Z","updated_at":"2026-02-09T21:15:15.000Z","published_at":"2020-04-07T04:52:58.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSuppose in an infinitely long line, the hare is standing in position 0.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFrom that place, it can jump either in the +ve direction or in the -ve [but not both].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eOne condition is that, in i-th jump, it can move i step. Meaning -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[0\\n1 [1st step \u003e\u003e so 0+1]\\n3 [2nd step \u003e\u003e so 1+2]\\n6\\n10]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a position x, determine whether the hare will be in that position or not.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ if x=15 then true \\n if x=14 then false.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSimilar problem\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45347-cat-s-paw-01\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45347-cat-s-paw-01\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45377,"title":"Maya - 03","description":"Following up on the previous two problems, now you've to add or subtract two Maya numerals.\r\n\r\nThe 3rd input will be a function handle.\r\n\r\nAnd the output should also be in Maya representation. \r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/45376-maya-02\u003e\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/problems/45375-maya-01\u003e","description_html":"\u003cp\u003eFollowing up on the previous two problems, now you've to add or subtract two Maya numerals.\u003c/p\u003e\u003cp\u003eThe 3rd input will be a function handle.\u003c/p\u003e\u003cp\u003eAnd the output should also be in Maya representation.\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45376-maya-02\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45376-maya-02\u003c/a\u003e \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45375-maya-01\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45375-maya-01\u003c/a\u003e\u003c/p\u003e","function_template":"function out = maya_03(a,b,fun)","test_suite":"%%\r\nassert(isequal(maya_03('.','..',@plus),'...'))\r\n%%\r\nassert(isequal(maya_03('. o o o o','..',@minus),'....___ ....___ ....___ ...___'))\r\n%%\r\nassert(isequal(maya_03('. o o o o','._ o ....__ __',@minus),'...__ ....___ _ __'))\r\n%%\r\nassert(isequal(maya_03('. o o o o','._ o ....__ __',@plus),'. ._ o ....__ __'))\r\n%%\r\nassert(isequal(maya_03('._','._',@minus),'o'))\r\n%%\r\nassert(isequal(maya_03('._ _ _','_ _ _ ._',@plus),'_ .__ __ .__'))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":10,"test_suite_updated_at":"2020-03-23T02:11:58.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-03-23T02:01:45.000Z","updated_at":"2026-01-06T08:29:50.000Z","published_at":"2020-03-23T02:11:58.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFollowing up on the previous two problems, now you've to add or subtract two Maya numerals.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe 3rd input will be a function handle.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAnd the output should also be in Maya representation.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45376-maya-02\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45376-maya-02\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45375-maya-01\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45375-maya-01\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45385,"title":"Coin distribution","description":"Imagine, u r in a shop. ur bill is n(2200). u want to pay the bill with minimum no of coins u have.\r\n\r\nu've coins of - 2000,1000,500,100,50,20,10,5,2,1.\r\n\r\nThere are multiple ways to do that but due to the imposed condition, the correct solution for the above scenario is -\r\n\r\n 2000 - 1\r\n 100 - 2\r\n\r\nthe output should be a 2D matrix of size 2-by-x; where the 1st row contains the coins u used and 2nd row contains how many. \r\n\r\n out=[2000 100;\r\n 1 2]","description_html":"\u003cp\u003eImagine, u r in a shop. ur bill is n(2200). u want to pay the bill with minimum no of coins u have.\u003c/p\u003e\u003cp\u003eu've coins of - 2000,1000,500,100,50,20,10,5,2,1.\u003c/p\u003e\u003cp\u003eThere are multiple ways to do that but due to the imposed condition, the correct solution for the above scenario is -\u003c/p\u003e\u003cpre\u003e 2000 - 1\r\n 100 - 2\u003c/pre\u003e\u003cp\u003ethe output should be a 2D matrix of size 2-by-x; where the 1st row contains the coins u used and 2nd row contains how many.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eout=[2000 100;\r\n 1 2]\r\n\u003c/pre\u003e","function_template":"function out = coin(n)","test_suite":"%%\r\ny=[2000 100\r\n 1 2]\r\nassert(isequal(coin(2200),y))\r\n\r\n%%\r\ny=[100,20,2;2,1,1]\r\nassert(isequal(coin(222),y))\r\n\r\n%%\r\ny=[2000 500 100 50 20 10 5 2 1\r\n 3 1 2 1 1 1 1 1 1]\r\nassert(isequal( coin(6788),y))\r\n\r\n%%\r\ny=[2000 100 20 5 2 1\r\n 56728 3 2 1 1 1]\r\nassert(isequal( coin(113456348),y))\r\n\r\n%%\r\ny=[2;2]\r\nassert(isequal( coin(4),y))\r\n\r\n%%\r\ny=[1000 100 20 5 2\r\n 1 4 2 1 2]\r\nassert(isequal( coin(1449),y))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":46,"test_suite_updated_at":"2020-03-24T19:30:04.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-03-24T18:58:31.000Z","updated_at":"2026-02-09T18:16:23.000Z","published_at":"2020-03-24T19:30:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eImagine, u r in a shop. ur bill is n(2200). u want to pay the bill with minimum no of coins u have.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eu've coins of - 2000,1000,500,100,50,20,10,5,2,1.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are multiple ways to do that but due to the imposed condition, the correct solution for the above scenario is -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 2000 - 1\\n 100 - 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethe output should be a 2D matrix of size 2-by-x; where the 1st row contains the coins u used and 2nd row contains how many.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[out=[2000 100;\\n 1 2]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45247,"title":"Tell your secret","description":"A secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons?\r\nOutput will be for two scenarios -\r\n\r\n* z(1) -- each person can continue spreading the secret\r\n* z(2) -- each person can tell the secret to only two persons\r\n\r\nn.b. outputs can only be multiples of 5. ","description_html":"\u003cp\u003eA secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons?\r\nOutput will be for two scenarios -\u003c/p\u003e\u003cul\u003e\u003cli\u003ez(1) -- each person can continue spreading the secret\u003c/li\u003e\u003cli\u003ez(2) -- each person can tell the secret to only two persons\u003c/li\u003e\u003c/ul\u003e\u003cp\u003en.b. outputs can only be multiples of 5.\u003c/p\u003e","function_template":"function t = puzzle_tell_ur_secret(n)","test_suite":"%%\r\nn = 1;\r\nz = [0,0];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 2;\r\nz = [5,5];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 3;\r\nz = [5,5];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 8;\r\nz = [10,15];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 20;\r\nz = [15,20];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 31;\r\nz = [20,20];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 64;\r\nz = [20,30];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 100;\r\nz = [25,30];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 768;\r\nz = [35,45];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 3000;\r\nz = [40,55];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n%%\r\nn = 100000;\r\nz = [55,80];\r\nassert(isequal(puzzle_tell_ur_secret(n),z))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2019-12-27T00:41:26.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2019-12-27T00:10:25.000Z","updated_at":"2026-03-13T11:59:30.000Z","published_at":"2019-12-27T00:41:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA secret can be told only to 2 persons in 5 minutes. Now, these 2 more persons can spread the secret to 4 other people in the next 5 minutes. this way continues... So How long will it take to spread the secret to n persons? Output will be for two scenarios -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ez(1) -- each person can continue spreading the secret\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ez(2) -- each person can tell the secret to only two persons\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003en.b. outputs can only be multiples of 5.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":475,"title":"Is this group simply connected?","description":"Given connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\r\n\r\nExample 1:\r\n \r\n Input node_pairs = [ 8 9\r\n 8 3 ]\r\n Output tf is true\r\n \r\nThe three nodes of this graph are fully connected, since this graph could be drawn like so:\r\n\r\n 3--8--9\r\n\r\nExample 2:\r\n\r\n Input node_pairs = [ 1 2 \r\n 2 3\r\n 1 4\r\n 3 4\r\n 5 6 ]\r\n Output tf is false\r\n\r\nThis graph could be drawn like so:\r\n\r\n 1--2 5--6\r\n | |\r\n 4--3\r\n\r\nThere are two distinct subgraphs.","description_html":"\u003cp\u003eGiven connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\u003c/p\u003e\u003cp\u003eExample 1:\u003c/p\u003e\u003cpre\u003e Input node_pairs = [ 8 9\r\n 8 3 ]\r\n Output tf is true\u003c/pre\u003e\u003cp\u003eThe three nodes of this graph are fully connected, since this graph could be drawn like so:\u003c/p\u003e\u003cpre\u003e 3--8--9\u003c/pre\u003e\u003cp\u003eExample 2:\u003c/p\u003e\u003cpre\u003e Input node_pairs = [ 1 2 \r\n 2 3\r\n 1 4\r\n 3 4\r\n 5 6 ]\r\n Output tf is false\u003c/pre\u003e\u003cp\u003eThis graph could be drawn like so:\u003c/p\u003e\u003cpre\u003e 1--2 5--6\r\n | |\r\n 4--3\u003c/pre\u003e\u003cp\u003eThere are two distinct subgraphs.\u003c/p\u003e","function_template":"function tf = isConnected(node_pairs)\r\n tf = true;\r\nend","test_suite":"%%\r\n\r\nnode_pairs = [8 9; 8 3];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2;\r\n 2 3;\r\n 1 4;\r\n 3 4;\r\n 5 6 ];\r\ntf = false;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2;\r\n 2 3;\r\n 1 4;\r\n 3 4;\r\n 5 6;\r\n 6 2 ];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2; 2 100];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n\r\n%%\r\n\r\nnode_pairs = [ 1 2; 50 100];\r\ntf = false;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n\r\n%%\r\n\r\nnode_pairs = [ 4 17 ];\r\ntf = true;\r\nassert(isequal(isConnected(node_pairs),tf))\r\n","published":true,"deleted":false,"likes_count":6,"comments_count":2,"created_by":7,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":36,"test_suite_updated_at":"2012-03-09T22:15:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-03-09T22:05:26.000Z","updated_at":"2026-03-03T23:05:27.000Z","published_at":"2012-03-12T16:23:04.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven connectivity information about a graph, your job is to figure out if the graph is fully connected. You are given a list of vertex pairs that specify undirected connectivity (edges) among vertices. Vertex labels are always positive integers.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 1:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input node_pairs = [ 8 9\\n 8 3 ]\\n Output tf is true]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe three nodes of this graph are fully connected, since this graph could be drawn like so:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 3--8--9]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample 2:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Input node_pairs = [ 1 2 \\n 2 3\\n 1 4\\n 3 4\\n 5 6 ]\\n Output tf is false]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis graph could be drawn like so:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 1--2 5--6\\n | |\\n 4--3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere are two distinct subgraphs.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45363,"title":"Exponentiation","description":"Given 3 integers b,e,k; find -- \r\n\r\n mod(b^e,k)","description_html":"\u003cp\u003eGiven 3 integers b,e,k; find --\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003emod(b^e,k)\r\n\u003c/pre\u003e","function_template":"function x = mod_expo(b,e,k)\r\nend","test_suite":"%%\r\nassert(isequal(mod_expo(5,3,13),8))\r\n%%\r\nassert(isequal(mod_expo(10,4,11),1))\r\n%%\r\nassert(isequal(mod_expo(4,13,497),445))\r\n%%\r\nassert(isequal(mod_expo(4,311,497),464))\r\n%%\r\nassert(isequal(mod_expo(50,300,13),1))\r\n%%\r\nassert(isequal(mod_expo(1000,300,23),3))\r\n%%\r\nassert(isequal(mod_expo(64,43,4),0))\r\n%%\r\nassert(isequal(mod_expo(644,43,5),4))\r\n%%\r\nassert(isequal(mod_expo(327,211,56),47))\r\n%%\r\nassert(isequal(mod_expo(3277,211,234),1))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":31,"test_suite_updated_at":"2020-03-14T15:29:04.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-03-13T18:57:51.000Z","updated_at":"2026-01-25T09:46:56.000Z","published_at":"2020-03-13T18:58:07.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven 3 integers b,e,k; find --\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[mod(b^e,k)]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":55290,"title":"Cut the rod","description":"A rod of length n can be cut in different sizes. Different price is associated with different length of cuts. \r\nlength, len= [1, 2, 3, 4, 5, 6, 7, 8]\r\nprice, p = [1, 5, 8, 9,10,17,17,20]\r\nHere, if you cut a piece of length 5, the price for that piece is 10. For length of 8, the price is 20.\r\nSay, you have to obtain a rod of length x. By cutting the rod in which way will give you the maximum price.\r\n\r\nFor instance, say x=4. you can cut the rod in pieces like (1,3)/(3,1), (2,2), (1,1,1,1), (1,1,2)/(1,2,1)/... or (4).\r\nThe maximum revenue that you can get here is when you cut the rod in (2,2) pieces to get length x =\u003e 5+5=10. \r\nFor (1,3)=\u003e9; (1,1,1,1)=\u003e 4; (1,1,2)=\u003e7, (4)=\u003e9. \r\n\r\nIn this problem, you have to return the maximum reveneue you can obtain by cutting the rod of size x.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 322.875px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 161.438px; transform-origin: 407px 161.438px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA rod of length n can be cut in different sizes. Different price is associated with different length of cuts. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cul style=\"block-size: 40.875px; font-family: Helvetica, Arial, sans-serif; list-style-type: square; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 20.4375px; transform-origin: 391px 20.4375px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003elength, len= [1, 2, 3, 4, 5, 6, 7, 8]\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4375px; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2125px; text-align: left; transform-origin: 363px 10.2188px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eprice, p = [1, 5, 8, 9,10,17,17,20]\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eHere, if you cut a piece of length 5, the price for that piece is 10. For length of 8, the price is 20.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eSay, you have to obtain a rod of length x. By cutting the rod in which way will give you the maximum price.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor instance, say x=4. you can cut the rod in pieces like (1,3)/(3,1), (2,2), (1,1,1,1), (1,1,2)/(1,2,1)/... or (4).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThe maximum revenue that you can get here is when you cut the rod in (2,2) pieces to get length x =\u0026gt; 5+5=10. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFor (1,3)=\u0026gt;9; (1,1,1,1)=\u0026gt; 4; (1,1,2)=\u0026gt;7, (4)=\u0026gt;9. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eIn this problem, you have to return the maximum reveneue you can obtain by cutting the rod of size x.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = rod_cut(x,p)\r\n y = x;\r\nend","test_suite":"%%\r\np=[1,5,8,9,10,17,17,20];\r\nx=4;\r\ny_correct = 10;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n%%\r\np=[1,5,8,9,10,17,17,20];\r\nx=8;\r\ny_correct = 22;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n%%\r\np=[1,5,8,9,10,17,17,20];\r\nx=7;\r\ny_correct = 18;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n%%\r\np=[1,5,8,9,10,17,17,20];\r\nx=6;\r\ny_correct = 17;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n%%\r\np=[10,5,3,18];\r\nx=4;\r\ny_correct = 40;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n\r\n%%\r\np=[10,5,3,18];\r\nx=2;\r\ny_correct = 20;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n\r\n%%\r\np=[10,5,36,18,36];\r\nx=4;\r\ny_correct = 46;\r\nassert(isequal(rod_cut(x,p),y_correct))\r\n\r\n%%\r\np=[10,5,36,18,36];\r\nx=5;\r\ny_correct = 56;\r\nassert(isequal(rod_cut(x,p),y_correct))","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":363598,"edited_by":363598,"edited_at":"2022-08-13T23:35:18.000Z","deleted_by":null,"deleted_at":null,"solvers_count":12,"test_suite_updated_at":"2022-08-13T23:35:18.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2022-08-13T23:28:51.000Z","updated_at":"2026-01-06T08:34:18.000Z","published_at":"2022-08-13T23:35:18.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA rod of length n can be cut in different sizes. Different price is associated with different length of cuts. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003elength, len= [1, 2, 3, 4, 5, 6, 7, 8]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"1\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eprice, p = [1, 5, 8, 9,10,17,17,20]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, if you cut a piece of length 5, the price for that piece is 10. For length of 8, the price is 20.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSay, you have to obtain a rod of length x. By cutting the rod in which way will give you the maximum price.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor instance, say x=4. you can cut the rod in pieces like (1,3)/(3,1), (2,2), (1,1,1,1), (1,1,2)/(1,2,1)/... or (4).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe maximum revenue that you can get here is when you cut the rod in (2,2) pieces to get length x =\u0026gt; 5+5=10. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor (1,3)=\u0026gt;9; (1,1,1,1)=\u0026gt; 4; (1,1,2)=\u0026gt;7, (4)=\u0026gt;9. \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem, you have to return the maximum reveneue you can obtain by cutting the rod of size x.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":1483,"title":"Number of paths on a grid","description":"\r\nConsider a grid formed by n vertices vertically down, and m vertices horizontally right. Your starting point is at the top left vertex. \r\nYour destination is the bottom right vertex. You are permitted at each vertex to choose to move down or right, that is in the direction towards the destination. You are not to move on what constitutes a back step like moving left or up. If you hit the bottom boundary, or right boundary take it to be given there is only 1 way to the destination, that is following along the boundary. \r\n\r\nEx: in a 2x2 grid there are two ways. One way: First down, then right. The other way: First right, then down. \r\n\r\n4x3 has 10 ways\r\n\r\n6x5 has 126 ways\r\n\r\nThis problem can be solved using dynamic programming but there are other methods too. \r\n\r\nProblem 7)\r\nPrev: \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1482 1482\u003e\r\nNext: \u003chttp://www.mathworks.com/matlabcentral/cody/problems/1484 1484\u003e","description_html":"\u003cdiv style = \"text-align: start; line-height: 20px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: normal; text-decoration: none; white-space: normal; \"\u003e\u003cdiv style=\"display: block; min-width: 0px; padding-top: 0px; transform-origin: 332px 148.5px; vertical-align: baseline; perspective-origin: 332px 148.5px; \"\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 63px; white-space: pre-wrap; perspective-origin: 309px 63px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eConsider a grid formed by n vertices vertically down, and m vertices horizontally right. Your starting point is at the top left vertex. Your destination is the bottom right vertex. You are permitted at each vertex to choose to move down or right, that is in the direction towards the destination. You are not to move on what constitutes a back step like moving left or up. If you hit the bottom boundary, or right boundary take it to be given there is only 1 way to the destination, that is following along the boundary.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 21px; white-space: pre-wrap; perspective-origin: 309px 21px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eEx: in a 2x2 grid there are two ways. One way: First down, then right. The other way: First right, then down.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 10.5px; white-space: pre-wrap; perspective-origin: 309px 10.5px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e4x3 has 10 ways\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 10.5px; white-space: pre-wrap; perspective-origin: 309px 10.5px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e6x5 has 126 ways\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 10.5px; white-space: pre-wrap; perspective-origin: 309px 10.5px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eThis problem can be solved using dynamic programming but there are other methods too.