- Initialization: Start with an initial design, which can be randomly selected or based on some heuristic.
- Exchange Process: Iteratively exchange rows between the current design and a candidate set to improve the design's optimality.
- Convergence: Continue exchanging until no further improvement can be made, or until a specified number of iterations is reached.
Which algorithm type does rowexch use?
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Hi, I looked through the documentation, but couldn't find an answer:
Which type of algorithm does 'rowexch' use?
- The Federov algorithm
- The Modified Federov Algorithm
- The K-Exchange Algorithm
- Or yet another algorithm?
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Aditya
el 3 de Feb. de 2025
Hi Tobias,
The 'rowexch' function in MATLAB is used for generating D-optimal designs, which are a type of experimental design. The algorithm behind 'rowexch' is based on the Modified Federov Algorithm. This algorithm iteratively improves the design by exchanging rows in a candidate set to maximize the determinant of the information matrix, which is the criterion for D-optimality.
Here's a brief overview of how the Modified Federov Algorithm works:
The Modified Federov Algorithm is particularly suited for handling large candidate sets and is widely used for generating optimal experimental designs.
Refer the following documentation for more details.
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