Writing equations in a matrix form

Hi MATLAB experts,

Could any one please help me to write-down the following equations into a matrix form? the initial value of c = zeros(I, L, K, M)

0<= c(i, j, k, m) <= 1 for all k= {1, 2, ...., K} and m = {1, 2, ..., M} and i = {1,...., I} and j = {1,..., L}

0<=sum_{j} sum_{i} c(i,j,k,m) <= 1 for all k= {1, 2, ...., K} and m = {1, 2, ..., M}

[Walter Roberson]

The first set of conditions you would normally express through lb / ub matrices, unless you were using Problem Based Optimization.

[Susan]

Thanks for your reply.

Do you mean I will have

lb = zeros(I,L, K, M) and ub = ones(I,L, K, M)

for the first set of conditions?

Can I write the second set of conditions as follows

sum(sum(c(:,:,:,:))) - 1 <= 0
nonlcon=@(c) deal([],sum(sum(c(:,:,:,:))) -1);

Is it correct? Is this constraint a linear or nonlinear?

[Walter Roberson]

Yes, those should be okay lb and ub.

Which release are you using? Which optimizer are you using?

Your task might be easier to express with Problem Based Optimization.

Do not use nonlinear constraints for those sum constraints: you only need linear constraints for those. It is just a nuisance to write out the matrices.

[Susan]

Thanks for your reply.

I am using R2016b and trying to use fmincom.

I am not familiar with Problem Based Optimization. But I will take a look to see how I can use that.

Do you know how I should write the second constraint? Thanks

sum(sum(c(:, :, k,m))) <= 1 for all k and m.

Answer 1

[Walter Roberson]

For any one particular scalar k and scalar m, you can express sum_{j} sum_{i} c(i,j,k,m) in range 0 to 1 as a linear constraint. The portion relevant to that k and m would be in the A matrix like

A(something,:) = [zeros(1, SOMETHING), ones(1, I*J), zeros(1,SOMETHINGELSE)];
b(something) = 1;
A(something+1,:) = [zeros(1, SOMETHING), -ones(1,I*J), zeros(1,SOMETHINGELSE)];
b(something) = 0;

You would have to walk this through K by M iterations, increasing the value of SOMETHING by I*J each time, and decreasing the value of SOMETHINGELSE by the same value.

An easier way of generating this would be something like:

blk = repmat({[ones(1, I*J); -ones(1, I*J)]}, 1, K*M);
A = blkdiag(blk{:});
b = zeros(size(A,1));
b(1:2:end) = 1;

[Susan]

Thank you very much!!!!

[Susan]

Just a quick question, in this format what would be the form of c then? A*c <= b should hold, rigth? Thanks in advance

[Walter Roberson]

x=c(:);

A*x<=b

[Susan]

Thank you very much!

[Susan]

I have got another question. Your reply would be greatly appreciated.

I define the objective function as follows

syms c
f{i, j, k, m} = ps(j, k)*c(i, j, k, m)*B(k,m)*log2(1 + (Ass(i, j, k, m)*SINR{i, j, k, m})/(ps(j, k)*c(i, j, k, m)*B(k,m))); (for all i, j, k, m)

and I have got the following errors:

Error using sub2ind (line 43)

Out of range subscript.

Error in sym/subsref (line 809)

R_tilde = sub2ind(size(L), Idx.subs{:});

The objective function works fine for c0 but I don't get the issue when I have "syms c". Any idea?

Thanks in advance

[Susan]

I guess I found the issue. I defind the c as a matrix and it works. Do you think it was the issue or there is something else that I am not aware of ? Thanks.

BTW, I define the objfun as

objfun = @(c) f(c) 

but I get the following error in the line that I called objfun i.e.,

[x,fval]=fmincon('objfun',c0,A,b,[],[],lb,ub);

Undefined function or variable 'objfun'.

Error in fmincon (line 536)

initVals.f = feval(funfcn{3},X,varargin{:});

Any idea?

[Walter Roberson]

Do not use a quoted string for the first argument of fmincon. Always pass it a function handle instead.

(By the time you know enough MATLAB to know when this rule can be safely violated, you will not want to violate it and will dislike that you have to violate it.)

[x, fval] = fmincon(@f, c0, A, b, [], [], lb, ub)

[Susan]

Thanks for your reply.

I am lost.

If I call [x, fval] = fmincon(@f, c0, A, b, [], [], lb, ub) I got the following error:

Not enough input arguments.

Error in CalculateLAASumRate (line 29)

for k = 1 : numel(nbrOfSubCarriers)

Error in fmincon (line 536)

initVals.f = feval(funfcn{3},X,varargin{:});

Error in test_Lagrangian (line 217)

c=fmincon(@f,c0,A,b,[],[],lb,ub);

Caused by:

Failure in initial objective function evaluation. FMINCON cannot continue.

And when I try

objfun = @(c) f(c) %==> objfun is a function_handle

[x, fval] = fmincon(objfun, c0, A, b, [], [], lb, ub)

I get

Error using fmincon (line 684)

FMINCON requires all values returned by functions to be of data type double.

Error in test_Lagrangian (line 217)

c=fmincon(objfun,c0,A,b,[],[],lb,ub);

[Walter Roberson]

I am lost as to what your f is. Above you defined syms c and the appear to assign to a particular cell array location in f based on indexing c, which was going to fail because syms c defines a scalar symbol and indexing a scalar symbol anywhere other than offset 1 will fail. But then you defined c as a matrix. Okay, but what is the definition of f now?

[Susan]

Thanks for your reply.