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-bottom: 9px; margin-left: 4px; margin-right: 10px; margin-top: 2px; text-align: left; transform-origin: 309px 10.5px; white-space: pre-wrap; perspective-origin: 309px 10.5px; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eProblem 7) Prev:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"http://www.mathworks.com/matlabcentral/cody/problems/1482\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e1482\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e Next:\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"display: inline; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; transform-origin: 0px 0px; perspective-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"http://www.mathworks.com/matlabcentral/cody/problems/1484\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e1484\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = paths2dest_ongrid(n,m)\r\n y = n+m;\r\nend","test_suite":"%%\r\nm = 1; n = 1 ;\r\ny_correct = 1;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 2; n = 2 ;\r\ny_correct = 2;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 4; n = 3 ;\r\ny_correct = 10;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 6; n = 5 ;\r\ny_correct = 126;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 5; n = 5 ;\r\ny_correct = 70;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 1; n = 100 ;\r\ny_correct = 1;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 100; n = 1 ;\r\ny_correct = 1;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 2; n = 100 ;\r\ny_correct = 100;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 100; n = 2 ;\r\ny_correct = 100;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n\r\n%%\r\nm = 15; n = 20 ;\r\ny_correct = 818809200;\r\nassert(isequal(paths2dest_ongrid(m,n),y_correct))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":5,"created_by":11275,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":81,"test_suite_updated_at":"2020-09-28T20:02:06.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-05-01T14:58:23.000Z","updated_at":"2026-03-19T08:10:31.000Z","published_at":"2013-05-01T14:58:23.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConsider a grid formed by n vertices vertically down, and m vertices horizontally right. Your starting point is at the top left vertex. Your destination is the bottom right vertex. You are permitted at each vertex to choose to move down or right, that is in the direction towards the destination. You are not to move on what constitutes a back step like moving left or up. If you hit the bottom boundary, or right boundary take it to be given there is only 1 way to the destination, that is following along the boundary.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEx: in a 2x2 grid there are two ways. One way: First down, then right. The other way: First right, then down.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e4x3 has 10 ways\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e6x5 has 126 ways\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem can be solved using dynamic programming but there are other methods too.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 7) Prev:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1482\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e1482\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e Next:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.com/matlabcentral/cody/problems/1484\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e1484\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45449,"title":"Quarantine Days","description":"In these quarantine days, a list of what Max may do on a typical day is given in a table with the starting hour to ending hour.\r\n\r\n Task Start End\r\nSleep 0 6\r\nWalk 6 7\r\nBreakfast 8 9\r\nMovie 10 16\r\nStudy 15 21\r\nDinner 22 23\r\n\r\nNow Max wants to perform the maximum amount of tasks in a single day. How should he make his routine?\r\n\r\nFor this problem -- just return the number of tasks.","description_html":"\u003cp\u003eIn these quarantine days, a list of what Max may do on a typical day is given in a table with the starting hour to ending hour.\u003c/p\u003e\u003cpre\u003e Task Start End\r\nSleep 0 6\r\nWalk 6 7\r\nBreakfast 8 9\r\nMovie 10 16\r\nStudy 15 21\r\nDinner 22 23\u003c/pre\u003e\u003cp\u003eNow Max wants to perform the maximum amount of tasks in a single day. How should he make his routine?\u003c/p\u003e\u003cp\u003eFor this problem -- just return the number of tasks.\u003c/p\u003e","function_template":"function yy = activity_2(x)\r\nend","test_suite":"%%\r\nStart=[0,6,8,10,15,22]';\r\nFinish=[6,7,9,16,21,23]';\r\nx=table(Start,Finish);\r\nassert(isequal(activity_2(x),5))\r\n\r\n%%\r\nStart=[0,5,8,10,15,22,23,3,11,16]';\r\nFinish=[6,8,11,11,17,24,24,4,16,18]';\r\nx=table(Start,Finish);\r\nassert(isequal(activity_2(x),6))\r\n\r\n%%\r\nStart=[0,5,8,10,15,22,23,3,11,16,5,12,14]';\r\nFinish=[6,8,11,11,17,24,24,4,16,18,10,13,16]';\r\nx=table(Start,Finish);\r\nassert(isequal(activity_2(x),7))\r\n\r\n%%\r\nStart=[1,12,16,21,11]';\r\nFinish=[11,13,18,24,17]';\r\nx=table(Start,Finish);\r\nassert(isequal(activity_2(x),4))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-13T02:15:41.000Z","updated_at":"2026-02-09T22:19:31.000Z","published_at":"2020-04-13T02:31:01.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn these quarantine days, a list of what Max may do on a typical day is given in a table with the starting hour to ending hour.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Task Start End\\nSleep 0 6\\nWalk 6 7\\nBreakfast 8 9\\nMovie 10 16\\nStudy 15 21\\nDinner 22 23]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow Max wants to perform the maximum amount of tasks in a single day. How should he make his routine?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this problem -- just return the number of tasks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45433,"title":"The Dark Knight","description":" The current position of the knight is x \r\n The desired destination is y\r\n The size of the chessboard is n.\r\n\r\nFind the minimum number of moves required by the knight to reach the destination.\r\n\r\nFor example, \r\n \r\n x=[2,2] y=[3,3] -- moves required = 2 \r\n [2,2] \u003e [1,4] \u003e [3,3]\r\n","description_html":"\u003cpre class=\"language-matlab\"\u003eThe current position of the knight is x \r\nThe desired destination is y\r\nThe size of the chessboard is n.\r\n\u003c/pre\u003e\u003cp\u003eFind the minimum number of moves required by the knight to reach the destination.\u003c/p\u003e\u003cp\u003eFor example,\u003c/p\u003e\u003cpre\u003e x=[2,2] y=[3,3] -- moves required = 2 \r\n [2,2] \u0026gt; [1,4] \u0026gt; [3,3]\u003c/pre\u003e","function_template":"function out = knight_step(x,y,n)","test_suite":"%%\r\nassert(isequal(knight_step([2,2],[3,3],8),2))\r\n%%\r\nassert(isequal(knight_step([2,2],[1,1],20),4))\r\n\r\n%%\r\nassert(isequal(knight_step([2,2],[8,8],12),4))\r\n\r\n%%\r\nassert(isequal(knight_step([2,2],[12,11],12),7))\r\n\r\n%%\r\nassert(isequal(knight_step([1,3],[8,3],8),5))\r\n%%\r\nassert(isequal(knight_step([1,3],[5,4],8),3))\r\n%%\r\nassert(isequal(knight_step([8,2],[1,2],8),5))\r\n\r\n%%\r\nassert(isequal(knight_step([8,7],[21,32],50),14))\r\n%%\r\nassert(isequal(knight_step([5,19],[5,19],20),0))\r\n%%\r\nassert(isequal(knight_step([5,19],[19,5],20),10))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":21,"test_suite_updated_at":"2020-04-10T06:28:31.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-10T06:20:09.000Z","updated_at":"2026-01-21T12:55:02.000Z","published_at":"2020-04-10T06:28:31.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[The current position of the knight is x \\nThe desired destination is y\\nThe size of the chessboard is n.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the minimum number of moves required by the knight to reach the destination.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x=[2,2] y=[3,3] -- moves required = 2 \\n [2,2] \u003e [1,4] \u003e [3,3]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45343,"title":"How lucky r u ?","description":"Find the nth number in the following sequence\r\n\r\n\u003chttps://oeis.org/A264940\u003e","description_html":"\u003cp\u003eFind the nth number in the following sequence\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://oeis.org/A264940\"\u003ehttps://oeis.org/A264940\u003c/a\u003e\u003c/p\u003e","function_template":"function y = lucky(n)\r\n\r\n y = x;\r\nend","test_suite":"%%\r\nassert(isequal(lucky(1),0))\r\n%%\r\nassert(isequal(lucky(5),3))\r\n%%\r\nassert(isequal(lucky(12),2))\r\n%%\r\nassert(isequal(lucky(19),7))\r\n%%\r\nassert(isequal(lucky(27),9))\r\n%%\r\nassert(isequal(lucky(31),0))\r\n%%\r\nassert(isequal(lucky(37),0))\r\n%%\r\nassert(isequal(lucky(45),13))\r\n%%\r\nassert(isequal(lucky(55),15))\r\n%%\r\nassert(isequal(lucky(85),21))\r\n%%\r\nassert(isequal(lucky(185),3))\r\n%%\r\nassert(isequal(lucky(303),0))\r\n%%\r\nassert(isequal(lucky(555),13))\r\n\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-02-19T07:42:17.000Z","updated_at":"2025-11-21T23:33:51.000Z","published_at":"2020-02-19T07:42:26.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the nth number in the following sequence\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://oeis.org/A264940\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://oeis.org/A264940\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1049,"title":"Path of least resistance","description":"Find the length of the shortest path through the matrix from the top left to bottom right corner. You may move right, down, or diagonally right-down one element at a time. The length of the path is the sum of the elements you pass through.\r\nE.g.\r\n M = [*8 6 10 10 4 7 7 7\r\n 9 *1 10 5 9 0 8 2\r\n 1 3 *2 8 8 8 7 7\r\n 9 5 10 *1 *10 *9 *4 *0 ]; \r\n\r\n\u003e\u003e shortest_path(M)\r\n\r\nans =\r\n\r\n 35\r\nThe shortest path through this matrix has length 35. Each element along the path is marked with a *","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 317.333px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 158.667px; transform-origin: 407px 158.667px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381.5px 8px; transform-origin: 381.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFind the length of the shortest path through the matrix from the top left to bottom right corner. You may move right, down, or diagonally right-down one element at a time. The length of the path is the sum of the elements you pass through.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12.5px 8px; transform-origin: 12.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eE.g.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 204.333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 102.167px; transform-origin: 404px 102.167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 224px 8.5px; tab-size: 4; transform-origin: 224px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e M = [*8 6 10 10 4 7 7 7\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 224px 8.5px; tab-size: 4; transform-origin: 224px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 9 *1 10 5 9 0 8 2\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 224px 8.5px; tab-size: 4; transform-origin: 224px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 1 3 *2 8 8 8 7 7\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 240px 8.5px; tab-size: 4; transform-origin: 240px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 9 5 10 *1 *10 *9 *4 *0 ]; \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 76px 8.5px; tab-size: 4; transform-origin: 76px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u0026gt;\u0026gt; shortest_path(M)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 20px 8.5px; tab-size: 4; transform-origin: 20px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003eans =\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 32px 8.5px; tab-size: 4; transform-origin: 32px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 35\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 308px 8px; transform-origin: 308px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe shortest path through this matrix has length 35. Each element along the path is marked with a\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 4.5px 8px; transform-origin: 4.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e *\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function p = shortest_path(M)\r\n p = sum(M(1:end,1)) + sum(M(end,2:end));\r\nend","test_suite":"%%\r\nM = [8 6 10 10 4 7 7 7\r\n 9 1 10 5 9 0 8 2\r\n 1 3 2 8 8 8 7 7\r\n 9 5 10 1 10 9 4 0 ];\r\ny_correct = 35;\r\nassert(isequal(shortest_path(M),y_correct))\r\n\r\n%%\r\nM = [6 8 5 5 3\r\n 5 4 8 0 5\r\n 9 6 9 3 2\r\n 3 1 1 2 6\r\n 8 1 6 8 3 ];\r\ny_correct = 22;\r\nassert(isequal(shortest_path(M),y_correct))\r\n\r\n%% \r\nM = hadamard(8);\r\ny_correct = -5;\r\nassert(isequal(shortest_path(M),y_correct))\r\n\r\n%% \r\nassert(isequal(arrayfun(@(x) shortest_path(eye(x)), 2:5),[2 2 2 2]))\r\n","published":true,"deleted":false,"likes_count":9,"comments_count":1,"created_by":450,"edited_by":223089,"edited_at":"2022-05-09T17:30:33.000Z","deleted_by":null,"deleted_at":null,"solvers_count":80,"test_suite_updated_at":"2022-05-09T17:30:33.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2012-11-22T20:58:51.000Z","updated_at":"2026-01-06T08:40:37.000Z","published_at":"2012-11-22T21:17:01.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the length of the shortest path through the matrix from the top left to bottom right corner. You may move right, down, or diagonally right-down one element at a time. The length of the path is the sum of the elements you pass through.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eE.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ M = [*8 6 10 10 4 7 7 7\\n 9 *1 10 5 9 0 8 2\\n 1 3 *2 8 8 8 7 7\\n 9 5 10 *1 *10 *9 *4 *0 ]; \\n\\n\u003e\u003e shortest_path(M)\\n\\nans =\\n\\n 35]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe shortest path through this matrix has length 35. Each element along the path is marked with a\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e *\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45415,"title":"Find an optimal placement of coolers on a grid","description":"In a certain chemical plant, 6 new pieces of cooling equipment (coolers) are to be installed in a vacant space. This vacant space was divided into a grid of 6 cells by 6 cells. Your task is to assign the 6 coolers to 6 cells in this grid with the following requirements:\r\n\r\n# There must be one cooler for every column in the grid.\r\n# If you decide to install a cooler at row R, column C, then the cooler at column C+1 must be installed either on row R-1, R, or R+1 only. This is done to ease the connection of coolers by piping.\r\n# The total installation cost for the 6 coolers must be minimum.\r\n\r\nFor this problem, you are given a cost matrix (COST) in relation to the third requirement above. COST is a 6 x 6 matrix where the _r,c_-th element is the cost of assigning any cooler to row _r_, column _c_ (All the coolers are identical). Write a function that accepts the matrix COST, and output the value of the minimum cost of installation. You are ensured that all elements of COST are integers in the range [1,1000].\r\n\r\nIn the sample test case below, the optimal placement is at the following rows: 4,3,3,2,2,2.\r\n\r\n \u003e\u003e COST = [695 766 710 119 752 548;\r\n 318 796 755 499 256 139;\r\n 951 187 277 960 506 150;\r\n 35 490 680 341 700 258;\r\n 439 446 656 586 891 841;\r\n 382 647 163 224 960 255];\r\n \u003e\u003e coolers(COST)\r\n ans = \r\n 1393\r\n \r\nMeanwhile, the optimal placement for the case below is at rows: 5,6,5,6,5,6\r\n\r\n \u003e\u003e COST = [815 617 918 76 569 312;\r\n 244 474 286 54 470 529;\r\n 930 352 758 531 455 988;\r\n 350 831 754 780 12 602;\r\n 197 586 381 935 163 263;\r\n 252 550 568 130 795 100];\r\n \u003e\u003e coolers(COST)\r\n ans = \r\n 1521\r\n","description_html":"\u003cp\u003eIn a certain chemical plant, 6 new pieces of cooling equipment (coolers) are to be installed in a vacant space. This vacant space was divided into a grid of 6 cells by 6 cells. Your task is to assign the 6 coolers to 6 cells in this grid with the following requirements:\u003c/p\u003e\u003col\u003e\u003cli\u003eThere must be one cooler for every column in the grid.\u003c/li\u003e\u003cli\u003eIf you decide to install a cooler at row R, column C, then the cooler at column C+1 must be installed either on row R-1, R, or R+1 only. This is done to ease the connection of coolers by piping.\u003c/li\u003e\u003cli\u003eThe total installation cost for the 6 coolers must be minimum.\u003c/li\u003e\u003c/ol\u003e\u003cp\u003eFor this problem, you are given a cost matrix (COST) in relation to the third requirement above. COST is a 6 x 6 matrix where the \u003ci\u003er,c\u003c/i\u003e-th element is the cost of assigning any cooler to row \u003ci\u003er\u003c/i\u003e, column \u003ci\u003ec\u003c/i\u003e (All the coolers are identical). Write a function that accepts the matrix COST, and output the value of the minimum cost of installation. You are ensured that all elements of COST are integers in the range [1,1000].\u003c/p\u003e\u003cp\u003eIn the sample test case below, the optimal placement is at the following rows: 4,3,3,2,2,2.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; COST = [695 766 710 119 752 548;\r\n 318 796 755 499 256 139;\r\n 951 187 277 960 506 150;\r\n 35 490 680 341 700 258;\r\n 439 446 656 586 891 841;\r\n 382 647 163 224 960 255];\r\n\u0026gt;\u0026gt; coolers(COST)\r\nans = \r\n 1393\r\n\u003c/pre\u003e\u003cp\u003eMeanwhile, the optimal placement for the case below is at rows: 5,6,5,6,5,6\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e\u0026gt;\u0026gt; COST = [815 617 918 76 569 312;\r\n 244 474 286 54 470 529;\r\n 930 352 758 531 455 988;\r\n 350 831 754 780 12 602;\r\n 197 586 381 935 163 263;\r\n 252 550 568 130 795 100];\r\n\u0026gt;\u0026gt; coolers(COST)\r\nans = \r\n 1521\r\n\u003c/pre\u003e","function_template":"function y = coolers(COST)\r\n y = x;\r\nend","test_suite":"%%\r\nfiletext = fileread('coolers.m')\r\nassert(isempty(strfind(filetext, 'rand')))\r\n%%\r\nCOST = [815 617 918 76 569 312;\r\n 244 474 286 54 470 529;\r\n 930 352 758 531 455 988;\r\n 350 831 754 780 12 602;\r\n 197 586 381 935 163 263;\r\n 252 550 568 130 795 100];\r\nassert(isequal(coolers(COST),1521))\r\n%%\r\nCOST = [690 153 107 85 182 550;\r\n 749 826 962 400 264 145;\r\n 451 539 5 260 146 854;\r\n 84 997 775 801 137 623;\r\n 229 79 818 432 870 351;\r\n 914 443 869 911 580 514];\r\nassert(isequal(coolers(COST),1179))\r\n%%\r\nCOST = [402 418 338 242 576 44;\r\n 76 50 901 404 60 169;\r\n 240 903 370 97 235 650;\r\n 124 945 112 132 354 732;\r\n 184 491 781 943 822 648;\r\n 240 490 390 957 16 451];\r\nassert(isequal(coolers(COST),697))\r\n%%\r\nCOST = [1 1 1000 1000 1000 1;\r\n 1 1000 1 1000 1000 1000;\r\n 1 1000 1000 1000 1 1000;\r\n 1 1000 1000 1 1000 1000;\r\n 1 1000 1000 1000 1000 1000;\r\n 1 1000 1000 1000 1000 1000];\r\nassert(isequal(coolers(COST),2004))\r\n%%\r\nCOST = [548 369 487 818 351 208;\r\n 297 626 436 795 940 302;\r\n 745 781 447 645 876 471;\r\n 189 82 307 379 551 231;\r\n 687 930 509 812 623 845;\r\n 184 776 511 533 588 195];\r\nassert(isequal(coolers(COST),1739))\r\n%%\r\nCOST = [226 431 259 222 86 489;\r\n 171 185 409 118 263 579;\r\n 228 905 595 297 802 238;\r\n 436 980 263 319 30 459;\r\n 312 439 603 425 929 964;\r\n 924 112 712 508 731 547];\r\nassert(isequal(coolers(COST),1234))\r\n%%\r\nCOST = [522 368 99 107 891 501;\r\n 232 988 262 654 335 480;\r\n 489 38 336 495 699 905;\r\n 625 886 680 780 198 610;\r\n 680 914 137 716 31 618;\r\n 396 797 722 904 745 860];\r\nassert(isequal(coolers(COST),1454))\r\n%%\r\nCOST = [806 490 60 819 973 84;\r\n 577 168 682 818 649 134;\r\n 183 979 43 723 801 174;\r\n 240 713 72 150 454 391;\r\n 887 501 522 660 433 832;\r\n 29 472 97 519 826 804];\r\nassert(isequal(coolers(COST),1172))\r\n%%\r\nCOST = [61 292 373 53 418 699;\r\n 400 432 199 738 984 667;\r\n 527 16 490 270 302 179;\r\n 417 985 340 423 702 129;\r\n 657 168 952 548 667 1000;\r\n 628 107 921 943 540 172];\r\nassert(isequal(coolers(COST),1253))\r\n%%\r\nCOST = [33 461 191 385 825 907;\r\n 562 982 429 583 983 880;\r\n 882 157 483 252 731 818;\r\n 670 856 121 291 344 261;\r\n 191 645 590 618 585 595;\r\n 369 377 227 266 108 23];\r\nassert(isequal(coolers(COST),1192))\r\n%%\r\nCOST = [426 599 69 719 779 441;\r\n 313 471 320 969 424 528;\r\n 162 696 531 532 91 458;\r\n 179 700 655 326 267 876;\r\n 423 639 408 106 154 519;\r\n 95 34 820 611 282 944];\r\nassert(isequal(coolers(COST),1316))\r\n%%\r\nCOST = [638 696 345 916 323 474;\r\n 958 68 781 2 785 153;\r\n 241 255 676 463 472 342;\r\n 677 225 7 425 36 608;\r\n 290 668 603 461 176 192;\r\n 672 845 387 771 722 739];\r\nassert(isequal(coolers(COST),1126))\r\n%%\r\nCOST = [243 92 648 237 771 257;\r\n 918 577 680 120 351 614;\r\n 270 684 636 608 663 583;\r\n 766 547 946 451 417 541;\r\n 189 426 209 459 842 870;\r\n 288 645 710 662 833 265];\r\nassert(isequal(coolers(COST),1711))\r\n%%\r\nCOST = [319 545 219 366 193 862;\r\n 120 648 106 764 139 485;\r\n 940 544 110 628 697 394;\r\n 646 722 64 772 94 672;\r\n 480 523 405 933 526 742;\r\n 640 994 449 973 531 521];\r\nassert(isequal(coolers(COST),1669))\r\n%%\r\nCOST = [674 622 842 636 885 896;\r\n 575 722 853 599 699 975;\r\n 794 844 722 911 905 664;\r\n 632 680 510 715 878 836;\r\n 523 869 667 944 689 720;\r\n 878 698 713 696 609 917];\r\nassert(isequal(coolers(COST),3837))\r\n%%\r\nCOST = [82 122 681 602 355 371;...\r\n 483 544 417 347 776 384;...\r\n 129 315 643 365 237 862;...\r\n 253 383 215 172 845 464;...\r\n 884 792 618 796 817 571;...\r\n 197 840 676 493 847 696];\r\nassert(isequal(coolers(COST),1452))\r\n%%\r\nCOST = [961 170 710 563 536 327;...\r\n 547 179 176 177 199 603;...\r\n 637 244 859 514 624 362;...\r\n 571 752 910 549 27 135;...\r\n 928 200 962 166 319 914;...\r\n 864 983 571 494 533 641];\r\nassert(isequal(coolers(COST),1569))\r\n%%\r\nCOST = [659 381 223 112 267 869;...\r\n 676 822 1000 425 292 529;...\r\n 745 172 64 614 189 915;...\r\n 843 330 426 989 23 974;...\r\n 517 967 405 220 450 586;...\r\n 152 807 401 355 244 119];\r\nassert(isequal(coolers(COST),1835))\r\n%%\r\nCOST = [927 925 215 554 571 679;...\r\n 594 643 249 631 336 213;...\r\n 884 105 227 986 958 82;...\r\n 425 701 704 635 440 275;...\r\n 608 396 755 601 602 868;...\r\n 71 85 548 910 721 560];\r\nassert(isequal(coolers(COST),1751))\r\n%%\r\nCOST = [465 433 384 904 809 299;...\r\n 431 506 712 219 180 769;...\r\n 774 376 481 874 166 502;...\r\n 654 481 730 83 182 910;...\r\n 658 343 938 466 692 58;...\r\n 162 778 518 22 214 437];\r\nassert(isequal(coolers(COST),1317))\r\n%%\r\nCOST = [573 952 497 860 78 228;...\r\n 566 767 809 627 339 710;...\r\n 824 752 633 181 581 149;...\r\n 127 139 689 574 476 659;...\r\n 301 350 640 164 806 634;...\r\n 3 152 730 907 531 230];\r\nassert(isequal(coolers(COST),1568))\r\n%%\r\nCOST = [183 958 897 179 998 879;...\r\n 167 26 190 747 5 747;...\r\n 150 972 661 50 543 118;...\r\n 203 298 942 72 862 510;...\r\n 955 526 976 490 910 169;...\r\n 16 863 108 850 846 832];\r\nassert(isequal(coolers(COST),539))\r\n%%\r\nCOST = [929 868 245 81 27 760;...\r\n 170 742 641 361 786 926;...\r\n 884 448 809 829 923 833;...\r\n 388 710 854 215 493 260;...\r\n 383 945 399 792 835 214;...\r\n 272 175 116 655 132 523];\r\nassert(isequal(coolers(COST),1564))\r\n%%\r\nCOST = [398 438 798 217 378 972;...\r\n 480 773 656 812 168 361;...\r\n 994 745 33 139 541 645;...\r\n 605 443 558 882 102 68;...\r\n 945 54 720 924 40 208;...\r\n 491 88 111 13 934 40];\r\nassert(isequal(coolers(COST),749))\r\n%%\r\nCOST = [470 581 9 642 470 879;...\r\n 151 541 825 106 220 189;...\r\n 992 706 768 269 923 760;...\r\n 428 6 998 764 321 32;...\r\n 956 783 228 806 858 643;...\r\n 725 927 920 105 260 567];\r\nassert(isequal(coolers(COST),1216))\r\n%%\r\nCOST = [377 187 35 595 562 603;...\r\n 213 486 489 499 634 474;...\r\n 793 839 972 568 931 357;...\r\n 146 142 113 427 978 476;...\r\n 490 733 744 77 94 672;...\r\n 13 692 639 291 662 960];\r\nassert(isequal(coolers(COST),1048))\r\n%%\r\nCOST = [90 52 454 629 707 46;...\r\n 798 505 738 133 536 886;...\r\n 591 769 510 619 194 840;...\r\n 913 283 383 384 690 119;...\r\n 102 226 906 992 51 411;...\r\n 294 332 966 287 185 121];\r\nassert(isequal(coolers(COST),1042))\r\n%%\r\nCOST = [573 666 232 754 549 464;...\r\n 950 974 53 622 461 590;...\r\n 257 623 902 395 646 188;...\r\n 990 64 794 360 514 612;...\r\n 350 374 374 89 815 52;...\r\n 209 167 833 342 98 576];\r\nassert(isequal(coolers(COST),934))\r\n%%\r\nCOST = [843 797 666 67 652 382;...\r\n 500 294 961 898 134 301;...\r\n 440 116 944 498 639 341;...\r\n 150 376 113 772 385 919;...\r\n 29 829 649 61 766 457;...\r\n 757 842 481 263 653 443];\r\nassert(isequal(coolers(COST),1166))\r\n%%\r\nCOST = [455 767 602 56 365 673;...\r\n 946 343 650 99 677 203;...\r\n 220 619 343 650 376 869;...\r\n 883 454 494 765 864 752;...\r\n 20 11 702 988 292 420;...\r\n 342 600 888 126 134 1];\r\nassert(isequal(coolers(COST),994))\r\n%%\r\nCOST = [150 436 24 66 150 132;...\r\n 274 904 575 924 351 887;...\r\n 873 926 47 535 336 675;...\r\n 602 506 423 367 785 836;...\r\n 322 628 468 364 487 657;...\r\n 285 720 23 152 465 984];\r\nassert(isequal(coolers(COST),958))\r\n%%\r\nCOST = [980 191 386 245 842 952;...\r\n 251 125 311 804 79 966;...\r\n 625 3 4 824 238 766;...\r\n 729 153 816 853 818 575;...\r\n 499 535 639 468 406 916;...\r\n 850 511 449 971 467 496];\r\nassert(isequal(coolers(COST),1655))\r\n%%\r\nCOST = [167 167 988 896 65 887;...\r\n 326 948 151 835 264 421;...\r\n 297 812 959 3 103 284;...\r\n 559 711 531 641 484 49;...\r\n 68 971 75 804 419 220;...\r\n 69 999 312 246 382 240];\r\nassert(isequal(coolers(COST),640))\r\n%%\r\nCOST = [30 653 667 689 433 469;...\r\n 703 321 848 321 905 546;...\r\n 8 104 763 532 631 180;...\r\n 611 536 808 874 984 635;...\r\n 409 165 633 55 586 963;...\r\n 249 884 711 501 841 535];\r\nassert(isequal(coolers(COST),2007))\r\n%%\r\nCOST = [480 679 683 689 62 13;...\r\n 794 566 947 148 220 217;...\r\n 93 479 100 778 83 12;...\r\n 881 321 512 400 951 643;...\r\n 4 602 111 899 17 517;...\r\n 512 914 546 308 115 246];\r\nassert(isequal(coolers(COST),648))\r\n%%\r\nCOST = [194 309 342 580 56 396;...\r\n 91 745 840 329 35 170;...\r\n 369 840 983 269 287 431;...\r\n 8 263 627 551 78 417;...\r\n 603 515 182 181 901 729;...\r\n 479 447 124 679 847 407];\r\nassert(isequal(coolers(COST),1129))\r\n%%\r\nCOST = [952 211 79 334 443 924;...\r\n 912 131 934 693 633 153;...\r\n 952 521 603 204 930 406;...\r\n 347 906 378 959 530 313;...\r\n 291 403 665 712 627 694;...\r\n 887 216 793 167 681 891];\r\nassert(isequal(coolers(COST),2052))\r\n%%\r\nCOST = [491 677 35 27 661 34;...\r\n 806 829 437 501 330 407;...\r\n 327 111 937 828 660 717;...\r\n 550 280 263 259 14 922;...\r\n 389 768 570 46 719 985;...\r\n 897 217 360 247 392 984];\r\nassert(isequal(coolers(COST),1266))\r\n%%\r\nCOST = [897 655 269 739 879 390;...\r\n 866 864 153 13 903 300;...\r\n 801 275 631 606 153 735;...\r\n 555 841 317 577 193 105;...\r\n 419 71 960 808 791 793;...\r\n 128 379 499 655 61 783];\r\nassert(isequal(coolers(COST),1254))\r\n%%\r\nCOST = [533 130 314 596 463 399;...\r\n 254 451 642 537 368 478;...\r\n 71 673 787 331 680 67;...\r\n 626 857 290 412 568 412;...\r\n 25 499 498 795 652 970;...\r\n 63 49 819 344 492 781];\r\nassert(isequal(coolers(COST),1580))\r\n%%\r\nCOST = [730 112 727 411 679 798;...\r\n 766 397 148 143 254 712;...\r\n 757 493 148 799 844 784;...\r\n 844 259 705 931 294 624;...\r\n 771 37 381 5 27 826;...\r\n 979 975 77 651 94 36];\r\nassert(isequal(coolers(COST),953))\r\n%%\r\nCOST = [406 27 672 377 507 436;...\r\n 250 156 53 114 329 158;...\r\n 481 834 735 965 754 601;...\r\n 881 195 500 433 837 938;...\r\n 281 830 944 85 254 108;...\r\n 600 339 290 717 535 900];\r\nassert(isequal(coolers(COST),931))\r\n%%\r\nCOST = [551 355 642 427 787 943;...\r\n 428 774 128 34 512 97;...\r\n 153 882 497 930 563 846;...\r\n 248 735 311 925 685 910;...\r\n 448 407 579 359 93 12;...\r\n 533 605 944 260 873 524];\r\nassert(isequal(coolers(COST),1430))\r\n%%\r\nCOST = [651 279 401 430 885 568;...\r\n 386 840 555 38 256 895;...\r\n 650 427 444 976 910 215;...\r\n 763 632 91 523 895 4;...\r\n 576 834 745 910 399 881;...\r\n 632 271 33 384 626 236];\r\nassert(isequal(coolers(COST),1575))\r\n%%\r\nCOST = [245 391 532 153 716 903;...\r\n 641 802 889 231 281 290;...\r\n 305 158 264 658 413 500;...\r\n 826 626 235 563 363 784;...\r\n 884 699 840 292 782 678;...\r\n 946 86 496 623 136 150];\r\nassert(isequal(coolers(COST),1276))\r\n%%\r\nCOST = [697 977 429 793 902 349;...\r\n 130 126 15 420 52 166;...\r\n 946 753 326 533 809 29;...\r\n 887 828 135 926 335 956;...\r\n 516 782 451 900 229 681;...\r\n 680 191 573 545 823 861];\r\nassert(isequal(coolers(COST),772))\r\n%%\r\nCOST = [940 301 652 887 695 895;...\r\n 681 74 170 115 207 842;...\r\n 918 768 532 443 555 131;...\r\n 257 85 634 660 880 190;...\r\n 886 729 15 295 558 154;...\r\n 921 448 471 951 753 29];\r\nassert(isequal(coolers(COST),1239))\r\n%%\r\nCOST = [10 496 746 686 237 463;...\r\n 597 259 338 268 196 926;...\r\n 610 733 585 970 706 216;...\r\n 919 117 469 184 181 2;...\r\n 734 747 88 300 523 907;...\r\n 302 810 829 412 297 681];\r\nassert(isequal(coolers(COST),1182))\r\n%%\r\nCOST = [515 394 842 779 399 396;...\r\n 523 8 49 728 671 399;...\r\n 103 546 317 651 441 752;...\r\n 997 510 784 665 133 523;...\r\n 359 247 973 939 440 491;...\r\n 626 46 587 536 548 89];\r\nassert(isequal(coolers(COST),1435))\r\n%%\r\nCOST = [251 93 71 159 652 1;...\r\n 448 954 301 63 499 641;...\r\n 638 163 814 702 285 8;...\r\n 710 971 77 87 831 107;...\r\n 993 598 355 617 819 107;...\r\n 933 241 133 174 939 368];\r\nassert(isequal(coolers(COST),771))\r\n%%\r\nCOST = [240 788 66 813 71 954;...\r\n 347 270 611 815 242 209;...\r\n 250 844 702 90 732 117;...\r\n 388 741 112 732 41 647;...\r\n 422 827 96 904 425 109;...\r\n 641 183 598 453 541 984];\r\nassert(isequal(coolers(COST),1343))\r\n%%\r\nCOST = [249 916 747 918 880 282;...\r\n 607 901 476 471 799 169;...\r\n 817 215 584 270 325 746;...\r\n 831 548 261 763 670 478;...\r\n 490 785 85 773 297 654;...\r\n 761 195 299 22 930 967];\r\nassert(isequal(coolers(COST),1567))\r\n%%\r\nCOST = [314 77 818 283 736 557;...\r\n 77 154 767 639 157 274;...\r\n 792 827 375 593 435 133;...\r\n 366 301 190 326 833 700;...\r\n 586 384 647 989 360 486;...\r\n 184 651 4 124 77 183];\r\nassert(isequal(coolers(COST),956))\r\n%%\r\nCOST = [102 248 381 378 851 968;...\r\n 202 438 749 973 257 477;...\r\n 135 670 157 606 286 995;...\r\n 324 548 59 339 780 491;...\r\n 951 610 340 928 702 504;...\r\n 533 864 818 899 493 769];\r\nassert(isequal(coolers(COST),1799))\r\n%%\r\nCOST = [389 799 340 268 52 543;...\r\n 454 221 273 177 628 809;...\r\n 133 858 171 432 30 794;...\r\n 759 905 665 476 137 502;...\r\n 566 293 536 786 695 277;...\r\n 649 726 830 131 516 120];\r\nassert(isequal(coolers(COST),1234))\r\n%%\r\nCOST = [887 183 567 504 84 962;...\r\n 971 32 378 261 74 467;...\r\n 943 725 822 733 770 787;...\r\n 639 145 305 163 818 423;...\r\n 91 636 320 922 741 944;...\r\n 75 790 785 223 759 2];\r\nassert(isequal(coolers(COST),1447))\r\n%%\r\nCOST = [982 666 329 847 415 7;...\r\n 571 685 928 902 662 767;...\r\n 347 793 757 596 784 22;...\r\n 558 349 289 69 248 394;...\r\n 300 251 607 219 555 253;...\r\n 160 346 767 870 230 205];\r\nassert(isequal(coolers(COST),1039))\r\n%%\r\nCOST = [663 975 947 135 372 260;...\r\n 915 120 967 806 227 134;...\r\n 7 519 68 525 447 420;...\r\n 747 822 438 945 267 507;...\r\n 800 638 321 989 460 325;...\r\n 908 954 135 410 433 685];\r\nassert(isequal(coolers(COST),1081))\r\n%%\r\nCOST = [444 502 305 970 394 701;...\r\n 436 556 767 690 459 110;...\r\n 794 631 268 718 209 7;...\r\n 816 98 40 560 758 598;...\r\n 753 246 297 534 547 660;...\r\n 790 616 557 876 358 581];\r\nassert(isequal(coolers(COST),1667))\r\n%%\r\nCOST = [910 165 870 241 503 143;...\r\n 637 281 788 9 568 381;...\r\n 526 260 970 672 189 397;...\r\n 260 548 181 905 325 577;...\r\n 52 542 931 573 717 20;...\r\n 732 789 46 156 553 578];\r\nassert(isequal(coolers(COST),1369))\r\n%%\r\nCOST = [933 303 817 55 540 946;...\r\n 107 460 451 638 938 377;...\r\n 733 49 807 425 661 68;...\r\n 971 386 791 906 395 182;...\r\n 609 362 283 418 259 576;...\r\n 720 288 69 155 848 186];\r\nassert(isequal(coolers(COST),1495))\r\n%%\r\nCOST = [292 28 747 842 871 890;...\r\n 462 659 747 511 212 66;...\r\n 347 159 174 166 837 510;...\r\n 319 803 118 715 860 621;...\r\n 460 409 175 908 524 734;...\r\n 236 328 628 219 478 230];\r\nassert(isequal(coolers(COST),1040))\r\n%%\r\nCOST = [22 327 315 114 208 74;...\r\n 139 138 160 701 785 595;...\r\n 770 385 153 180 527 862;...\r\n 970 563 137 804 572 449;...\r\n 387 634 710 514 423 653;...\r\n 994 542 465 549 722 304];\r\nassert(isequal(coolers(COST),716))\r\n%%\r\nCOST = [608 457 948 406 340 75;...\r\n 279 600 453 439 896 664;...\r\n 800 843 811 679 546 704;...\r\n 797 32 929 466 750 919;...\r\n 955 188 673 954 125 661;...\r\n 445 944 373 355 454 691];\r\nassert(isequal(coolers(COST),2010))\r\n%%\r\nCOST = [854 26 473 324 731 29;...\r\n 468 57 679 802 774 128;...\r\n 459 143 115 300 901 134;...\r\n 807 172 237 776 139 129;...\r\n 825 626 290 553 795 936;...\r\n 191 30 173 555 190 274];\r\nassert(isequal(coolers(COST),1199))\r\n%%\r\nCOST = [943 546 850 417 382 152;...\r\n 639 256 8 518 723 437;...\r\n 873 306 635 887 96 13;...\r\n 368 16 360 150 668 230;...\r\n 237 588 115 435 297 264;...\r\n 188 963 541 60 599 512];\r\nassert(isequal(coolers(COST),627))\r\n%%\r\nCOST = [216 475 261 618 402 95;...\r\n 347 826 590 145 15 376;...\r\n 748 304 480 717 75 546;...\r\n 414 822 199 402 592 112;...\r\n 56 566 240 463 447 905;...\r\n 391 55 781 708 927 634];\r\nassert(isequal(coolers(COST),940))\r\n%%\r\nCOST = [906 61 814 762 771 880;...\r\n 631 674 316 876 876 374;...\r\n 15 478 312 872 68 767;...\r\n 317 306 345 173 647 169;...\r\n 112 517 667 851 325 520;...\r\n 630 708 862 960 641 628];\r\nassert(isequal(coolers(COST),1043))\r\n%%\r\nCOST = [714 628 766 404 544 135;...\r\n 307 193 319 751 411 541;...\r\n 264 777 253 488 901 858;...\r\n 917 865 201 385 57 199;...\r\n 616 334 70 62 444 156;...\r\n 94 136 552 214 538 62];\r\nassert(isequal(coolers(COST),575))\r\n%%\r\nCOST = [662 683 487 77 915 603;...\r\n 19 138 680 445 92 466;...\r\n 292 630 705 166 994 299;...\r\n 974 858 461 399 97 134;...\r\n 765 900 365 921 314 296;...\r\n 244 349 281 512 786 167];\r\nassert(isequal(coolers(COST),1112))\r\n%%\r\nCOST = [318 40 863 414 595 658;...\r\n 110 617 277 415 310 685;...\r\n 833 670 532 984 902 474;...\r\n 972 38 523 58 94 142;...\r\n 219 4 568 397 320 951;...\r\n 707 143 334 792 887 883];\r\nassert(isequal(coolers(COST),1040))\r\n%%\r\nCOST = [438 952 914 477 226 187;...\r\n 835 2 534 251 568 647;...\r\n 326 296 805 308 999 129;...\r\n 368 49 563 967 132 82;...\r\n 795 443 751 209 955 660;...\r\n 100 790 10 521 124 28];\r\nassert(isequal(coolers(COST),914))\r\n%%\r\nCOST = [986 693 226 415 41 625;...\r\n 540 603 797 499 583 296;...\r\n 374 776 997 950 565 75;...\r\n 707 592 282 954 356 294;...\r\n 948 377 711 733 881 235;...\r\n 383 851 665 385 625 346];\r\nassert(isequal(coolers(COST),1955))\r\n%%\r\nCOST = [849 636 448 593 844 465;...\r\n 161 844 327 69 424 31;...\r\n 158 783 280 206 545 435;...\r\n 509 265 932 724 528 558;...\r\n 604 315 400 576 186 639;...\r\n 162 184 380 201 82 35];\r\nassert(isequal(coolers(COST),1044))\r\n%%\r\nCOST = [710 855 367 351 91 968;...\r\n 170 385 350 193 258 434;...\r\n 594 400 631 920 428 785;...\r\n 609 326 665 289 578 526;...\r\n 773 556 993 551 900 332;...\r\n 57 296 945 920 219 432];\r\nassert(isequal(coolers(COST),1623))\r\n%%\r\nCOST = [718 654 235 805 97 557;...\r\n 917 41 201 104 600 840;...\r\n 891 505 381 730 234 205;...\r\n 135 895 595 649 33 622;...\r\n 120 386 269 475 580 175;...\r\n 894 293 623 933 843 290];\r\nassert(isequal(coolers(COST),1365))\r\n%%\r\nCOST = [19 283 206 904 234 347;...\r\n 702 245 435 541 247 298;...\r\n 953 287 143 818 171 405;...\r\n 750 964 376 709 236 303;...\r\n 757 231 794 44 276 758;...\r\n 543 538 813 146 952 360];\r\nassert(isequal(coolers(COST),1417))\r\n%%\r\nCOST = [125 458 201 22 719 780;...\r\n 618 78 960 483 450 568;...\r\n 356 905 666 808 660 77;...\r\n 363 282 542 737 754 252;...\r\n 69 614 869 573 805 134;...\r\n 868 662 558 9 30 565];\r\nassert(isequal(coolers(COST),953))\r\n%%\r\nCOST = [541 43 122 494 863 421;...\r\n 69 528 592 856 685 488;...\r\n 989 257 360 725 635 461;...\r\n 252 409 720 200 142 516;...\r\n 316 948 524 158 80 272;...\r\n 301 920 261 371 877 232];\r\nassert(isequal(coolers(COST),1198))\r\n%%\r\nCOST = [900 895 226 336 740 434;...\r\n 909 88 105 620 889 290;...\r\n 604 540 10 993 860 632;...\r\n 366 429 60 649 598 296;...\r\n 599 618 323 540 655 623;...\r\n 669 559 780 233 916 48];\r\nassert(isequal(coolers(COST),2054))\r\n%%\r\nCOST = [995 67 543 281 171 63;...\r\n 207 928 781 599 372 407;...\r\n 608 88 522 37 40 464;...\r\n 348 333 932 64 710 203;...\r\n 718 527 148 323 642 870;...\r\n 28 247 417 99 175 598];\r\nassert(isequal(coolers(COST),730))\r\n%%\r\nCOST = [24 867 571 301 445 250;...\r\n 900 616 326 522 983 956;...\r\n 453 27 451 562 579 143;...\r\n 59 323 578 242 235 513;...\r\n 107 464 75 913 811 972;...\r\n 999 100 58 826 452 649];\r\nassert(isequal(coolers(COST),902))\r\n%%\r\nCOST = [615 424 874 119 114 462;...\r\n 470 274 301 99 491 864;...\r\n 578 445 401 891 600 263;...\r\n 912 628 518 34 91 824;...\r\n 377 535 62 840 979 329;...\r\n 229 386 232 508 654 942];\r\nassert(isequal(coolers(COST),1065))\r\n%%\r\nCOST = [245 314 524 145 800 565;...\r\n 958 58 893 245 209 218;...\r\n 511 45 406 379 897 858;...\r\n 565 813 605 271 746 862;...\r\n 994 413 95 216 537 314;...\r\n 771 385 336 634 970 327];\r\nassert(isequal(coolers(COST),1381))\r\n%%\r\nCOST = [841 334 286 610 505 816;...\r\n 493 367 657 384 8 910;...\r\n 52 795 232 31 920 778;...\r\n 779 39 622 858 411 758;...\r\n 427 727 76 604 733 406;...\r\n 280 873 967 848 152 702];\r\nassert(isequal(coolers(COST),1140))\r\n%%\r\nCOST = [566 563 904 539 781 176;...\r\n 585 547 605 738 537 417;...\r\n 345 551 927 182 770 740;...\r\n 705 695 117 427 639 893;...\r\n 161 928 341 99 894 26;...\r\n 2 945 37 304 61 138];\r\nassert(isequal(coolers(COST),1153))\r\n%%\r\nCOST = [425 619 192 805 191 892;...\r\n 765 7 715 860 504 674;...\r\n 525 741 178 567 51 686;...\r\n 755 992 987 755 57 696;...\r\n 170 129 17 520 336 800;...\r\n 673 361 40 586 631 661];\r\nassert(isequal(coolers(COST),1579))\r\n%%\r\nCOST = [520 255 928 227 995 523;...\r\n 332 846 588 2 984 274;...\r\n 935 539 102 894 965 719;...\r\n 247 911 367 454 667 779;...\r\n 512 351 276 579 727 82;...\r\n 741 936 267 316 335 222];\r\nassert(isequal(coolers(COST),1598))\r\n%%\r\nCOST = [204 470 675 379 889 587;...\r\n 625 379 552 618 563 969;...\r\n 726 341 52 563 195 582;...\r\n 835 64 308 831 222 100;...\r\n 19 762 966 958 706 167;...\r\n 203 403 932 76 597 103];\r\nassert(isequal(coolers(COST),993))\r\n%%\r\nCOST = [147 800 210 627 782 774;...\r\n 672 400 481 412 936 748;...\r\n 640 756 113 639 740 319;...\r\n 373 296 133 857 254 511;...\r\n 163 641 65 764 720 774;...\r\n 390 886 80 977 694 573];\r\nassert(isequal(coolers(COST),1784))\r\n%%\r\nCOST = [954 463 111 359 365 588;...\r\n 172 130 200 135 400 778;...\r\n 908 550 163 999 927 658;...\r\n 753 970 37 514 496 529;...\r\n 287 443 273 388 610 826;...\r\n 628 591 231 250 5 963];\r\nassert(isequal(coolers(COST),1501))\r\n%%\r\nCOST = [314 306 715 905 664 101;...\r\n 798 19 460 387 603 287;...\r\n 286 163 920 604 657 355;...\r\n 15 444 989 561 310 536;...\r\n 95 767 933 846 332 991;...\r\n 329 682 462 285 189 29];\r\nassert(isequal(coolers(COST),1729))\r\n%%\r\nCOST = [710 876 77 818 359 455;...\r\n 906 865 377 174 364 30;...\r\n 866 356 150 677 268 638;...\r\n 120 632 35 876 338 60;...\r\n 956 865 783 758 87 170;...\r\n 441 21 328 230 452 685];\r\nassert(isequal(coolers(COST),1098))\r\n%%\r\nCOST = [555 461 15 279 896 625;...\r\n 7 371 5 645 888 207;...\r\n 289 825 167 288 394 110;...\r\n 377 538 366 322 676 566;...\r\n 147 818 719 156 253 275;...\r\n 75 461 160 388 950 72];\r\nassert(isequal(coolers(COST),1175))\r\n%%\r\nCOST = [159 896 447 617 948 551;...\r\n 50 608 812 304 334 161;...\r\n 681 15 145 90 390 118;...\r\n 782 345 975 522 151 399;...\r\n 808 534 834 826 334 832;...\r\n 266 628 341 764 554 186];\r\nassert(isequal(coolers(COST),569))\r\n%%\r\nCOST = [501 491 579 185 613 662;...\r\n 127 887 776 198 738 896;...\r\n 865 906 662 862 304 275;...\r\n 767 499 470 126 43 998;...\r\n 565 530 220 646 836 835;...\r\n 390 910 603 438 370 792];\r\nassert(isequal(coolers(COST),1584))\r\n%%\r\nCOST = [657 988 206 794 242 185;...\r\n 544 789 495 682 210 88;...\r\n 387 853 32 651 273 309;...\r\n 823 486 828 238 776 231;...\r\n 596 874 265 478 332 910;...\r\n 782 334 678 937 604 937];\r\nassert(isequal(coolers(COST),1504))\r\n%%\r\nCOST = [32 626 369 811 16 974;...\r\n 594 256 338 724 95 373;...\r\n 44 905 619 675 515 321;...\r\n 425 768 87 914 953 784;...\r\n 522 563 376 821 844 213;...\r\n 841 898 188 549 936 960];\r\nassert(isequal(coolers(COST),1717))\r\n%%\r\nCOST = [931 594 600 844 914 151;...\r\n 366 98 227 771 577 947;...\r\n 345 560 714 510 288 651;...\r\n 693 44 725 707 267 983;...\r\n 717 758 345 792 891 194;...\r\n 801 660 33 331 367 829];\r\nassert(isequal(coolers(COST),1626))\r\n%%\r\nCOST = [494 60 648 497 429 28;...\r\n 591 516 624 763 42 906;...\r\n 45 54 954 180 226 472;...\r\n 565 844 177 692 667 911;...\r\n 634 595 475 915 872 328;...\r\n 499 692 843 163 345 692];\r\nassert(isequal(coolers(COST),526))\r\n%%\r\nCOST = [18 334 151 424 616 681;...\r\n 513 633 839 529 102 394;...\r\n 332 416 727 276 670 392;...\r\n 337 577 109 828 348 207;...\r\n 954 960 162 793 670 725;...\r\n 751 616 298 274 296 820];\r\nassert(isequal(coolers(COST),1421))\r\n%%\r\nCOST = [927 854 101 228 459 109;...\r\n 515 430 413 358 952 652;...\r\n 194 926 925 925 133 147;...\r\n 603 374 56 503 71 694;...\r\n 199 699 385 804 707 626;...\r\n 411 589 241 351 932 44];\r\nassert(isequal(coolers(COST),1345))\r\n%%\r\nCOST = [798 582 93 769 788 680;...\r\n 987 602 334 283 125 10;...\r\n 437 253 652 650 158 129;...\r\n 345 655 504 923 415 764;...\r\n 49 61 531 331 688 842;...\r\n 226 941 380 814 658 747];\r\nassert(isequal(coolers(COST),1350))\r\n%%\r\nCOST = [945 618 338 215 587 443;...\r\n 920 559 552 293 939 692;...\r\n 224 731 744 450 619 760;...\r\n 510 64 413 241 251 400;...\r\n 788 493 508 264 103 568;...\r\n 523 230 129 892 547 206];\r\nassert(isequal(coolers(COST),1251))\r\n%%\r\nCOST = [944 708 393 379 767 434;...\r\n 513 657 243 634 337 103;...\r\n 277 779 282 32 626 237;...\r\n 230 581 905 921 327 972;...\r\n 953 504 703 470 312 699;...\r\n 690 879 854 520 647 660];\r\nassert(isequal(coolers(COST),1565))\r\n%%\r\nCOST = [327 450 172 573 377 40;...\r\n 20 769 722 832 394 565;...\r\n 571 628 90 348 429 50;...\r\n 37 315 918 780 424 873;...\r\n 265 293 430 709 219 861;...\r\n 191 79 80 591 497 339];\r\nassert(isequal(coolers(COST),1224))\r\n%%\r\nCOST = [975 347 346 214 761 636;...\r\n 955 234 903 936 580 803;...\r\n 989 756 13 970 622 670;...\r\n 158 226 133 376 490 472;...\r\n 3 449 389 985 221 945;...\r\n 249 260 841 123 145 592];\r\nassert(isequal(coolers(COST),1311))\r\n%%\r\nCOST = [227 932 883 581 327 592;...\r\n 684 697 780 627 288 1;...\r\n 321 96 19 112 497 904;...\r\n 749 246 771 214 182 683;...\r\n 999 935 686 37 934 74;...\r\n 270 480 868 445 941 997];\r\nassert(isequal(coolers(COST),804))\r\n%%\r\nCOST = [315 176 835 189 254 788;...\r\n 810 588 997 578 287 438;...\r\n 462 155 973 629 126 551;...\r\n 588 446 746 365 529 987;...\r\n 478 538 200 637 821 690;...\r\n 553 462 37 892 300 971];\r\nassert(isequal(coolers(COST),2037))\r\n%%\r\nCOST = [96 320 833 203 939 1;...\r\n 899 796 571 493 975 484;...\r\n 59 302 71 730 608 148;...\r\n 124 608 267 955 958 315;...\r\n 631 462 171 763 331 172;...\r\n 371 293 561 81 902 593];\r\nassert(isequal(coolers(COST),1341))\r\n%%\r\nCOST = [658 16 262 188 656 56;...\r\n 349 974 894 744 782 820;...\r\n 54 735 973 719 863 107;...\r\n 48 753 995 189 786 7;...\r\n 56 34 347 434 434 276;...\r\n 72 118 293 46 348 943];\r\nassert(isequal(coolers(COST),862))\r\n%%\r\nCOST = [675 962 293 837 855 788;...\r\n 386 823 439 42 174 175;...\r\n 857 11 399 424 536 720;...\r\n 675 142 138 76 295 233;...\r\n 609 483 953 314 903 208;...\r\n 440 581 361 946 82 217];\r\nassert(isequal(coolers(COST),1114))\r\n%%\r\nCOST = [977 461 237 907 842 269;...\r\n 382 4 832 993 684 981;...\r\n 636 465 438 391 172 313;...\r\n 47 825 508 827 238 572;...\r\n 233 872 186 955 435 612;...\r\n 875 302 152 894 26 902];\r\nassert(isequal(coolers(COST),1700))\r\n%%\r\nCOST = [397 297 634 847 995 269;...\r\n 693 572 965 41 429 758;...\r\n 205 317 201 330 268 610;...\r\n 63 810 599 710 473 967;...\r\n 623 366 84 861 161 774;...\r\n 689 699 545 501 159 573];\r\nassert(isequal(coolers(COST),1320))\r\n%%\r\nCOST = [451 355 996 87 199 597;...\r\n 288 581 84 520 657 323;...\r\n 753 299 469 553 699 831;...\r\n 95 714 94 686 460 123;...\r\n 107 361 726 300 158 252;...\r\n 735 719 913 602 422 938];\r\nassert(isequal(coolers(COST),1069))\r\n\r\n","published":true,"deleted":false,"likes_count":7,"comments_count":2,"created_by":255320,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":56,"test_suite_updated_at":"2020-04-03T23:22:52.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-01T22:04:29.000Z","updated_at":"2026-02-26T16:11:43.000Z","published_at":"2020-04-01T22:55:22.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn a certain chemical plant, 6 new pieces of cooling equipment (coolers) are to be installed in a vacant space. This vacant space was divided into a grid of 6 cells by 6 cells. Your task is to assign the 6 coolers to 6 cells in this grid with the following requirements:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThere must be one cooler for every column in the grid.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you decide to install a cooler at row R, column C, then the cooler at column C+1 must be installed either on row R-1, R, or R+1 only. This is done to ease the connection of coolers by piping.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe total installation cost for the 6 coolers must be minimum.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this problem, you are given a cost matrix (COST) in relation to the third requirement above. COST is a 6 x 6 matrix where the\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er,c\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e-th element is the cost of assigning any cooler to row\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003er\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, column\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003ec\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e (All the coolers are identical). Write a function that accepts the matrix COST, and output the value of the minimum cost of installation. You are ensured that all elements of COST are integers in the range [1,1000].\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn the sample test case below, the optimal placement is at the following rows: 4,3,3,2,2,2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e COST = [695 766 710 119 752 548;\\n 318 796 755 499 256 139;\\n 951 187 277 960 506 150;\\n 35 490 680 341 700 258;\\n 439 446 656 586 891 841;\\n 382 647 163 224 960 255];\\n\u003e\u003e coolers(COST)\\nans = \\n 1393]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMeanwhile, the optimal placement for the case below is at rows: 5,6,5,6,5,6\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\u003e\u003e COST = [815 617 918 76 569 312;\\n 244 474 286 54 470 529;\\n 930 352 758 531 455 988;\\n 350 831 754 780 12 602;\\n 197 586 381 935 163 263;\\n 252 550 568 130 795 100];\\n\u003e\u003e coolers(COST)\\nans = \\n 1521]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45321,"title":"Kolakoski Sequence","description":"This is a modified version of the kolakoski sequence. \r\n\r\n\r\nRefer to the problem \u003chttps://www.mathworks.com/matlabcentral/cody/problems/2371\u003e for the original one.\r\n\r\nfor this case,\r\n\r\n\r\nGiven a particular initial vector a and length x,\r\ngenerate the kolakoski sequence (first x terms) from that vector.\r\n\r\n\r\nFor example if a=[2,1] and x=10 ; the sequence would look like --\r\n\r\n\r\n 2 2 1 1 2 1 2 2 1 2 2","description_html":"\u003cp\u003eThis is a modified version of the kolakoski sequence.\u003c/p\u003e\u003cp\u003eRefer to the problem \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/2371\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/2371\u003c/a\u003e for the original one.\u003c/p\u003e\u003cp\u003efor this case,\u003c/p\u003e\u003cp\u003eGiven a particular initial vector a and length x,\r\ngenerate the kolakoski sequence (first x terms) from that vector.\u003c/p\u003e\u003cp\u003eFor example if a=[2,1] and x=10 ; the sequence would look like --\u003c/p\u003e\u003cpre\u003e 2 2 1 1 2 1 2 2 1 2 2\u003c/pre\u003e","function_template":"function y = kolakoski_seq_3(a,x)\r\n y = x;\r\nend","test_suite":"%%\r\na = [2,1];\r\nx=10;\r\ny_correct = [2 2 1 1 2 1 2 2 1 2 ];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [2,1];\r\nx=15;\r\ny_correct = [2 2 1 1 2 1 2 2 1 2 2 1 1 2 1 ];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [1,3,1,2];\r\nx=30;\r\ny_correct = [1, 3, 3, 3, 1, 1, 1, 2, 2, 2, 1, 3, 1, 2, 2, 1, 1, 3, 3, 1, 2, 2, 2, 1, 3, 3, 1, 1, 2, 1];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [4,2];\r\nx=15;\r\ny_correct = [ 4 4 4 4 2 2 2 2 4 4 4 4 2 2 2];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [1,3,2];\r\nx=15;\r\ny_correct = [1 3 3 3 2 2 2 1 1 1 3 3 2 2 1];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [1,3,2,1];\r\nx=30;\r\ny_correct = [1, 3, 3, 3, 2, 2, 2, 1, 1, 1, 1, 1, 3, 3, 2, 2, 1, 1, 3, 2, 1, 1, 1, 1, 3, 3, 3, 2, 2, 1];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))\r\n%%\r\na = [3,12,1,5];\r\nx=40;\r\ny_correct = [3 3 3 12 12 12 1 1 1 5 5 5 5 5 5 5 5 5 5 5 5 3 3 3 3 3 3 3 3 3 3 3 3 12 12 12 12 12 12 12];\r\nassert(isequal( kolakoski_seq_3(a,x),y_correct))","published":true,"deleted":false,"likes_count":2,"comments_count":5,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-02-11T22:04:19.000Z","updated_at":"2026-02-10T02:18:16.000Z","published_at":"2020-02-11T22:17:59.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis is a modified version of the kolakoski sequence.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRefer to the problem\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/2371\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/2371\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt; for the original one.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003efor this case,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a particular initial vector a and length x, generate the kolakoski sequence (first x terms) from that vector.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example if a=[2,1] and x=10 ; the sequence would look like --\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 2 2 1 1 2 1 2 2 1 2 2]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2126,"title":"Split bread like the Pharaohs - Egyptian fractions and greedy algorithm","description":"How would you split 5 loaves of bread among 8 people in all fairness? Get a hint from the Pharaohs. 5/8 = 4/8 + 1/8 , i.e. each receives 1/2 of loaf plus 1/8 - splitting 4 loaves into 1/2 and then the last loaf into 8 pieces.\r\nEgyptian fraction is writing any rational number p/q in terms of unity fractions;\r\ne.g. \r\n\r\n 3/4 = 1/2 + 1/4, OR \r\n = 1/3 + 1/4 + 1/6\r\n\r\n 13/48 = 1/4 + 1/48\r\nWrite a program to return the Egyptian fraction of a given rational number p, q. You outputs in the above cases should be the series of denominator values,\r\n i.e. egyptian_fraction( 13, 48) should return [4,48],\r\nYou can use simple greedy algorithm or alternatives.\r\nReferences\r\nhttp://en.wikipedia.org/wiki/Egyptian_fraction\r\nAMS blog post by Tyler Clark (@tylermath12) http://blogs.ams.org/jmm2014/2014/01/17/friday-morning-math-fun/\r\nBonus points if you can enumerate all possible Egyptian fractions of (p,q), but thats a problem for another day.\r\nP.S: Updated test suite to check for proper solutions only","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 460.333px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 230.167px; transform-origin: 407px 230.167px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379.5px 8px; transform-origin: 379.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eHow would you split 5 loaves of bread among 8 people in all fairness? Get a hint from the Pharaohs. 5/8 = 4/8 + 1/8 , i.e. each receives 1/2 of loaf plus 1/8 - splitting 4 loaves into 1/2 and then the last loaf into 8 pieces.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 242px 8px; transform-origin: 242px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eEgyptian fraction is writing any rational number p/q in terms of unity fractions;\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 122.6px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 61.3px; transform-origin: 404px 61.3px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 24px 8.5px; tab-size: 4; transform-origin: 24px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003ee.g. \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 112px 8.5px; tab-size: 4; transform-origin: 112px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 3/4 = 1/2 + 1/4, OR \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 116px 8.5px; tab-size: 4; transform-origin: 116px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e = 1/3 + 1/4 + 1/6\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 8.5px; tab-size: 4; transform-origin: 0px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 96px 8.5px; tab-size: 4; transform-origin: 96px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e 13/48 = 1/4 + 1/48\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 383.5px 8px; transform-origin: 383.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eWrite a program to return the Egyptian fraction of a given rational number p, q. You outputs in the above cases should be the series of denominator values,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 236px 8.5px; tab-size: 4; transform-origin: 236px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 180px 8.5px; transform-origin: 180px 8.5px; \"\u003e i.e. egyptian_fraction( 13, 48) should \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 52px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 52px 8.5px; \"\u003ereturn [4,48]\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e,\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 165px 8px; transform-origin: 165px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eYou can use simple greedy algorithm or alternatives.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 41px 8px; transform-origin: 41px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-weight: 700; \"\u003eReferences\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003col style=\"block-size: 61.3px; counter-reset: list-item 0; font-family: Helvetica, Arial, sans-serif; list-style-type: decimal; margin-block-end: 20px; margin-block-start: 10px; margin-bottom: 20px; margin-top: 10px; perspective-origin: 391px 30.65px; transform-origin: 391px 30.65px; margin-top: 10px; margin-bottom: 20px; \"\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ehttp://en.wikipedia.org/wiki/Egyptian_fraction\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 142px 8px; transform-origin: 142px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAMS blog post by Tyler Clark (@tylermath12)\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"/#null\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ehttp://blogs.ams.org/jmm2014/2014/01/17/friday-morning-math-fun/\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003c/li\u003e\u003cli style=\"block-size: 20.4333px; counter-reset: none; display: list-item; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-start: 56px; margin-left: 56px; margin-top: 0px; perspective-origin: 363px 10.2167px; text-align: left; transform-origin: 363px 10.2167px; white-space: pre-wrap; margin-left: 56px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-inline-start: 0px; margin-left: 0px; perspective-origin: 347px 8px; transform-origin: 347px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eBonus points if you can enumerate all possible Egyptian fractions of (p,q), but thats a problem for another day.\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 178px 8px; transform-origin: 178px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eP.S: Updated test suite to check for proper solutions only\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function denoms = egyptian_fraction(p,q)\r\n denoms = [];\r\n \r\n if ( p == 1 )\r\n denoms(end+1) = q;\r\n return;\r\n end\r\n % treat other cases..\r\nend","test_suite":"%%\r\n% Updated test suite to remove trivial solutions;\r\n\r\n%%\r\n Vmin = 13; Vmax = 48;\r\n arr = egyptian_fraction(Vmin,Vmax);\r\n assert(isequal(arr, [4 48]))\r\n \r\n%%\r\n Vmin = 3; Vmax = 4;\r\n arr = egyptian_fraction(Vmin,Vmax);\r\n assert(isequal(arr, [2 4]) || isequal(arr, [3 4 6]))\r\n \r\n%%\r\n Vmin = 2;\r\n in = primes(20);\r\n Vmax = in(randi(numel(in)));\r\n arr = egyptian_fraction(Vmin,Vmax);\r\n l=lcm(arr(1),arr(2));\r\n for k=3:numel(arr)\r\n l=lcm(l,arr(k));\r\n end\r\n assert(isequal(sum(arr),sum(l./arr)/(Vmax/l)))\r\n \r\n \r\n%%\r\n% Small\r\n Vmin = 10; Vmax = 55;\r\n denom = floor(unique(egyptian_fraction(Vmin,Vmax)));\r\n\r\n egyptian_value = sum(1./denom);\r\n\r\n rel_tol = Vmin/Vmax*1e-6;\r\n actual_error = abs( egyptian_value - Vmin/Vmax );\r\n assert(isequal(actual_error \u003c rel_tol ,true))\r\n\r\n%%\r\n% Pie\r\n Vmin = 113; Vmax = 355;\r\n denom = floor(unique(egyptian_fraction(Vmin,Vmax)));\r\n\r\n egyptian_value = sum(1./denom);\r\n\r\n rel_tol = Vmin/Vmax*1e-6;\r\n actual_error = abs( egyptian_value - Vmin/Vmax );\r\n assert(isequal(actual_error \u003c rel_tol ,true))\r\n\r\n%%\r\n% Ramanujan\r\n Vmin = 1023; Vmax = 1729;\r\n denom = floor(unique(egyptian_fraction(Vmin,Vmax)));\r\n\r\n egyptian_value = sum(1./denom);\r\n\r\n rel_tol = Vmin/Vmax*1e-6;\r\n actual_error = abs( egyptian_value - Vmin/Vmax );\r\n assert(isequal(actual_error \u003c rel_tol ,true))\r\n\r\n%%\r\n% E\r\n Vmin = 27; Vmax = 183;\r\n denom = floor(unique(egyptian_fraction(Vmin,Vmax)));\r\n egyptian_value = sum(1./denom);\r\n\r\n rel_tol = Vmin/Vmax*1e-6;\r\n actual_error = abs( egyptian_value - Vmin/Vmax );\r\n assert(isequal(actual_error \u003c rel_tol ,true))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":5,"created_by":3378,"edited_by":223089,"edited_at":"2023-03-29T14:08:28.000Z","deleted_by":null,"deleted_at":null,"solvers_count":96,"test_suite_updated_at":"2023-03-29T14:08:28.000Z","rescore_all_solutions":false,"group_id":38,"created_at":"2014-01-19T06:15:39.000Z","updated_at":"2026-03-31T17:38:29.000Z","published_at":"2014-01-19T17:44:14.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHow would you split 5 loaves of bread among 8 people in all fairness? Get a hint from the Pharaohs. 5/8 = 4/8 + 1/8 , i.e. each receives 1/2 of loaf plus 1/8 - splitting 4 loaves into 1/2 and then the last loaf into 8 pieces.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eEgyptian fraction is writing any rational number p/q in terms of unity fractions;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[e.g. \\n\\n 3/4 = 1/2 + 1/4, OR \\n = 1/3 + 1/4 + 1/6\\n\\n 13/48 = 1/4 + 1/48]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eWrite a program to return the Egyptian fraction of a given rational number p, q. You outputs in the above cases should be the series of denominator values,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ i.e. egyptian_fraction( 13, 48) should return [4,48],]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can use simple greedy algorithm or alternatives.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eReferences\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://en.wikipedia.org/wiki/Egyptian_fraction\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAMS blog post by Tyler Clark (@tylermath12)\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://blogs.ams.org/jmm2014/2014/01/17/friday-morning-math-fun/\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"ListParagraph\\\"/\u003e\u003cw:numPr\u003e\u003cw:numId w:val=\\\"2\\\"/\u003e\u003c/w:numPr\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBonus points if you can enumerate all possible Egyptian fractions of (p,q), but thats a problem for another day.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eP.S: Updated test suite to check for proper solutions only\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45463,"title":"Word Ladder","description":"Given a set of words, and two other words - start and destination,\r\n\r\nFind the smallest chain from start to the destination such that adjacent words in the chain only differ by one character and each word in the chain exists in the set. \r\n\r\nAll the words are of the same length.\r\nThe starting word is not in the set but destination word would be.\r\n\r\nFor example, \r\n\r\n Start = 'COLD'\r\n Destination = 'WARM'\r\n set ={ CORD CARD DART FORT WARM FARM WARD}\r\n\r\n COLD → CORD → CARD → WARD → WARM","description_html":"\u003cp\u003eGiven a set of words, and two other words - start and destination,\u003c/p\u003e\u003cp\u003eFind the smallest chain from start to the destination such that adjacent words in the chain only differ by one character and each word in the chain exists in the set.\u003c/p\u003e\u003cp\u003eAll the words are of the same length.\r\nThe starting word is not in the set but destination word would be.\u003c/p\u003e\u003cp\u003eFor example,\u003c/p\u003e\u003cpre\u003e Start = 'COLD'\r\n Destination = 'WARM'\r\n set ={ CORD CARD DART FORT WARM FARM WARD}\u003c/pre\u003e\u003cpre\u003e COLD → CORD → CARD → WARD → WARM\u003c/pre\u003e","function_template":"function out2 = word_lad_2(ary,st,des)","test_suite":"%%\r\nary={ 'CORD', 'CARD', 'DART', 'FORT', 'WARM', 'FARM', 'WARD'}\r\nst='COLD'\r\ndes='WARM'\r\ny_correct = {'CORD','CARD','WARD','WARM'};\r\nassert(isequal(word_lad_2(ary,st,des),y_correct))\r\n\r\n%%\r\nary={'pan','can','fan','pat','mat','fat','lot','opt','apt','act','ape','put','aut'}\r\nst='man'\r\ndes='ape'\r\ny_correct = {'pan'\t'pat'\t'put'\t'aut'\t'apt'\t'ape'};\r\nassert(isequal(word_lad_2(ary,st,des),y_correct))\r\n\r\n%%\r\nary={'leg','hot','dot','dog','lot','log','cog','hog','zog','fog'}\r\nst='hit'\r\ndes='fog'\r\ny_correct = {'hot','hog','fog'};\r\nassert(isequal(word_lad_2(ary,st,des),y_correct))\r\n\r\n%%\r\nary={'safer', 'rifer','upper','rifre', 'rider','tider' ,'cider','coder','loder', 'cooer', 'cooey', 'gooey', 'goosy' , 'goose' ,'loose','nosey','doose'}\r\nst='refer'\r\ndes='loose'\r\ny_correct = {'rifer'\t'rider'\t'cider'\t'coder'\t'cooer'\t'cooey'\t'gooey'\t'goosy'\t'goose'\t'loose'};\r\nassert(isequal(word_lad_2(ary,st,des),y_correct))\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":4,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2020-04-16T07:12:17.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-16T07:11:43.000Z","updated_at":"2026-02-10T01:03:52.000Z","published_at":"2020-04-16T07:12:17.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a set of words, and two other words - start and destination,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the smallest chain from start to the destination such that adjacent words in the chain only differ by one character and each word in the chain exists in the set.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAll the words are of the same length. The starting word is not in the set but destination word would be.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Start = 'COLD'\\n Destination = 'WARM'\\n set ={ CORD CARD DART FORT WARM FARM WARD}\\n\\n COLD → CORD → CARD → WARD → WARM]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45473,"title":"Sub-sequence - 01","description":"Find the length of the longest increasing subsequence in the given array.\r\n\r\n a=[2,4,2,1,3,5,6]\r\n longest increasing subsequence = [2,4,5,6]\r\n = [1,3,5,6]\r\n = [2,3,5,6]\r\n so, length = 4","description_html":"\u003cp\u003eFind the length of the longest increasing subsequence in the given array.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ea=[2,4,2,1,3,5,6]\r\nlongest increasing subsequence = [2,4,5,6]\r\n = [1,3,5,6]\r\n = [2,3,5,6]\r\nso, length = 4\r\n\u003c/pre\u003e","function_template":"function yy = longest_sub_2(x)","test_suite":"%%\r\nx =[2,4,2,1,3,5,6];\r\nassert(isequal(longest_sub_2(x),4))\r\n\r\n%%\r\nx=[15,27,14,38,26,55,46,65,85];\r\nassert(isequal(longest_sub_2(x),6))\r\n\r\n%%\r\nx =[2,4,2,1,3,5,6,12,0,2,1,1,2,1,4,5,6,12,3,2];\r\nassert(isequal(longest_sub_2(x),7))\r\n\r\n%%\r\nx =[82\t91\t13\t92\t64\t10\t28\t55\t96\t97\t16\t98\t96\t49\t81\t15\t43\t92\t80\t96\t66\t4\t85\t94\t68];\r\nassert(isequal(longest_sub_2(x),6))\r\n\r\n%%\r\nx =[758\t744\t393\t656\t172\t707\t32\t277\t47\t98\t824\t695\t318\t951\t35\t439\t382\t766\t796\t187\t490\t446\t647\t710\t755\t277\t680\t656\t163\t119\t499\t960\t341\t586\t224\t752\t256\t506\t700\t891\t960\t548\t139\t150\t258\t841\t255\t815\t244\t930];\r\nassert(isequal(longest_sub_2(x),11))\r\n\r\n%%\r\nx =ones(1,1000);\r\nassert(isequal(longest_sub_2(x),1))\r\n\r\n%%\r\nx =[ones(1,1000),zeros(1,50),2*ones(1,20)];\r\nassert(isequal(longest_sub_2(x),2))\r\n\r\n%%\r\nx=[34999 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9\t48268\t37602\t52379\t26488\t6836\t43633\t17386\t2611\t95468\t43060\t96156\t76242\t735\t68004\t70596\t64513\t55231\t21811\t77237\t22803\t37087\t89093\t85638\t40244\t31802\t60864\t91020\t90910\t59160\t33258\t85307\t44240\t90436\t3318\t53243\t71650\t17931\t33654\t18772\t32193\t40386\t54857\t4874\t55274\t27482\t24151\t24315\t15416\t95642];\r\nassert(isequal(longest_sub_2(x),72))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-23T15:37:38.000Z","updated_at":"2026-02-10T06:04:32.000Z","published_at":"2020-04-23T15:37:38.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFind the length of the longest increasing subsequence in the given array.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[a=[2,4,2,1,3,5,6]\\nlongest increasing subsequence = [2,4,5,6]\\n = [1,3,5,6]\\n = [2,3,5,6]\\nso, length = 4]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":55305,"title":"Chain multiplication - 02","description":"Following up on the problem in 55295, you found the number of multiplications needed to multiply two matrices.\r\nNow, you are given a sequence of matrices. There are many different ways you can multiply the matrices. For example, \r\nsay, you are given 4 matrix - A, B, C, D. They can be multiplied as follows - A(B(CD)), A((BC)D), ((AB)C)D, (AB)(CD), (A(BC))D.\r\n\r\nyou have to figure out which is the optimal way of multiplying those matrices based on the mininum number of multiplications required. For example, consider a simple 3 matrix case.\r\nA(1,2), B(2,3), C(3,2)\r\nA(BC) =\u003e BC requires 12 multiplications; multiplying A matrix with the result requires 4 multiplications. Total = 12+4= 16.\r\n(AB)C =\u003e AB requires 6 multiplications; multiplying the result with the C matrix requires 6 multiplications. Total = 6+6= 12.\r\nTherefore, to multiply ABC - the optimal way is (AB)C requiring 12 multiplications in total.\r\n\r\nHere, you will be given an array 'a' containing the size of consequtive matrices. The output is the minimum number of multiplications required to multiply those matrices.\r\nhere, a = [2, 4, 6, 1] represents 3 matrices -- A(2,4), B(4,6), and C(6,1)","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 414px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 207px; transform-origin: 407px 207px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eFollowing up on the problem in \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://www.mathworks.com/matlabcentral/cody/problems/55295-chain-multiplication-01\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003e55295\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e, you found the number of multiplications needed to multiply two matrices.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eNow, you are given a sequence of matrices. There are many different ways you can multiply the matrices. For example, \u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003esay, you are given 4 matrix - A, B, C, D. They can be multiplied as follows - A(B(CD)), A((BC)D), ((AB)C)D, (AB)(CD), (A(BC))D.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eyou have to figure out which is the optimal way of multiplying those matrices based on the mininum number of multiplications required. For example, consider a simple 3 matrix case.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA(1,2), B(2,3), C(3,2)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eA(BC) =\u0026gt; BC requires 12 multiplications; multiplying A matrix with the result requires 4 multiplications. Total = 12+4= 16.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e(AB)C =\u0026gt; AB requires 6 multiplications; multiplying the result with the C matrix requires 6 multiplications. Total = 6+6= 12.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eTherefore, to multiply ABC - the optimal way is (AB)C requiring 12 multiplications in total.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003eHere, you will be given an array 'a' containing the size of consequtive matrices. The output is the minimum number of multiplications required to multiply those matrices.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; \"\u003e\u003cspan style=\"\"\u003ehere, a = [2, 4, 6, 1] represents 3 matrices -- A(2,4), B(4,6), and C(6,1)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function y = chain_mul_02(a)\r\n y = x;\r\nend","test_suite":"%%\r\na=[1,2,3,2];\r\ny_correct = 12;\r\nassert(isequal(chain_mul_02(a),y_correct))\r\n\r\n%%\r\na=[4,10,3,12,20,7];\r\ny_correct = 1344;\r\nassert(isequal(chain_mul_02(a),y_correct))\r\n\r\n\r\n%%\r\na=[1,2,3,4];\r\ny_correct = 18;\r\nassert(isequal(chain_mul_02(a),y_correct))\r\n\r\n\r\n%%\r\na=[81,213,78,96,2,1,98,102, 1200,4];\r\ny_correct = 179067;\r\nassert(isequal(chain_mul_02(a),y_correct))\r\n\r\n%%\r\na=[40, 20, 30, 10, 30];\r\ny_correct = 26000;\r\nassert(isequal(chain_mul_02(a),y_correct))\r\n\r\n%%\r\na=[7,1,5,4,2];\r\ny_correct = 42;\r\nassert(isequal(chain_mul_02(a),y_correct))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":363598,"edited_at":"2022-08-14T21:58:45.000Z","deleted_by":null,"deleted_at":null,"solvers_count":21,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2022-08-14T20:23:49.000Z","updated_at":"2026-02-10T20:51:38.000Z","published_at":"2022-08-14T21:45:21.000Z","restored_at":null,"restored_by":null,"spam":null,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFollowing up on the problem in \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/55295-chain-multiplication-01\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e55295\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e, you found the number of multiplications needed to multiply two matrices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow, you are given a sequence of matrices. There are many different ways you can multiply the matrices. For example, \u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003esay, you are given 4 matrix - A, B, C, D. They can be multiplied as follows - A(B(CD)), A((BC)D), ((AB)C)D, (AB)(CD), (A(BC))D.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eyou have to figure out which is the optimal way of multiplying those matrices based on the mininum number of multiplications required. For example, consider a simple 3 matrix case.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(1,2), B(2,3), C(3,2)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA(BC) =\u0026gt; BC requires 12 multiplications; multiplying A matrix with the result requires 4 multiplications. Total = 12+4= 16.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e(AB)C =\u0026gt; AB requires 6 multiplications; multiplying the result with the C matrix requires 6 multiplications. Total = 6+6= 12.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTherefore, to multiply ABC - the optimal way is (AB)C requiring 12 multiplications in total.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, you will be given an array 'a' containing the size of consequtive matrices. The output is the minimum number of multiplications required to multiply those matrices.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ehere, a = [2, 4, 6, 1] represents 3 matrices -- A(2,4), B(4,6), and C(6,1)\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":45352,"title":"Number generator","description":"In this problem, a number will be given.\r\n\r\nstarting with the first digit, keep on adding all subsequent digits till the state (even or odd) of the sum of the digits is opposite to the state of the first digit. \r\n\r\nContinue this way till the last digit of the number is reached. \r\n\r\nConcatenate all the sums to generate the new number.\r\n\r\n for example, n=74561. \r\n we start with 7(odd); 7+4==11(odd); 11+5=16(even); y=16\r\n remaining 6(even); 6+1=7(odd); y=167\r\n","description_html":"\u003cp\u003eIn this problem, a number will be given.\u003c/p\u003e\u003cp\u003estarting with the first digit, keep on adding all subsequent digits till the state (even or odd) of the sum of the digits is opposite to the state of the first digit.\u003c/p\u003e\u003cp\u003eContinue this way till the last digit of the number is reached.\u003c/p\u003e\u003cp\u003eConcatenate all the sums to generate the new number.\u003c/p\u003e\u003cpre\u003e for example, n=74561. \r\n we start with 7(odd); 7+4==11(odd); 11+5=16(even); y=16\r\n remaining 6(even); 6+1=7(odd); y=167\u003c/pre\u003e","function_template":"function y=number_gen(x)\r\n y = x;\r\nend","test_suite":"%%\r\nassert(isequal(number_gen(74562),168))\r\n%%\r\nassert(isequal(number_gen(74561562),16713))\r\n%%\r\nassert(isequal(number_gen(111111111111),222222))\r\n%%\r\nassert(isequal(number_gen(1111111111111),2222221))\r\n%%\r\nassert(isequal(number_gen(3456696524532), 122111115))\r\n%%\r\nassert(isequal(number_gen(100000024008),15))\r\n%%\r\nassert(isequal(number_gen(288624888862),70))\r\n","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":14,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-02-22T13:23:25.000Z","updated_at":"2025-12-04T12:36:29.000Z","published_at":"2020-02-22T16:12:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIn this problem, a number will be given.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003estarting with the first digit, keep on adding all subsequent digits till the state (even or odd) of the sum of the digits is opposite to the state of the first digit.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eContinue this way till the last digit of the number is reached.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eConcatenate all the sums to generate the new number.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ for example, n=74561. \\n we start with 7(odd); 7+4==11(odd); 11+5=16(even); y=16\\n remaining 6(even); 6+1=7(odd); y=167]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45428,"title":"Minimal Path - 01","description":"Given a matrix, find the minimal path sum from the top left to the bottom right by only moving to the right and down.\r\n\r\nFor example,\r\n \r\n x=[ 2 2 2 2 2\r\n 10 10 10 1 2\r\n 20 20 20 1 2\r\n 30 30 30 30 2]\r\n\r\nHere, the minimum path sum is 14 [ 2+2+2+2+1+1+2+2 ]","description_html":"\u003cp\u003eGiven a matrix, find the minimal path sum from the top left to the bottom right by only moving to the right and down.\u003c/p\u003e\u003cp\u003eFor example,\u003c/p\u003e\u003cpre\u003e x=[ 2 2 2 2 2\r\n 10 10 10 1 2\r\n 20 20 20 1 2\r\n 30 30 30 30 2]\u003c/pre\u003e\u003cp\u003eHere, the minimum path sum is 14 [ 2+2+2+2+1+1+2+2 ]\u003c/p\u003e","function_template":"function y = minimal_path(x)","test_suite":"%%\r\nx = [2 2 2 2 2\r\n 10 10 10 1 2\r\n 20 20 20 1 2\r\n 30 30 30 30 2];\r\nassert(isequal(minimal_path(x),14))\r\n\r\n%%\r\nx = [2 2 2 2 2\r\n 0 0 10 1 2\r\n 20 0 20 1 2\r\n 30 0 0 3 2];\r\nassert(isequal(minimal_path(x),7))\r\n\r\n%%\r\nx = [100 20 30 40 50\r\n 60 70 80 90 100];\r\nassert(isequal(minimal_path(x),340))\r\n\r\n%%\r\nx = [11 111 23 45 67 -500 34 23\r\n 22 32 432 1234 12 1244 -544 44\r\n 1 2 3 4 5 6 7 8\r\n -12000 45 6 7 8 433 664 2344];\r\nassert(isequal(minimal_path(x),-8459))","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-08T09:32:44.000Z","updated_at":"2026-01-06T08:45:42.000Z","published_at":"2020-04-08T09:32:44.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a matrix, find the minimal path sum from the top left to the bottom right by only moving to the right and down.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ x=[ 2 2 2 2 2\\n 10 10 10 1 2\\n 20 20 20 1 2\\n 30 30 30 30 2]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHere, the minimum path sum is 14 [ 2+2+2+2+1+1+2+2 ]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":795,"title":"Joining Ranges","description":"You are given a n-by-2 matrix. Each row represents a numeric range, e.g.\r\n\r\n x = [0 5; 10 3; 20 15; 16 19; 25 25]\r\n \r\ncontains ranges [0 5], [10 3], [20 15], [16 19], and [25 25]. Note that the first column does not always contain the smaller number. Join all overlapping ranges and return the sorted (both columns and all rows must be sorted) matrix of joined ranges\r\n\r\n y = [0 10; 15 20; 25 25]\r\n\r\ni.e. ranges [0 5] and [10 3] are combined to [0 10], range [16 19] is completely overlapped by [15 20] and [25 25] is kept because it is a separate range.","description_html":"\u003cp\u003eYou are given a n-by-2 matrix. Each row represents a numeric range, e.g.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ex = [0 5; 10 3; 20 15; 16 19; 25 25]\r\n\u003c/pre\u003e\u003cp\u003econtains ranges [0 5], [10 3], [20 15], [16 19], and [25 25]. Note that the first column does not always contain the smaller number. Join all overlapping ranges and return the sorted (both columns and all rows must be sorted) matrix of joined ranges\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ey = [0 10; 15 20; 25 25]\r\n\u003c/pre\u003e\u003cp\u003ei.e. ranges [0 5] and [10 3] are combined to [0 10], range [16 19] is completely overlapped by [15 20] and [25 25] is kept because it is a separate range.\u003c/p\u003e","function_template":"function y = joinRanges(x)\r\n y = x;\r\nend","test_suite":"%%\r\nx = [0 5; 10 3; 20 15; 16 19; 25 25];\r\ny_correct = [0 10;15 20;25 25];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n%%\r\nx = [-10 -5; 0 -8; -1 5]; \r\ny_correct = [-10 5];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n%%\r\nx = [-50 0; 0 50; 100 50; -50 -100]; \r\ny_correct = [-100 100];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n\r\n%%\r\nx = [99 51; -49 -1; -51 -99; 1 49]; \r\ny_correct = [-99 -51;-49 -1;1 49;51 99];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n\r\n%%\r\nx = [-inf inf]; \r\ny_correct = x;\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n%%\r\nx = [0 -42; -inf -10; inf 42]; \r\ny_correct = [-Inf 0;42 Inf];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n%%\r\nx = [36.154 63.178; 12.007 -5.156; -0.519 17.651]; \r\ny_correct = [-5.156 17.651;36.154 63.178];\r\nassert(isequal(joinRanges(x),y_correct))\r\n\r\n%%\r\nassert(isempty(strfind(evalc('type joinRanges'), 'regexp')));","published":true,"deleted":false,"likes_count":9,"comments_count":3,"created_by":4976,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":400,"test_suite_updated_at":"2013-10-20T11:57:08.000Z","rescore_all_solutions":false,"group_id":12,"created_at":"2012-06-27T16:04:34.000Z","updated_at":"2026-04-10T23:50:24.000Z","published_at":"2012-06-27T16:10:37.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou are given a n-by-2 matrix. Each row represents a numeric range, e.g.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[x = [0 5; 10 3; 20 15; 16 19; 25 25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003econtains ranges [0 5], [10 3], [20 15], [16 19], and [25 25]. Note that the first column does not always contain the smaller number. Join all overlapping ranges and return the sorted (both columns and all rows must be sorted) matrix of joined ranges\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[y = [0 10; 15 20; 25 25]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ei.e. ranges [0 5] and [10 3] are combined to [0 10], range [16 19] is completely overlapped by [15 20] and [25 25] is kept because it is a separate range.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45389,"title":"Knight's Watch","description":" \"Night gathers, and now my watch begins\"\r\n\r\nA knight is placed on an n-by-n sized chessboard at the position x. Find the probability that after k steps, the knight will remain within the chessboard.\r\n\r\nAny knight's move that places him outside the board should be considered invalid.\r\n\r\n For simplicity, the knight's position on the chessboard is defined with the numeric\r\n notation instead of algebraic notation. so 'Ka1' is represented as (1,1).\r\n\r\nBrief explanation:\r\n\r\n Say the knight is placed in pos-(1,1). A knight has 8 possible moves. So in the next move, \r\nthe Knight can go to 8 different positions in the chessboard. But among them, only 2\r\n positions are valid i.e. the knight remains within the chessboard and they are -\r\n(3,2) \u0026 (2,3). So the prob. is 2/8 after 1 move. What will be the probability after k moves?\r\n\r\n","description_html":"\u003cpre class=\"language-matlab\"\u003e\"Night gathers, and now my watch begins\"\r\n\u003c/pre\u003e\u003cp\u003eA knight is placed on an n-by-n sized chessboard at the position x. Find the probability that after k steps, the knight will remain within the chessboard.\u003c/p\u003e\u003cp\u003eAny knight's move that places him outside the board should be considered invalid.\u003c/p\u003e\u003cpre\u003e For simplicity, the knight's position on the chessboard is defined with the numeric\r\n notation instead of algebraic notation. so 'Ka1' is represented as (1,1).\u003c/pre\u003e\u003cp\u003eBrief explanation:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003eSay the knight is placed in pos-(1,1). A knight has 8 possible moves. So in the next move, \r\nthe Knight can go to 8 different positions in the chessboard. But among them, only 2\r\npositions are valid i.e. the knight remains within the chessboard and they are -\r\n(3,2) \u0026 (2,3). So the prob. is 2/8 after 1 move. What will be the probability after k moves?\r\n\u003c/pre\u003e","function_template":"function prob = knights_watch(x,n,k)","test_suite":"%%\r\nx =[1,1];\r\nassert(isequal(knights_watch(x,3,2),0.0625))\r\n%%\r\nx =[1,1];\r\nassert(isequal(knights_watch(x,4,4),0.0176))\r\n%%\r\nx =[6,4];\r\nassert(isequal(knights_watch(x,6,9),0.012))\r\n%%\r\nx =[6,4];\r\nassert(isequal(knights_watch(x,8,25),0.0011))\r\n%%\r\nx =[8,8];\r\nassert(isequal(knights_watch(x,8,15),0.0042))\r\n%%\r\nx =[8,8];\r\nassert(isequal(knights_watch(x,16,15),0.4666))\r\n%%\r\nx =[3,1];\r\nassert(isequal(knights_watch(x,16,50),0.0037))","published":true,"deleted":false,"likes_count":1,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":13,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-03-25T18:55:22.000Z","updated_at":"2026-01-23T12:14:39.000Z","published_at":"2020-03-25T18:55:22.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[\\\"Night gathers, and now my watch begins\\\"]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA knight is placed on an n-by-n sized chessboard at the position x. Find the probability that after k steps, the knight will remain within the chessboard.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAny knight's move that places him outside the board should be considered invalid.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ For simplicity, the knight's position on the chessboard is defined with the numeric\\n notation instead of algebraic notation. so 'Ka1' is represented as (1,1).]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBrief explanation:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[Say the knight is placed in pos-(1,1). A knight has 8 possible moves. So in the next move, \\nthe Knight can go to 8 different positions in the chessboard. But among them, only 2\\npositions are valid i.e. the knight remains within the chessboard and they are -\\n(3,2) \u0026 (2,3). So the prob. is 2/8 after 1 move. What will be the probability after k moves?]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45426,"title":"The Tortoise and the Hare - 02","description":"Previous problem \u003chttps://www.mathworks.com/matlabcentral/cody/problems/45425-the-tortoise-and-the-hare-01\u003e\r\n\r\nSuppose in an infinitely long line, the tortoise is standing in position 0.\r\n\r\nFrom that place, it can move in both +ve and -ve direction. The condition is that, in i-th jump, it can move i step forward or backward. \r\n\r\nSo one possible scenario can be -\r\n\r\n 0 [i=1] --- 1 step forward\r\n 1 [i=2] --- 2 step forward\r\n 3 [i=3] --- 3 step forward\r\n 6 [i=4] --- 4 step backward\r\n 2 [i=5] --- 5 step forward\r\n 7 [i=6] --- 6 step backward\r\n 1 [i=7] --- 7 step forward\r\n 8\r\n\r\nIf you look carefully, you'll find that -- If the tortoise moves this way, it'll always be able to reach any destination (x). \r\n\r\nThe question is what is the minimum number of moves it'll take to reach destination x.\r\n\r\nFor example -- \r\n\r\n if x=8\r\n \u003e\u003e in the above example, it takes 7 steps\r\n \u003e\u003e but if it moves this way -- [0,-1,1,4,8] -- steps required = 4.\r\n\r\nSo 4 is the optimum way.\r\n","description_html":"\u003cp\u003ePrevious problem \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45425-the-tortoise-and-the-hare-01\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45425-the-tortoise-and-the-hare-01\u003c/a\u003e\u003c/p\u003e\u003cp\u003eSuppose in an infinitely long line, the tortoise is standing in position 0.\u003c/p\u003e\u003cp\u003eFrom that place, it can move in both +ve and -ve direction. The condition is that, in i-th jump, it can move i step forward or backward.\u003c/p\u003e\u003cp\u003eSo one possible scenario can be -\u003c/p\u003e\u003cpre\u003e 0 [i=1] --- 1 step forward\r\n 1 [i=2] --- 2 step forward\r\n 3 [i=3] --- 3 step forward\r\n 6 [i=4] --- 4 step backward\r\n 2 [i=5] --- 5 step forward\r\n 7 [i=6] --- 6 step backward\r\n 1 [i=7] --- 7 step forward\r\n 8\u003c/pre\u003e\u003cp\u003eIf you look carefully, you'll find that -- If the tortoise moves this way, it'll always be able to reach any destination (x).\u003c/p\u003e\u003cp\u003eThe question is what is the minimum number of moves it'll take to reach destination x.\u003c/p\u003e\u003cp\u003eFor example --\u003c/p\u003e\u003cpre\u003e if x=8\r\n \u0026gt;\u0026gt; in the above example, it takes 7 steps\r\n \u0026gt;\u0026gt; but if it moves this way -- [0,-1,1,4,8] -- steps required = 4.\u003c/pre\u003e\u003cp\u003eSo 4 is the optimum way.\u003c/p\u003e","function_template":"function y = rabbit(n)","test_suite":"%%\r\nassert(isequal(rabbit(8),4))\r\n%%\r\nassert(isequal(rabbit(18),7))\r\n%%\r\nassert(isequal(rabbit(-600),35))\r\n%%\r\nassert(isequal(rabbit(6600),115))\r\n%%\r\nassert(isequal(rabbit(99999),449))\r\n%%\r\nassert(isequal(rabbit(-16),7))\r\n%%\r\nassert(isequal(rabbit(45237929),9513))\r\n%%\r\nassert(isequal(rabbit(46),11))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":5,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":24,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-07T05:20:53.000Z","updated_at":"2026-03-30T18:12:01.000Z","published_at":"2020-04-07T05:20:53.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrevious problem\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45425-the-tortoise-and-the-hare-01\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45425-the-tortoise-and-the-hare-01\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSuppose in an infinitely long line, the tortoise is standing in position 0.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFrom that place, it can move in both +ve and -ve direction. The condition is that, in i-th jump, it can move i step forward or backward.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo one possible scenario can be -\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 0 [i=1] --- 1 step forward\\n 1 [i=2] --- 2 step forward\\n 3 [i=3] --- 3 step forward\\n 6 [i=4] --- 4 step backward\\n 2 [i=5] --- 5 step forward\\n 7 [i=6] --- 6 step backward\\n 1 [i=7] --- 7 step forward\\n 8]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf you look carefully, you'll find that -- If the tortoise moves this way, it'll always be able to reach any destination (x).\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe question is what is the minimum number of moves it'll take to reach destination x.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example --\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ if x=8\\n \u003e\u003e in the above example, it takes 7 steps\\n \u003e\u003e but if it moves this way -- [0,-1,1,4,8] -- steps required = 4.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo 4 is the optimum way.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45422,"title":"Coin Distribution - 02 ","description":"Prev prob \u003chttps://www.mathworks.com/matlabcentral/cody/problems/45385-coin-distribution\u003e\r\n\r\nGiven a set of coins and an amount, find out how many ways the amount can be made using the coins given.\r\nAssume, there is an infinite supply of all the coins.\r\n\r\nFor instance,\r\n\r\n Amount = 10\r\n Coins = [ 2,3,5]\r\n\r\n possible ways are - [2,2,2,2,2],[2,3,5],[5,5],[2,2,3,3]\r\n so total no. of ways = 4.","description_html":"\u003cp\u003ePrev prob \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45385-coin-distribution\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45385-coin-distribution\u003c/a\u003e\u003c/p\u003e\u003cp\u003eGiven a set of coins and an amount, find out how many ways the amount can be made using the coins given.\r\nAssume, there is an infinite supply of all the coins.\u003c/p\u003e\u003cp\u003eFor instance,\u003c/p\u003e\u003cpre\u003e Amount = 10\r\n Coins = [ 2,3,5]\u003c/pre\u003e\u003cpre\u003e possible ways are - [2,2,2,2,2],[2,3,5],[5,5],[2,2,3,3]\r\n so total no. of ways = 4.\u003c/pre\u003e","function_template":"function out = coin_lev(coins,amount)","test_suite":"%%\r\ncoins= [ 2,3,5];\r\namount = 10;\r\nassert(isequal(coin_lev(coins,amount),4))\r\n\r\n%%\r\ncoins= [2,3,5,10];\r\namount = 15;\r\nassert(isequal(coin_lev(coins,amount),9))\r\n\r\n%%\r\ncoins= [ 2,3,5];\r\namount = 50;\r\nassert(isequal(coin_lev(coins,amount),51))\r\n\r\n%%\r\ncoins= [2,5,10,1,20];\r\namount = 1225;\r\nassert(isequal(coin_lev(coins,amount),49884828))\r\n\r\n%%\r\ncoins= [ 11,19,23];\r\namount = 12252;\r\nassert(isequal(coin_lev(coins,amount),15681))\r\n%%\r\ncoins= [ 11,19,23,100];\r\namount = 50;\r\nassert(isequal(coin_lev(coins,amount),0))\r\n\r\n%%\r\ncoins= [1,2,3,4,5,8,10,15,20,25];\r\namount = 200;\r\nassert(isequal(coin_lev(coins,amount),119495730))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":32,"test_suite_updated_at":"2020-04-02T19:38:33.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-02T19:03:04.000Z","updated_at":"2026-02-09T20:05:28.000Z","published_at":"2020-04-02T19:38:33.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ePrev prob\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45385-coin-distribution\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45385-coin-distribution\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven a set of coins and an amount, find out how many ways the amount can be made using the coins given. Assume, there is an infinite supply of all the coins.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor instance,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Amount = 10\\n Coins = [ 2,3,5]\\n\\n possible ways are - [2,2,2,2,2],[2,3,5],[5,5],[2,2,3,3]\\n so total no. of ways = 4.]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45474,"title":"Sub-sequence - 02","description":"Given two sequences, find the length of the longest common subsequence.\r\n\r\n a=[1,1,1,1,1,2,3,1,4]\r\n b=[2,3,0,0,9,5,4,1]\r\n longest Common subsequence = [2,3,4]\r\n = [2,3,1]\r\nso,length=3","description_html":"\u003cp\u003eGiven two sequences, find the length of the longest common subsequence.\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003ea=[1,1,1,1,1,2,3,1,4]\r\nb=[2,3,0,0,9,5,4,1]\r\nlongest Common subsequence = [2,3,4]\r\n = [2,3,1]\r\nso,length=3\r\n\u003c/pre\u003e","function_template":"function y = longest_sub_common(a,b)","test_suite":"%%\r\na=[1,1,1,1,1,2,3,1,4]\r\nb=[2,3,0,0,9,5,4,1]\r\nassert(isequal(longest_sub_common(a,b),3))\r\n\r\n%%\r\na=[1,1,1,1,1,2,3]\r\nb=[2,3,0,0,9,5,4,1]\r\nassert(isequal(longest_sub_common(a,b),2))\r\n\r\n%%\r\na=[1,1,1,1,1,2,3,1,4]\r\nb=zeros(1,500);\r\nassert(isequal(longest_sub_common(a,b),0))\r\n\r\n%%\r\na=[1,1,1,1,1,2,3,1,4]\r\nb=[zeros(1,50),ones(1,200),ones(1,20)*3]\r\nassert(isequal(longest_sub_common(a,b),6))\r\n\r\n%%\r\na='aaabbbcccxyz'\r\nb='abcyycbaabc'\r\nassert(isequal(longest_sub_common(a,b),5))\r\n\r\n%%\r\na=[10\t9\t8\t2\t4\t2\t1\t4\t7\t7\t6\t5\t3\t6\t8\t8\t6\t8\t7\t2\t6\t4\t1\t2\t2\t7\t5\t7\t3\t1\t6\t3\t10\t10\t4\t1\t7\t9\t10\t1\t5\t6\t7\t8\t7\t8\t4\t6\t2\t1\t10\t3\t6\t10\t2\t2\t4\t10\t4\t3\t2\t4\t4\t2\t5\t1\t7\t1\t6\t8\t3\t5\t6\t1\t5\t7\t3\t9\t10\t9\t6\t3\t8\t3\t10\t7\t7\t2\t1\t3\t9\t10\t7\t8\t3\t6\t9\t5\t10\t1\t4\t6\t1\t8\t6\t6\t9\t9\t8\t4\t5\t8\t2\t2\t3\t6\t10\t8\t4\t3\t9\t10\t7\t3\t1\t9\t6\t10\t1\t6\t3\t9\t2\t5\t4\t9\t7\t3\t4\t2\t7\t6\t2\t2\t5\t10\t6\t1\t1\t9\t5\t4\t8\t4\t6\t8\t9\t4\t7\t10\t1\t6\t5\t4\t3\t8\t10\t2\t8\t2\t10\t9\t5\t8\t5\t9\t4\t1\t6\t10\t2\t5\t8\t1\t10\t8\t6\t2\t5\t6\t10\t9\t10\t7\t5\t10\t5\t3\t4\t8\t6\t8\t10\t10\t6\t10\t2\t1\t4\t6\t6\t10\t6\t5\t6\t8\t1\t9\t2\t5\t3\t4\t7\t2\t3\t2\t2\t4\t9\t5\t5\t2\t10\t5\t9\t7\t4\t9\t8\t5\t9\t9\t5\t4\t6\t10\t8\t4\t8\t10\t6\t6\t4\t1\t2\t1\t5\t1\t10\t7\t1\t1\t3\t5\t2\t1\t8\t4\t8\t5\t5\t1\t1\t1\t6\t3\t9\t9\t10\t5\t3\t3\t6\t8\t4\t5\t7\t10\t2\t8\t6\t5\t9\t4\t2\t7\t7\t4\t9\t10\t10\t2\t3\t1\t7\t2\t5\t9\t6\t4\t3\t5\t10\t2\t5\t9\t1\t7\t10\t3\t2\t7\t10\t7\t9\t2\t8\t4\t5\t2\t9\t7\t8\t9\t1\t10\t5\t8\t8\t9\t2\t5\t7\t10\t9\t9\t6\t6\t9\t1\t9\t5\t1\t8\t2\t2\t7\t3\t4\t5\t5\t4\t7\t2\t2\t1\t4\t8\t3\t8\t7\t9\t9\t3\t4\t6\t4\t9\t9\t6\t3\t7\t3\t5\t4\t6\t10\t8\t10\t3\t6\t1\t8\t7\t9\t10\t10\t5\t1\t6\t3\t3\t4\t1\t8\t8\t6\t4\t9\t6\t10\t9\t4\t6\t4\t7\t8\t8\t2\t9\t1\t5\t8\t8\t4\t8\t9\t3\t2\t3\t4\t3\t10\t1\t6\t2\t9\t2\t6\t10\t4\t1\t3\t4\t4\t3\t10\t7\t10\t5\t10\t1\t7\t9\t3\t10\t8\t9\t6\t8\t4\t3\t4\t6\t9\t3\t5\t9\t7\t10\t3\t9\t7\t3\t5\t4\t6\t9\t2\t9\t9\t4\t5\t6\t8\t8\t8\t4\t5\t10\t6\t9\t3\t7\t6\t10\t1\t6\t6\t1]\r\nb=[14\t14\t7\t12\t3\t10\t4\t7\t13\t3\t5\t8\t6\t12\t15\t3\t4\t11\t6\t15\t15\t10\t13\t7\t10\t15\t9\t15\t11\t8\t10\t14\t3\t6\t15\t7\t10\t14\t15\t10\t2\t1\t10\t9\t15\t12\t10\t8\t4\t15\t9\t1\t11\t8\t1\t14\t5\t4\t2\t5\t4\t10\t1\t5\t5\t14\t7\t12\t10\t12\t2\t15\t13\t1\t7\t5\t10\t4\t9\t10\t9\t7\t1\t8\t7\t2\t7\t7\t9\t13\t11\t14\t1\t4\t7\t15\t12\t7\t6\t1\t12\t8\t3\t7\t3\t12\t6\t15\t1\t13\t10\t9\t10\t11\t2\t14\t1\t5\t3\t14\t2\t9\t10\t10\t13\t1\t13\t8\t11\t4\t9\t11\t15\t7\t2\t1\t10\t12\t11\t6\t15\t8\t15\t2\t4\t12\t14\t12\t5\t3\t13\t12\t5\t4\t5\t13\t13\t9\t9\t5\t11\t12\t7\t7\t7\t5\t11\t14\t14\t12\t4\t11\t2\t2\t3\t3\t9\t2\t13\t11\t14\t8\t10\t14\t9\t5\t15\t1\t5\t15\t6\t5\t2\t14\t3\t5\t14\t8\t10\t9\t11\t1\t8\t1\t13\t6\t13\t4\t9\t15\t1\t1\t1\t11\t9\t2\t12\t10\t2\t2\t3\t12\t2\t4\t4\t2\t13\t11\t12\t10\t8\t5\t10\t2\t3\t1\t15\t15\t2\t8\t10\t5\t12\t9\t7\t5\t12\t14\t11\t7]\r\nassert(isequal(longest_sub_common(a,b),121))\r\n\r\n%%\r\na='aaabaaabaaabaaa'\r\nb='abababababa'\r\nassert(isequal(longest_sub_common(a,b),9))","published":true,"deleted":false,"likes_count":2,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":22,"test_suite_updated_at":"2020-04-23T16:07:08.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-23T15:59:45.000Z","updated_at":"2026-02-10T12:11:50.000Z","published_at":"2020-04-23T16:07:08.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven two sequences, find the length of the longest common subsequence.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[a=[1,1,1,1,1,2,3,1,4]\\nb=[2,3,0,0,9,5,4,1]\\nlongest Common subsequence = [2,3,4]\\n = [2,3,1]\\nso,length=3]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45475,"title":"Sub-sequence - 03","description":"Given three sequences, find the length of the longest common subsequence.\r\n\r\nIt is similar to the previous problem -- \r\n\r\n\u003chttps://www.mathworks.com/matlabcentral/cody/groups/1/problems/45474-sub-sequence-02\u003e","description_html":"\u003cp\u003eGiven three sequences, find the length of the longest common subsequence.\u003c/p\u003e\u003cp\u003eIt is similar to the previous problem --\u003c/p\u003e\u003cp\u003e\u003ca href = \"https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45474-sub-sequence-02\"\u003ehttps://www.mathworks.com/matlabcentral/cody/groups/1/problems/45474-sub-sequence-02\u003c/a\u003e\u003c/p\u003e","function_template":"function y = longest_sub_common_3d(a,b,c)","test_suite":"%%\r\na=[1,1,1,1,1,2,3,1,4]\r\nb=[2,3,0,0,9,5,4,1]\r\nc=[10,10,9,9,4,4,5]\r\nassert(isequal(longest_sub_common_3d(a,b,c),1))\r\n\r\n%%\r\na=[1,1,1,1,1,2,3,1,4]\r\nb=[2,3,0,1,0,9,5,4,1,10]\r\nc=[10,10,9,9,4,4,5,1,1,3]\r\nassert(isequal(longest_sub_common_3d(a,b,c),2))\r\n\r\n%%\r\na=' arthur'\r\nb='ser arthur '\r\nc='ser arthur dayne'\r\nassert(isequal(longest_sub_common_3d(a,b,c),7))\r\n\r\n%%\r\na=[105\t29\t60\t106\t30\t28\t103\t8\t34\t66\t23\t71\t89\t56\t73\t89\t26\t67\t13\t58\t94\t103\t56\t31\t73\t102\t57\t109\t22\t13\t34\t45\t47\t35\t78\t11\t45\t33\t35\t12\t66\t32\t18\t1\t32\t62\t97\t5\t101\t15\t93\t89\t102\t16\t57\t45\t20\t64\t68\t24\t58\t110\t55\t78\t46\t4\t33\t89\t39\t10\t57\t41\t83\t59\t90\t91\t22\t14\t92\t71\t2\t100\t58\t61\t68\t85\t95\t43\t10\t82\t37\t94\t42\t92\t20\t15\t98\t5\t77\t82\t49\t43\t109\t45\t49\t18\t37\t35\t100\t28\t35]\r\nb=[46\t79\t16\t97\t10\t52\t4\t84\t78\t24\t76\t62\t95\t63\t101\t47\t40\t55\t29\t104\t52\t29\t48\t78\t45\t21\t96\t65\t42\t25\t25\t58\t49\t83\t8\t95\t76\t16\t96\t23\t68\t61\t19\t1\t86\t85\t47\t7\t66\t20\t81\t60\t29\t102\t85\t99\t8\t21\t82\t78\t87\t56\t48\t68\t95\t75\t59\t34\t79\t43\t64\t99\t94\t100\t105\t91\t1\t1\t10\t29\t3\t48\t38\t61\t103\t34\t38\t96\t38\t16\t57\t96\t43\t78\t70\t50\t53\t106\t10\t32\t50\t66\t98\t53\t49\t83\t52\t96\t52\t56\t55]\r\n%c=[46\t79\t16\t97\t10\t52\t4\t84\t78\t24\t76\t62\t95\t63\t101\t47\t40\t55\t29\t104\t52\t29\t48\t78\t45\t21\t96\t65\t42\t25\t25\t58\t49\t83\t8\t95\t76\t16\t96\t23\t68\t61\t19\t1\t86\t85\t47\t7\t66\t20\t81\t60\t29\t102\t85\t99\t8\t21\t82\t78\t87\t56\t48\t68\t95\t75\t59\t34\t79\t43\t64\t99\t94\t100\t105\t91\t1\t1\t10\t29\t3\t48\t38\t61\t103\t34\t38\t96\t38\t16\t57\t96\t43\t78\t70\t50\t53\t106\t10\t32\t50\t66\t98\t53\t49\t83\t52\t96\t52\t56\t55]\r\nc=[26\t10\t8\t99\t26\t96\t80\t97\t105\t16\t44\t109\t72\t100\t54\t2\t70\t26\t59\t81\t68\t66\t49\t28\t48\t2\t68\t107\t11\t4\t99\t28\t1\t91\t16\t98\t11\t40\t66\t65\t75\t72\t49\t16\t84\t27\t73\t96\t10\t108\t4\t93\t93\t6\t61\t105\t36\t90\t67\t88\t89\t6\t32\t73\t55\t108\t84\t64\t34\t29\t99\t50\t91\t11\t96\t4\t100\t100\t59\t14\t20\t79\t93\t4\t85\t107\t39\t71\t39\t25\t88\t81\t31\t65\t47\t11\t3\t55\t31\t38\t32\t19\t45\t78\t23\t74\t50\t49\t20\t22\t69]\r\nassert(isequal(longest_sub_common_3d(a,b,c),6))\r\n\r\n%%\r\na=repelem('abc',1,50)\r\nb=repelem('acb',1,50)\r\nc=repelem('acdb',1,50)\r\nassert(isequal(longest_sub_common_3d(a,b,c),100))\r\n","published":true,"deleted":false,"likes_count":3,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":17,"test_suite_updated_at":"2020-04-23T16:36:03.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-23T16:29:45.000Z","updated_at":"2026-03-18T19:22:50.000Z","published_at":"2020-04-23T16:36:03.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven three sequences, find the length of the longest common subsequence.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIt is similar to the previous problem --\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45474-sub-sequence-02\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/groups/1/problems/45474-sub-sequence-02\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":2523,"title":"longest common substring : Skipped character version","description":"Two strings are given. Find the longest common substring between them. The substring characters need not be adjacent. They, however, must appear in same order in both input strings.\r\n\r\nExample:\r\n\r\n str1 = 'abcdefghi';\r\n str2 = 'zazbzczd';\r\n \r\n output = 'abcd'","description_html":"\u003cp\u003eTwo strings are given. Find the longest common substring between them. The substring characters need not be adjacent. They, however, must appear in same order in both input strings.\u003c/p\u003e\u003cp\u003eExample:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003estr1 = 'abcdefghi';\r\nstr2 = 'zazbzczd';\r\n\u003c/pre\u003e\u003cpre class=\"language-matlab\"\u003eoutput = 'abcd'\r\n\u003c/pre\u003e","function_template":"function y = skipped(str1,str2)\r\n y = x;\r\nend","test_suite":"%%\r\nstr1 = 'abcdefghi';\r\nstr2 = 'zazbzczd';\r\ny_correct = 'abcd';\r\nassert(isequal(skipped(str1,str2),y_correct))\r\n\r\n%%\r\nstr1 = 'abcdefghi';\r\nstr2 = 'zzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzzz';\r\ny_correct = '';\r\nassert(isequal(skipped(str1,str2),y_correct))\r\n\r\n\r\n\r\n%%\r\nstr1 = 'catcatcat';\r\nstr2 = 'catcatcat';\r\ny_correct = str1;\r\nassert(isequal(skipped(str1,str2),y_correct))\r\n\r\n\r\n%%\r\nstr1 = 'an example of a string';\r\nstr2 = 'the example z a s t r i i i n ssss';\r\ny_correct = ' example a strin';\r\nassert(isequal(skipped(str1,str2),y_correct))\r\n\r\n\r\n%%\r\nstr1 = 'a string with many characters';\r\nstr2 = 'zzz zzz zzz zzz zzz';\r\ny_correct = ' ';\r\nassert(isequal(skipped(str1,str2),y_correct))\r\n\r\n\r\n%%\r\nstr1 = 'lets!not!use!spaces';\r\nstr2 = 'z!zzzzzzzzZZZzZzZ!zzzzz!zzzzzzzzz!!!!!!!!!zzz';\r\ny_correct = '!!!';\r\nassert(isequal(skipped(str1,str2),y_correct))","published":true,"deleted":false,"likes_count":0,"comments_count":4,"created_by":17203,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":53,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":32,"created_at":"2014-08-20T10:44:21.000Z","updated_at":"2026-04-08T08:28:14.000Z","published_at":"2014-08-20T10:44:21.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTwo strings are given. Find the longest common substring between them. The substring characters need not be adjacent. They, however, must appear in same order in both input strings.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[str1 = 'abcdefghi';\\nstr2 = 'zazbzczd';\\n\\noutput = 'abcd']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45416,"title":"Don't be Greedy!","description":"A list of assignments is given to the students along with the submission deadlines. Each of\r\n the assignment contains particular marks.\r\n\r\nIf the student can submit a particular assignment within its deadline, he'll get marks.\r\n\r\nBut he can submit only one assignment per day.\r\n\r\nFor instance,\r\n\r\n Assignment = [ a1, a2, a3, a4, a5, a6]\r\n Marks = [ 60,100, 20, 40, 20, 10]\r\n Deadline = [ 2, 1, 3, 2, 1, 3]\r\n\r\nNow, on the 1st day - he can submit one among all the assignments. But in the 2nd day, he can no longer submit a2 \u0026 a5.\r\n\r\nHe wants to achieve maximum marks by carefully submitting those assignments within the deadlines.\r\nCan u help him?\r\n\r\nThe answer should be - [a2,a1,a3]. Since by submitting in this sequence, he can get the maximum marks.","description_html":"\u003cp\u003eA list of assignments is given to the students along with the submission deadlines. Each of\r\n the assignment contains particular marks.\u003c/p\u003e\u003cp\u003eIf the student can submit a particular assignment within its deadline, he'll get marks.\u003c/p\u003e\u003cp\u003eBut he can submit only one assignment per day.\u003c/p\u003e\u003cp\u003eFor instance,\u003c/p\u003e\u003cpre\u003e Assignment = [ a1, a2, a3, a4, a5, a6]\r\n Marks = [ 60,100, 20, 40, 20, 10]\r\n Deadline = [ 2, 1, 3, 2, 1, 3]\u003c/pre\u003e\u003cp\u003eNow, on the 1st day - he can submit one among all the assignments. But in the 2nd day, he can no longer submit a2 \u0026 a5.\u003c/p\u003e\u003cp\u003eHe wants to achieve maximum marks by carefully submitting those assignments within the deadlines.\r\nCan u help him?\u003c/p\u003e\u003cp\u003eThe answer should be - [a2,a1,a3]. Since by submitting in this sequence, he can get the maximum marks.\u003c/p\u003e","function_template":"function yy=greedy_01(marks,deadline)","test_suite":"%%\r\nmarks = [ 60,100, 20, 40, 20, 10]\r\ndeadline = [ 2, 1, 3, 2, 1, 3]\r\nassert(isequal(greedy_01(marks,deadline),[2,1,3]))\r\n\r\n%%\r\nmarks = [10,10,50,40,30,100,20,10]\r\ndeadline = [1,2,3,3,5,5,1,3]\r\nassert(isequal(greedy_01(marks,deadline),[7,4,3,5,6]))\r\n\r\n%%\r\nmarks = [50,100,40,80,200,220,10]\r\ndeadline = [2,1,2,1,1,1,4]\r\nassert(isequal(greedy_01(marks,deadline),[6,1,7]))\r\n\r\n%%\r\nmarks = [50,100,40,80,200,220,10,150]\r\ndeadline = [2,1,3,6,2,2,6,7]\r\nassert(isequal(greedy_01(marks,deadline),[5 6 3 7 4 8]))\r\n","published":true,"deleted":false,"likes_count":2,"comments_count":15,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":19,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-02T00:18:30.000Z","updated_at":"2026-03-10T13:00:11.000Z","published_at":"2020-04-02T00:18:30.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA list of assignments is given to the students along with the submission deadlines. Each of the assignment contains particular marks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eIf the student can submit a particular assignment within its deadline, he'll get marks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eBut he can submit only one assignment per day.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor instance,\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ Assignment = [ a1, a2, a3, a4, a5, a6]\\n Marks = [ 60,100, 20, 40, 20, 10]\\n Deadline = [ 2, 1, 3, 2, 1, 3]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eNow, on the 1st day - he can submit one among all the assignments. But in the 2nd day, he can no longer submit a2 \u0026amp; a5.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eHe wants to achieve maximum marks by carefully submitting those assignments within the deadlines. Can u help him?\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe answer should be - [a2,a1,a3]. Since by submitting in this sequence, he can get the maximum marks.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":45451,"title":"Don't be Too Greedy!!","description":"Refer to the prev problem \u003chttps://www.mathworks.com/matlabcentral/cody/problems/45450-don-t-be-too-greedy\u003e\r\n\r\nFor this time, calculate the maximum number of marks he can achieve after finishing all his assignments.","description_html":"\u003cp\u003eRefer to the prev problem \u003ca href = \"https://www.mathworks.com/matlabcentral/cody/problems/45450-don-t-be-too-greedy\"\u003ehttps://www.mathworks.com/matlabcentral/cody/problems/45450-don-t-be-too-greedy\u003c/a\u003e\u003c/p\u003e\u003cp\u003eFor this time, calculate the maximum number of marks he can achieve after finishing all his assignments.\u003c/p\u003e","function_template":"function y = greedy_3(p,t,mark)","test_suite":"%%\r\np=[ 10, 4, 1, 2, 5, 2];\r\nt=[ 2, 4, 3, 8, 1, 10];\r\nassert(isequal(greedy_3(p,t,100),515))\r\n\r\n%%\r\n p=[4,2,1, 5, 3, 2, 6];\r\n t=[1,8,2, 3, 1, 4, 2];\r\nassert(isequal(greedy_3(p,t,10),-16))\r\n\r\n%%\r\np=[9,10,2,10,7,1,3,6,10,10,2,10];\r\nt=[48 25 41 8 22 46 40 48 33 2 43 47];\r\nassert(isequal(greedy_3(p,t,200),-4008))\r\n\r\n%%\r\np=[1,3,1,2,4];\r\nt=[1,4,10,2,5];\r\nassert(isequal(greedy_3(p,t,200),950))\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":363598,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":9,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2020-04-13T12:04:50.000Z","updated_at":"2026-02-24T09:23:57.000Z","published_at":"2020-04-13T12:04:50.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eRefer to the prev problem\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://www.mathworks.com/matlabcentral/cody/problems/45450-don-t-be-too-greedy\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026lt;https://www.mathworks.com/matlabcentral/cody/problems/45450-don-t-be-too-greedy\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e\u0026gt;\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor this time, calculate the maximum number of marks he can achieve after finishing all his assignments.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":42888,"title":"Frobenius McNugget Factorization","description":"\r\nMr. Frobenius McNugget is a peculiar man.\r\nAs you might expect, he likes to eat Chicken McNuggets. But his love of number theory influences his appetite in strange ways. On any given day he wakes up with a desire for a specific number of McNuggets. If that number is 19, then no other number of McNuggets will do. But the McFastFood restaurant down the street serves McNuggets only in quantities of 6, 9, or 20 to a box. So he can be satisfied on a 21 nugget day, but on a 19 nugget day he must go hungry. To make matters more interesting, the restaurant often changes the quantities in their boxed McNuggets.\r\nGiven the box counts nuggets, what is the highest number frob for which Frobenius must go hungry? nuggets is a vector of positive integers with two or more elements.\r\nExamples\r\n nuggets = [2 5]\r\n frob = 3\r\n\r\n nuggets = [6 9 20]\r\n frob = 43","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.44px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: none solid rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 787.419px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 408.495px 393.704px; transform-origin: 408.495px 393.71px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 450.197px; font-family: Helvetica, Arial, sans-serif; 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\" data-image-state=\"image-loaded\"\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0069px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385.498px 10.4977px; text-align: left; transform-origin: 385.498px 10.5035px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eMr. Frobenius McNugget is a peculiar man.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 105.035px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385.498px 52.5116px; text-align: left; transform-origin: 385.498px 52.5174px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eAs you might expect, he likes to eat Chicken McNuggets. But his love of number theory influences his appetite in strange ways. On any given day he wakes up with a desire for a specific number of McNuggets. If that number is 19, then no other number of McNuggets will do. But the McFastFood restaurant down the street serves McNuggets only in quantities of 6, 9, or 20 to a box. So he can be satisfied on a 21 nugget day, but on a 19 nugget day he must go hungry. To make matters more interesting, the restaurant often changes the quantities in their boxed McNuggets.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42.0139px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385.498px 21.0069px; text-align: left; transform-origin: 385.498px 21.0069px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eGiven the box counts\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003enuggets\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e, what is the highest number\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003efrob\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e for which Frobenius must go hungry?\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"font-family: Menlo, Monaco, Consolas, \u0026quot;Courier New\u0026quot;, monospace; \"\u003enuggets\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e is a vector of positive integers with two or more elements.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21.0069px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 385.498px 10.4977px; text-align: left; transform-origin: 385.498px 10.5035px; white-space-collapse: preserve; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 0px 0px; transform-origin: 0px 0px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eExamples\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 102.199px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 405.498px 51.0995px; transform-origin: 405.498px 51.0995px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4398px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.740741px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.740741px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.740741px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.740741px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405.498px 10.2199px; text-wrap-mode: nowrap; transform-origin: 405.498px 10.2199px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e nuggets = [2 5]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4398px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.740741px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.740741px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.740741px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.740741px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405.498px 10.2199px; text-wrap-mode: nowrap; transform-origin: 405.498px 10.2199px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e frob = 3\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4398px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.740741px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.740741px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.740741px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.740741px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405.498px 10.2199px; text-wrap-mode: nowrap; transform-origin: 405.498px 10.2199px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4398px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.740741px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.740741px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.740741px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.740741px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405.498px 10.2199px; text-wrap-mode: nowrap; transform-origin: 405.498px 10.2199px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e nuggets = [6 9 20]\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4398px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 0.740741px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 0.740741px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 0.740741px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 0.740741px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 405.498px 10.2199px; text-wrap-mode: nowrap; transform-origin: 405.498px 10.2199px; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 0px 0px; tab-size: 4; transform-origin: 0px 0px; unicode-bidi: normal; white-space-collapse: preserve; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; \"\u003e frob = 43\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function frob = hungerGame(nuggets)\r\n frob = 0;\r\nend","test_suite":"%%\r\nnuggets = [2 5];\r\nfrob_correct = 3;\r\nassert(isequal(hungerGame(nuggets),frob_correct))\r\n\r\n%%\r\nnuggets = [6 9 20];\r\nfrob_correct = 43;\r\nassert(isequal(hungerGame(nuggets),frob_correct))\r\n\r\n\r\n%%\r\nnuggets = [17 19 32];\r\nfrob_correct = 175;\r\nassert(isequal(hungerGame(nuggets),frob_correct))\r\n\r\n\r\n%%\r\nnuggets = [13 14 15];\r\nfrob_correct = 77;\r\nassert(isequal(hungerGame(nuggets),frob_correct))\r\n","published":true,"deleted":false,"likes_count":16,"comments_count":4,"created_by":7,"edited_by":7,"edited_at":"2025-03-31T15:09:16.000Z","deleted_by":null,"deleted_at":null,"solvers_count":42,"test_suite_updated_at":"2016-06-07T17:02:16.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2016-06-06T19:10:29.000Z","updated_at":"2026-02-09T14:49:48.000Z","published_at":"2016-06-06T19:49:01.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:customXml w:element=\\\"image\\\"\u003e\u003cw:customXmlPr\u003e\u003cw:attr w:name=\\\"height\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"width\\\" w:val=\\\"-1\\\"/\u003e\u003cw:attr w:name=\\\"verticalAlign\\\" w:val=\\\"baseline\\\"/\u003e\u003cw:attr w:name=\\\"altText\\\" w:val=\\\"\\\"/\u003e\u003cw:attr w:name=\\\"relationshipId\\\" w:val=\\\"rId1\\\"/\u003e\u003c/w:customXmlPr\u003e\u003c/w:customXml\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eMr. Frobenius McNugget is a peculiar man.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs you might expect, he likes to eat Chicken McNuggets. But his love of number theory influences his appetite in strange ways. On any given day he wakes up with a desire for a specific number of McNuggets. If that number is 19, then no other number of McNuggets will do. But the McFastFood restaurant down the street serves McNuggets only in quantities of 6, 9, or 20 to a box. So he can be satisfied on a 21 nugget day, but on a 19 nugget day he must go hungry. To make matters more interesting, the restaurant often changes the quantities in their boxed McNuggets.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven the box counts\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enuggets\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e, what is the highest number\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003efrob\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e for which Frobenius must go hungry?\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:rFonts w:cs=\\\"monospace\\\"/\u003e\u003c/w:rPr\u003e\u003cw:t\u003enuggets\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is a vector of positive integers with two or more elements.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExamples\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ nuggets = [2 5]\\n frob = 3\\n\\n nuggets = [6 9 20]\\n frob = 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\",\"relationship\":null}],\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"target\":\"/matlab/document.xml\",\"relationshipId\":\"rId1\"}]}"},{"id":2309,"title":"Calculate the Damerau-Levenshtein distance between two strings.","description":"\u003chttp://www.mathworks.co.uk/matlabcentral/cody/problems/2303-compute-hamming-distances-between-each-pair-of-rows-from-two-input-matrices Problem 2303\u003e reminded me a few older ones dealing with metrics between strings, problems \u003chttp://www.mathworks.co.uk/matlabcentral/cody/problems/93-calculate-the-levenshtein-distance-between-two-strings 93\u003e,\r\n\u003chttp://www.mathworks.co.uk/matlabcentral/cody/problems/846-calculate-the-hamming-distance-between-two-strings 846\u003e or\r\n\u003chttp://www.mathworks.co.uk/matlabcentral/cody/problems/848-calculate-a-modified-levenshtein-distance-between-two-strings 848\u003e\r\n about Hamming and Levenshtein distances.\r\n\r\n\u003chttp://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance Damerau-Levenshtein distance\u003e is an extension to Levenshtein distance. It is also defined as minimum number of simple edit operations on string to change it into another, but the list of allowed operations is extended.\r\n\r\nAs it is written on Wikipedia there are 4 allowed edits: deletion, insertion and substitution of an single character and an transposition of two adjacent characters.\r\n\r\nExample. Such defined distance between words _gifts_ and _profit_ is 5:\r\n\r\n gifts =\u003e pgifts (insertion of 'p')\r\n pgifts =\u003e prgifts (insertion of 'r')\r\n prgifts =\u003e proifts (substitution of 'g' to 'o')\r\n proifts =\u003e profits (transposition of 'if' to 'fi')\r\n profits =\u003e profit (deletion of 's')\r\n \r\n\r\n","description_html":"\u003cp\u003e\u003ca href = \"http://www.mathworks.co.uk/matlabcentral/cody/problems/2303-compute-hamming-distances-between-each-pair-of-rows-from-two-input-matrices\"\u003eProblem 2303\u003c/a\u003e reminded me a few older ones dealing with metrics between strings, problems \u003ca href = \"http://www.mathworks.co.uk/matlabcentral/cody/problems/93-calculate-the-levenshtein-distance-between-two-strings\"\u003e93\u003c/a\u003e, \u003ca href = \"http://www.mathworks.co.uk/matlabcentral/cody/problems/846-calculate-the-hamming-distance-between-two-strings\"\u003e846\u003c/a\u003e or \u003ca href = \"http://www.mathworks.co.uk/matlabcentral/cody/problems/848-calculate-a-modified-levenshtein-distance-between-two-strings\"\u003e848\u003c/a\u003e\r\n about Hamming and Levenshtein distances.\u003c/p\u003e\u003cp\u003e\u003ca href = \"http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance\"\u003eDamerau-Levenshtein distance\u003c/a\u003e is an extension to Levenshtein distance. It is also defined as minimum number of simple edit operations on string to change it into another, but the list of allowed operations is extended.\u003c/p\u003e\u003cp\u003eAs it is written on Wikipedia there are 4 allowed edits: deletion, insertion and substitution of an single character and an transposition of two adjacent characters.\u003c/p\u003e\u003cp\u003eExample. Such defined distance between words \u003ci\u003egifts\u003c/i\u003e and \u003ci\u003eprofit\u003c/i\u003e is 5:\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003egifts =\u0026gt; pgifts (insertion of 'p')\r\npgifts =\u0026gt; prgifts (insertion of 'r')\r\nprgifts =\u0026gt; proifts (substitution of 'g' to 'o')\r\nproifts =\u0026gt; profits (transposition of 'if' to 'fi')\r\nprofits =\u0026gt; profit (deletion of 's')\r\n\u003c/pre\u003e","function_template":"function distance = Damerau_Levenshtein(str1,str2)\r\n distance = 0;\r\nend","test_suite":"%%\r\nassert(isequal(Damerau_Levenshtein('mom','dad'),3));\r\n% 3 substitutions\r\n%%\r\nassert(isequal(Damerau_Levenshtein('dogs','dog'),1));\r\n% 1 deletion\r\n%%\r\nassert(isequal(Damerau_Levenshtein('true','true'),0));\r\n% identity\r\n%%\r\nassert(isequal(Damerau_Levenshtein('true','false'),4));\r\n% 1 insertion, 3 substitutions\r\n%%\r\nassert(isequal(Damerau_Levenshtein('abc','ca'),2));\r\n% 1 deletion, 1 transposition\r\n%%\r\nassert(isequal(Damerau_Levenshtein('tee','tree'),1));\r\n%%\r\nassert(isequal(Damerau_Levenshtein('email','mails'),2));\r\n%%\r\nassert(isequal(Damerau_Levenshtein('profit','gifts'),5));\r\n%%\r\n% symmetry\r\nrnd=@()char(randi([97 122],1,randi([4 10])));\r\nfor k=1:10\r\n str1=rnd();\r\n str2=rnd();\r\n a=Damerau_Levenshtein(str1,str2);\r\n b=Damerau_Levenshtein(str2,str1);\r\n assert(isequal(a,b))\r\nend\r\n%%\r\n% trinagle inequality\r\nrnd=@()char(randi([97 122],1,randi([4 10])));\r\nfor k=1:50\r\n str1=rnd();\r\n str2=rnd();\r\n str3=rnd();\r\n a=Damerau_Levenshtein(str2,str3);\r\n b=Damerau_Levenshtein(str3,str1);\r\n c=Damerau_Levenshtein(str1,str2);\r\n assert(a+b\u003e=c)\r\nend","published":true,"deleted":false,"likes_count":1,"comments_count":8,"created_by":14358,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":46,"test_suite_updated_at":"2014-05-07T12:40:15.000Z","rescore_all_solutions":false,"group_id":32,"created_at":"2014-05-07T12:34:17.000Z","updated_at":"2026-04-08T09:10:54.000Z","published_at":"2014-05-07T12:40:15.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.co.uk/matlabcentral/cody/problems/2303-compute-hamming-distances-between-each-pair-of-rows-from-two-input-matrices\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eProblem 2303\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e reminded me a few older ones dealing with metrics between strings, problems\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.co.uk/matlabcentral/cody/problems/93-calculate-the-levenshtein-distance-between-two-strings\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e93\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e,\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.co.uk/matlabcentral/cody/problems/846-calculate-the-hamming-distance-between-two-strings\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e846\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e or\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://www.mathworks.co.uk/matlabcentral/cody/problems/848-calculate-a-modified-levenshtein-distance-between-two-strings\\\"\u003e\u003cw:r\u003e\u003cw:t\u003e848\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e about Hamming and Levenshtein distances.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Damerau%E2%80%93Levenshtein_distance\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eDamerau-Levenshtein distance\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e is an extension to Levenshtein distance. It is also defined as minimum number of simple edit operations on string to change it into another, but the list of allowed operations is extended.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eAs it is written on Wikipedia there are 4 allowed edits: deletion, insertion and substitution of an single character and an transposition of two adjacent characters.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eExample. Such defined distance between words\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003egifts\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e and\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:i/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eprofit\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is 5:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[gifts =\u003e pgifts (insertion of 'p')\\npgifts =\u003e prgifts (insertion of 'r')\\nprgifts =\u003e proifts (substitution of 'g' to 'o')\\nproifts =\u003e profits (transposition of 'if' to 'fi')\\nprofits =\u003e profit (deletion of 's')]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1394,"title":"Prime Ladders","description":"A \u003chttp://en.wikipedia.org/wiki/Word_ladder word ladder\u003e transforms one word to another by means of single-letter mutations. So COLD can become WARM like so (there are often multiple solutions):\r\n\r\n COLD\r\n CORD\r\n CARD\r\n WARD\r\n WARM\r\n\r\nA number ladder does much the same thing, changing one digit at a time. A *prime ladder* is a number ladder with the additional constraint that each element is a prime number. Here is a prime ladder that connects 757 and 139\r\n\r\n 757 \r\n 157\r\n 137\r\n 139\r\n\r\nGiven two numbers p1 and p2, construct a prime ladder column vector in which p1 is the first element, p2 is the last element, and each successive row differs by exactly one digit from the preceding element. \r\n\r\nTo restate the above example, consider\r\n\r\n p1 = 757\r\n p2 = 139\r\n\r\nfor which an acceptable answer is\r\n\r\n ladder = [757; 157; 137; 139]\r\n\r\nYou can assume that p1 and p2 contain the same number of digits. I am not looking for a unique answer. I will only check that the conditions of a prime ladder are met.\r\n\r\n","description_html":"\u003cp\u003eA \u003ca href = \"http://en.wikipedia.org/wiki/Word_ladder\"\u003eword ladder\u003c/a\u003e transforms one word to another by means of single-letter mutations. So COLD can become WARM like so (there are often multiple solutions):\u003c/p\u003e\u003cpre\u003e COLD\r\n CORD\r\n CARD\r\n WARD\r\n WARM\u003c/pre\u003e\u003cp\u003eA number ladder does much the same thing, changing one digit at a time. A \u003cb\u003eprime ladder\u003c/b\u003e is a number ladder with the additional constraint that each element is a prime number. Here is a prime ladder that connects 757 and 139\u003c/p\u003e\u003cpre\u003e 757 \r\n 157\r\n 137\r\n 139\u003c/pre\u003e\u003cp\u003eGiven two numbers p1 and p2, construct a prime ladder column vector in which p1 is the first element, p2 is the last element, and each successive row differs by exactly one digit from the preceding element.\u003c/p\u003e\u003cp\u003eTo restate the above example, consider\u003c/p\u003e\u003cpre\u003e p1 = 757\r\n p2 = 139\u003c/pre\u003e\u003cp\u003efor which an acceptable answer is\u003c/p\u003e\u003cpre\u003e ladder = [757; 157; 137; 139]\u003c/pre\u003e\u003cp\u003eYou can assume that p1 and p2 contain the same number of digits. I am not looking for a unique answer. I will only check that the conditions of a prime ladder are met.\u003c/p\u003e","function_template":"function ladder = prime_ladder(p1,p2)\r\n ladder = 0;\r\nend","test_suite":"%%\r\n\r\np1 = 13;\r\np2 = 29;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 389;\r\np2 = 269;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 761;\r\np2 = 397;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 983;\r\np2 = 239;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 271;\r\np2 = 439;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 877;\r\np2 = 733;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n\r\n%%\r\n\r\np1 = 2267;\r\np2 = 1153;\r\nladder = prime_ladder(p1,p2);\r\n\r\nassert(all(isprime(ladder)))\r\nassert(iscolumn(ladder))\r\nassert(ladder(1)==p1)\r\nassert(ladder(end)==p2)\r\nassert(all(sum(diff(num2str(ladder))~=0,2)==1))\r\n","published":true,"deleted":false,"likes_count":10,"comments_count":3,"created_by":7,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":55,"test_suite_updated_at":"2013-03-27T21:24:26.000Z","rescore_all_solutions":false,"group_id":1,"created_at":"2013-03-26T22:51:16.000Z","updated_at":"2026-01-03T14:28:57.000Z","published_at":"2013-03-27T15:28:59.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"http://en.wikipedia.org/wiki/Word_ladder\\\"\u003e\u003cw:r\u003e\u003cw:t\u003eword ladder\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e transforms one word to another by means of single-letter mutations. So COLD can become WARM like so (there are often multiple solutions):\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ COLD\\n CORD\\n CARD\\n WARD\\n WARM]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eA number ladder does much the same thing, changing one digit at a time. A\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eprime ladder\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e is a number ladder with the additional constraint that each element is a prime number. Here is a prime ladder that connects 757 and 139\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ 757 \\n 157\\n 137\\n 139]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eGiven two numbers p1 and p2, construct a prime ladder column vector in which p1 is the first element, p2 is the last element, and each successive row differs by exactly one digit from the preceding element.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eTo restate the above example, consider\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ p1 = 757\\n p2 = 139]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003efor which an acceptable answer is\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ ladder = [757; 157; 137; 139]]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eYou can assume that p1 and p2 contain the same number of digits. I am not looking for a unique answer. I will only check that the conditions of a prime ladder are met.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":1885,"title":"Minimum Sum thru a Lower Triangle","description":"This Challenge is to find the minimum cumulative sum that traverses from row-1 thru row-N via vertical/diagonal adjacent elements of adjacent rows.\r\n\r\nThe input is a series of values of length n*(n+1)/2.\r\n\r\n*Input:* S [Series that can be converted into a lower triangle]\r\n\r\n*Output:* MinSum [Minimum cost path from top to bottom]\r\n\r\n*Example:*\r\n\r\n[5 7 6 3 2 5] becomes\r\n\r\n 5 0 0\r\n 7 6 0\r\n 3 2 5\r\n\r\nCreates a MinSum of 13 [5+6+2]. The 5 can only see 6, 3 sees 7 and 6, while 2 sees 7 6 0.\r\n ","description_html":"\u003cp\u003eThis Challenge is to find the minimum cumulative sum that traverses from row-1 thru row-N via vertical/diagonal adjacent elements of adjacent rows.\u003c/p\u003e\u003cp\u003eThe input is a series of values of length n*(n+1)/2.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInput:\u003c/b\u003e S [Series that can be converted into a lower triangle]\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutput:\u003c/b\u003e MinSum [Minimum cost path from top to bottom]\u003c/p\u003e\u003cp\u003e\u003cb\u003eExample:\u003c/b\u003e\u003c/p\u003e\u003cp\u003e[5 7 6 3 2 5] becomes\u003c/p\u003e\u003cpre class=\"language-matlab\"\u003e5 0 0\r\n7 6 0\r\n3 2 5\r\n\u003c/pre\u003e\u003cp\u003eCreates a MinSum of 13 [5+6+2]. The 5 can only see 6, 3 sees 7 and 6, while 2 sees 7 6 0.\u003c/p\u003e","function_template":"function MinSum=Find_MinSum(s)\r\n MinSum=0;\r\nend","test_suite":"%%\r\ns=[5 7 6 3 2 5];\r\nMinSum=Find_MinSum(s);\r\nexp=13;\r\nassert(exp==MinSum)\r\n%%\r\ns=[7 9 8 3 2 6 7 1 5 5 9 4 8 2 4 6 3 2 9 7 5 7 2 4 8 5 1 9 4 2 9 3 8 5 2 8 8 2 4 8 5 9 2 7 3];\r\nMinSum=Find_MinSum(s);\r\nexp=30;\r\nassert(exp==MinSum)\r\n%%\r\ns=[21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1];\r\nMinSum=Find_MinSum(s);\r\nexp=76;\r\nassert(exp==MinSum)\r\n%%\r\ns=1:28;\r\nMinSum=Find_MinSum(s);\r\nexp=63;\r\nassert(exp==MinSum)\r\n%%\r\ns=[82 91 13 92 64 10 28 55 96 97 16 98 96 49 81 15 43 92 80 96 66 4 85 94 68 76 75 40 66 18 71 4 28 5 10 83 ];\r\nMinSum=Find_MinSum(s);\r\nexp=213;\r\nassert(exp==MinSum)\r\n%%\r\ns=[348 159 476 18 220 191 383 398 94 245 223 324 355 378 139 340 328 82 60 250 480 171 293 112 376 128 253 350 446 480 274 70 75 129 421 128 ];\r\nMinSum=Find_MinSum(s);\r\nexp=1409;\r\nassert(exp==MinSum)\r\n\r\n\r\n","published":true,"deleted":false,"likes_count":0,"comments_count":0,"created_by":3097,"edited_by":null,"edited_at":null,"deleted_by":null,"deleted_at":null,"solvers_count":15,"test_suite_updated_at":null,"rescore_all_solutions":false,"group_id":1,"created_at":"2013-09-21T02:45:50.000Z","updated_at":"2026-04-08T08:33:22.000Z","published_at":"2013-09-21T03:00:18.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"relationships\":[{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/document\",\"targetMode\":\"\",\"relationshipId\":\"rId1\",\"target\":\"/matlab/document.xml\"},{\"relationshipType\":\"http://schemas.mathworks.com/matlab/code/2013/relationships/output\",\"targetMode\":\"\",\"relationshipId\":\"rId2\",\"target\":\"/matlab/output.xml\"}],\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"relationship\":[],\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\\n\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis Challenge is to find the minimum cumulative sum that traverses from row-1 thru row-N via vertical/diagonal adjacent elements of adjacent rows.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe input is a series of values of length n*(n+1)/2.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eInput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e S [Series that can be converted into a lower triangle]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eOutput:\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e MinSum [Minimum cost path from top to bottom]\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:rPr\u003e\u003cw:b/\u003e\u003c/w:rPr\u003e\u003cw:t\u003eExample:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e[5 7 6 3 2 5] becomes\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[5 0 0\\n7 6 0\\n3 2 5]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eCreates a MinSum of 13 [5+6+2]. The 5 can only see 6, 3 sees 7 and 6, while 2 sees 7 6 0.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003c/w:body\u003e\u003c/w:document\u003e\"},{\"partUri\":\"/matlab/output.xml\",\"contentType\":\"text/xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\" standalone=\\\"no\\\" ?\u003e\u003cembeddedOutputs\u003e\u003cmetaData\u003e\u003cevaluationState\u003emanual\u003c/evaluationState\u003e\u003clayoutState\u003ecode\u003c/layoutState\u003e\u003coutputStatus\u003eready\u003c/outputStatus\u003e\u003c/metaData\u003e\u003coutputArray type=\\\"array\\\"/\u003e\u003cregionArray type=\\\"array\\\"/\u003e\u003c/embeddedOutputs\u003e\"}]}"},{"id":93,"title":"Calculate the Levenshtein distance between two strings","description":"This problem description is lifted from http://en.wikipedia.org/wiki/Levenshtein_distance.\r\nThe Levenshtein distance between two strings is defined as the minimum number of edits needed to transform one string into the other, with the allowable edit operations being insertion, deletion, or substitution of a single character.\r\nFor example, the Levenshtein distance between \"kitten\" and \"sitting\" is 3, since the following three edits change one into the other, and there is no way to do it with fewer than three edits:\r\n kitten =\u003e sitten (substitution of 's' for 'k')\r\n sitten =\u003e sittin (substitution of 'e' for 'i')\r\n sittin =\u003e sitting (insert 'g' at the end).\r\nSo when\r\n s1 = 'kitten'\r\nand\r\n s2 = 'sitting'\r\nthen the distance d is equal to 3.","description_html":"\u003cdiv style = \"text-align: start; line-height: 20.4333px; min-height: 0px; white-space: normal; color: rgb(0, 0, 0); font-family: Menlo, Monaco, Consolas, monospace; font-style: normal; font-size: 14px; font-weight: 400; text-decoration: rgb(0, 0, 0); white-space: normal; \"\u003e\u003cdiv style=\"block-size: 348.167px; display: block; min-width: 0px; padding-block-start: 0px; padding-top: 0px; perspective-origin: 407px 174.083px; transform-origin: 407px 174.083px; vertical-align: baseline; \"\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 117.5px 8px; transform-origin: 117.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThis problem description is lifted from\u003c/span\u003e\u003c/span\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e \u003c/span\u003e\u003c/span\u003e\u003ca target='_blank' href = \"https://en.wikipedia.org/wiki/Levenshtein_distance\"\u003e\u003cspan style=\"\"\u003e\u003cspan style=\"\"\u003ehttp://en.wikipedia.org/wiki/Levenshtein_distance\u003c/span\u003e\u003c/span\u003e\u003c/a\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 2px 8px; transform-origin: 2px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003e.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 381.5px 8px; transform-origin: 381.5px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eThe Levenshtein distance between two strings is defined as the minimum number of edits needed to transform one string into the other, with the allowable edit operations being insertion, deletion, or substitution of a single character.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 42px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 2px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 2px; perspective-origin: 384px 21px; text-align: left; transform-origin: 384px 21px; white-space: pre-wrap; margin-left: 4px; margin-top: 2px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 379px 8px; transform-origin: 379px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eFor example, the Levenshtein distance between \"kitten\" and \"sitting\" is 3, since the following three edits change one into the other, and there is no way to do it with fewer than three edits:\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 61.3px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 30.65px; transform-origin: 404px 30.65px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 192px 8.5px; tab-size: 4; transform-origin: 192px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 144px 8.5px; transform-origin: 144px 8.5px; \"\u003e kitten =\u0026gt; sitten (substitution of \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003e's' \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); perspective-origin: 16px 8.5px; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); transform-origin: 16px 8.5px; \"\u003efor \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 12px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 12px 8.5px; \"\u003e'k'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 192px 8.5px; tab-size: 4; transform-origin: 192px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 144px 8.5px; transform-origin: 144px 8.5px; \"\u003e sitten =\u0026gt; sittin (substitution of \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003e'e' \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(14, 0, 255); border-block-start-color: rgb(14, 0, 255); border-bottom-color: rgb(14, 0, 255); border-inline-end-color: rgb(14, 0, 255); border-inline-start-color: rgb(14, 0, 255); border-left-color: rgb(14, 0, 255); border-right-color: rgb(14, 0, 255); border-top-color: rgb(14, 0, 255); caret-color: rgb(14, 0, 255); color: rgb(14, 0, 255); column-rule-color: rgb(14, 0, 255); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(14, 0, 255); perspective-origin: 16px 8.5px; text-decoration-color: rgb(14, 0, 255); text-emphasis-color: rgb(14, 0, 255); transform-origin: 16px 8.5px; \"\u003efor \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 12px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 12px 8.5px; \"\u003e'i'\u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 4px 8.5px; transform-origin: 4px 8.5px; \"\u003e)\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); block-size: 20.4333px; border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 172px 8.5px; tab-size: 4; transform-origin: 172px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 108px 8.5px; transform-origin: 108px 8.5px; \"\u003e sittin =\u0026gt; sitting (insert \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 16px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 16px 8.5px; \"\u003e'g' \u003c/span\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 48px 8.5px; transform-origin: 48px 8.5px; \"\u003eat the end).\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 27px 8px; transform-origin: 27px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eSo when\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 56px 8.5px; tab-size: 4; transform-origin: 56px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 24px 8.5px; transform-origin: 24px 8.5px; \"\u003e s1 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 32px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 32px 8.5px; \"\u003e'kitten'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 12px 8px; transform-origin: 12px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003eand\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003cdiv style=\"background-color: rgb(247, 247, 247); block-size: 20.4333px; border-bottom-left-radius: 4px; border-bottom-right-radius: 4px; border-end-end-radius: 4px; border-end-start-radius: 4px; border-start-end-radius: 4px; border-start-start-radius: 4px; border-top-left-radius: 4px; border-top-right-radius: 4px; margin-block-end: 10px; margin-block-start: 10px; margin-bottom: 10px; margin-inline-end: 3px; margin-inline-start: 3px; margin-left: 3px; margin-right: 3px; margin-top: 10px; perspective-origin: 404px 10.2167px; transform-origin: 404px 10.2167px; margin-left: 3px; margin-top: 10px; margin-bottom: 10px; margin-right: 3px; \"\u003e\u003cdiv style=\"background-color: rgba(0, 0, 0, 0); border-bottom-left-radius: 0px; border-bottom-right-radius: 0px; border-end-end-radius: 0px; border-end-start-radius: 0px; border-inline-end-color: rgb(233, 233, 233); border-inline-end-style: solid; border-inline-end-width: 1px; border-inline-start-color: rgb(233, 233, 233); border-inline-start-style: solid; border-inline-start-width: 1px; border-left-color: rgb(233, 233, 233); border-left-style: solid; border-left-width: 1px; border-right-color: rgb(233, 233, 233); border-right-style: solid; border-right-width: 1px; border-start-end-radius: 0px; border-start-start-radius: 0px; border-top-left-radius: 0px; border-top-right-radius: 0px; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; min-block-size: 18px; min-height: 18px; padding-inline-start: 4px; padding-left: 4px; white-space: nowrap; \"\u003e\u003cspan style=\"block-size: auto; border-inline-end-color: rgb(0, 0, 0); border-inline-end-style: none; border-inline-end-width: 0px; border-inline-start-color: rgb(0, 0, 0); border-inline-start-style: none; border-inline-start-width: 0px; border-left-color: rgb(0, 0, 0); border-left-style: none; border-left-width: 0px; border-right-color: rgb(0, 0, 0); border-right-style: none; border-right-width: 0px; display: inline; margin-inline-end: 45px; margin-right: 45px; min-block-size: 0px; min-height: 0px; padding-inline-start: 0px; padding-left: 0px; perspective-origin: 60px 8.5px; tab-size: 4; transform-origin: 60px 8.5px; unicode-bidi: normal; white-space: pre; margin-right: 45px; \"\u003e\u003cspan style=\"margin-inline-end: 0px; margin-right: 0px; perspective-origin: 24px 8.5px; transform-origin: 24px 8.5px; \"\u003e s2 = \u003c/span\u003e\u003cspan style=\"border-block-end-color: rgb(170, 4, 249); border-block-start-color: rgb(170, 4, 249); border-bottom-color: rgb(170, 4, 249); border-inline-end-color: rgb(170, 4, 249); border-inline-start-color: rgb(170, 4, 249); border-left-color: rgb(170, 4, 249); border-right-color: rgb(170, 4, 249); border-top-color: rgb(170, 4, 249); caret-color: rgb(170, 4, 249); color: rgb(170, 4, 249); column-rule-color: rgb(170, 4, 249); margin-inline-end: 0px; margin-right: 0px; outline-color: rgb(170, 4, 249); perspective-origin: 36px 8.5px; text-decoration-color: rgb(170, 4, 249); text-emphasis-color: rgb(170, 4, 249); transform-origin: 36px 8.5px; \"\u003e'sitting'\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv style=\"block-size: 21px; font-family: Helvetica, Arial, sans-serif; line-height: 21px; margin-block-end: 9px; margin-block-start: 10px; margin-bottom: 9px; margin-inline-end: 10px; margin-inline-start: 4px; margin-left: 4px; margin-right: 10px; margin-top: 10px; perspective-origin: 384px 10.5px; text-align: left; transform-origin: 384px 10.5px; white-space: pre-wrap; margin-left: 4px; margin-top: 10px; margin-bottom: 9px; margin-right: 10px; \"\u003e\u003cspan style=\"block-size: auto; display: inline; margin-block-end: 0px; margin-block-start: 0px; margin-bottom: 0px; margin-inline-end: 0px; margin-inline-start: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; perspective-origin: 103px 8px; transform-origin: 103px 8px; unicode-bidi: normal; \"\u003e\u003cspan style=\"\"\u003ethen the distance d is equal to 3.\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e","function_template":"function d = levenshtein(s1,s2)\r\n d = 0;\r\nend","test_suite":"%%\r\nfiletext = fileread('levenshtein.m');\r\nassert(isempty(strfind(filetext, 'switch')))\r\nassert(isempty(strfind(filetext, 'elseif')))\r\n\r\n\r\n%%\r\n\r\ns1 = 'kitten';\r\ns2 = 'sitting';\r\nd_correct = 3;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%% \r\n\r\ns1 = 'Saturday';\r\ns2 = 'Sunday';\r\nd_correct = 3;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%% \r\n\r\ns1 = 'MATLAB rocks!';\r\ns2 = 'MathWorks';\r\nd_correct = 9;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%% \r\n\r\ns1 = 'Four score and seven years ago';\r\ns2 = 'Eighty seven years before today';\r\nd_correct = 25;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%% \r\n\r\ns1 = 'Row row row your boat';\r\ns2 = 'Gently down the stream';\r\nd_correct = 18;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%%\r\n\r\ns1 = 'ninety-nine bottles of beer on the wall';\r\ns2 = 'eighty-six bottles of beer on the wall';\r\nd_correct = 6;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%%\r\n\r\ns1 = 'these are the times that try men''s souls';\r\ns2 = 'soulwise, these are trying times';\r\nd_correct = 27;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%%\r\n\r\ns1 = 'abc';\r\ns2 = 'abc';\r\nd_correct = 0;\r\nassert(isequal(levenshtein(s1,s2),d_correct))\r\n\r\n%%\r\n\r\ns1 = 'god';\r\ns2 = 'dog';\r\nd_correct = 2;\r\nassert(isequal(levenshtein(s1,s2),d_correct))","published":true,"deleted":false,"likes_count":25,"comments_count":3,"created_by":1,"edited_by":223089,"edited_at":"2022-11-28T06:34:46.000Z","deleted_by":null,"deleted_at":null,"solvers_count":1507,"test_suite_updated_at":"2022-11-28T06:34:46.000Z","rescore_all_solutions":false,"group_id":2,"created_at":"2012-01-18T01:00:30.000Z","updated_at":"2026-03-02T01:10:11.000Z","published_at":"2012-01-18T01:00:30.000Z","restored_at":null,"restored_by":null,"spam":false,"simulink":false,"admin_reviewed":false,"description_opc":"{\"parts\":[{\"partUri\":\"/matlab/document.xml\",\"contentType\":\"application/vnd.mathworks.matlab.code.document+xml\",\"content\":\"\u003c?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?\u003e\u003cw:document xmlns:w=\\\"http://schemas.openxmlformats.org/wordprocessingml/2006/main\\\"\u003e\u003cw:body\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThis problem description is lifted from\u003c/w:t\u003e\u003c/w:r\u003e\u003cw:r\u003e\u003cw:t\u003e \u003c/w:t\u003e\u003c/w:r\u003e\u003cw:hyperlink w:docLocation=\\\"https://en.wikipedia.org/wiki/Levenshtein_distance\\\"\u003e\u003cw:r\u003e\u003cw:t\u003ehttp://en.wikipedia.org/wiki/Levenshtein_distance\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:hyperlink\u003e\u003cw:r\u003e\u003cw:t\u003e.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eThe Levenshtein distance between two strings is defined as the minimum number of edits needed to transform one string into the other, with the allowable edit operations being insertion, deletion, or substitution of a single character.\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eFor example, the Levenshtein distance between \\\"kitten\\\" and \\\"sitting\\\" is 3, since the following three edits change one into the other, and there is no way to do it with fewer than three edits:\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ kitten =\u003e sitten (substitution of 's' for 'k')\\n sitten =\u003e sittin (substitution of 'e' for 'i')\\n sittin =\u003e sitting (insert 'g' at the end).]]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eSo when\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ s1 = 'kitten']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003eand\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"code\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003e\u003c![CDATA[ s2 = 'sitting']]\u003e\u003c/w:t\u003e\u003c/w:r\u003e\u003c/w:p\u003e\u003cw:p\u003e\u003cw:pPr\u003e\u003cw:pStyle w:val=\\\"text\\\"/\u003e\u003cw:jc w:val=\\\"left\\\"/\u003e\u003c/w:pPr\u003e\u003cw:r\u003e\u003cw:t\u003ethen the distance d is equal to 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