This is what I am doing:

c = sym('c', [I J K M]);
for k = 1 : K
    for m = 1 : M
        for j = 1 : J
            for i = 1 : I
f{i, j, k, m} = ps(j, k)*c(i, j, k, m)*B(k,m)*log2(1 + (Ass(i, j, k, m)*SINR{i, j, k, m})/(ps(j, k)*c(i, j, k, m)*B(k,m))); 
end
end
end
end

function[sumr] = Calculatesumr(I, M, K, J, tr_prob, Ta, Pf, f, NumUEPerOperator)
sumra = 0;
for k = 1 : K
    for m = 1 : M
        for j = 1 : J
            for i = 1 : I
                sumra = sumra + ((1 - tr_prob(k))*(1 - Pf(k))*f{i + NumUEPerOperator*(j - 1), j, k, m} )/Ta(k);
            end
        end
    end
end
sumr = sumra;
end

objfun = @(c) Calculatesumr(I, M, K, J, tr_prob, Ta, Pf, f, NumUEPerOperator)

[Walter Roberson]

At the moment it is not obvious to me why you are using a cell array for f, instead of a sym array of size I J K M, such as

f = zeros(I, J, K, M, 'sym');

Your f is defined in terms of the symbolic array c, so your result of Calculatesumr() is going to be in terms of the variables in the symbolic array c because it involves f and f is defined in terms of the symbolic array c.

You define objfun in terms of a dummy numeric parameter c that will be ignored, and then Calculatesumr() will be called, returning an expression in terms of the symbolic variables c3_2_5_4, c2_1_4_3, and so on (what was in the symbolic array c.) fmincon() will then complain because the result is not numeric.

When you define a parameter named c to a function, and the function involves symbolic variables that were originally stored in a variable named c, then there is no connection made between the parameter and the symbolic variables -- not unless you deliberately subs() the parameters in.

What you should probably be doing is using matlabFunction() with the 'vars' parameter to convert the symbolic expression resulting from Calculatesumr into an anonymous function (taking numeric inputs) that can be called by fmincon()

[Susan]

Thanks again Walter for your detailed explanation.

I am using cell array for f because I have

for k = 1 : K
    for m = 1 : M(k) %% size(M) = 1*K
        for j = 1 : J
            for i = 1 : I(j) %% size(I) = 1* J
                f = ....
            end
        end
    end
end

I don't know if in this case I can use sym array insted of cell array. Can I?

Regarding the fmincon function, I get to take the following steps, right?

ht = matlabFunction(Calculatesumr , 'Vars', c)
[x, fval] = fmincon(@ht, c0, A, b, [], [], lb, ub)

I am not sure about the 'Vars' parameter though. Please put me right if I am wrong. Thank you very much.

[Susan]

Could you please tell me whether I am using the matlabFunction() and fmincon() correctly or not? Thanks

objfun_symbExpression = Calculatesumr(I, M, K, J, tr_prob, Ta, Pf, f, NumUEPerOperator);
objfun_anonymousfunction  = matlabFunction(objfun_symbExpression, 'Vars', c);
objfun = @( c ) objfun_anonymousfunction(c);
[x,fval]=fmincon(objfun,c0,A,b,[],[],lb,ub);

I am getting the following error

Not enough input arguments.

Error in

symengine>@(c1_1_1_1,c2_1_1_1,c3_1_1_1,c1_2_1_1,c2_2_1_1,c3_2_1_1,c1_3_1_1,c2_3_1_1,c3_3_1_1,c1_1_2_1,c2_1_2_1,c3_1_2_1,c1_2_2_1,c2_2_2_1,c3_2_2_1,c1_3_2_1,c2_3_2_1,c3_3_2_1,c1_1_3_1,c2_1_3_1,c3_1_3_1,c1_2_3_1,c2_2_3_1,c3_2_3_1,c1_3_3_1,c2_3_3_1,c3_3_3_1,c1_1_4_1,c2_1_4_1,c3_1_4_1,c1_2_4_1,c2_2_4_1,c3_2_4_1,c1_3_4_1,c2_3_4_1,c3_3_4_1,c1_1_5_1,c2_1_5_1,c3_1_5_1,c1_2_5_1,c2_2_5_1,c3_2_5_1,c1_3_5_1,c2_3_5_1,c3_3_5_1,c1_1_6_1,c2_1_6_1,c3_1_6_1,c1_2_6_1,c2_2_6_1,c3_2_6_1,c1_3_6_1,c2_3_6_1,c3_3_6_1,c1_1_7_1,c2_1_7_1,c3_1_7_1,c1_2_7_1,c2_2_7_1,c3_2_7_1,c1_3_7_1,c2_3_7_1,c3_3_7_1,c1_1_8_1,c2_1_8_1,c3_1_8_1,c1_2_8_1,c2_2_8_1,c3_2_8_1,c1_3_8_1,c2_3_8_1,c3_3_8_1,c1_1_9_1,c2_1_9_1,c3_1_9_1,c1_2_9_1,c2_2_9_1,c3_2_9_1,c1_3_9_1,c2_3_9_1,c3_3_9_1,c1_1_10_1,c2_1_10_1,c3_1_10_1,c1_2_10_1,c2_2_10_1,c3_2_10_1,c1_3_10_1,c2_3_10_1,c3_3_10_1,c1_1_1_2,c2_1_1_2,c3_1_1_2,c1_2_1_2,c2_2_1_2,c3_2_1_2,c1_3_1_2,c2_3_1_2,c3_3_1_2,c1_1_2_2,c2_1_2_2,c3_1_2_2,c1_2_2_2,c2_2_2_2,c3_2_2_2,c1_3_2_2,c2_3_2_2,c3_3_2_2,c1_1_3_2,c2_1_3_2,c3_1_3_2,c1_2_3_2,c2_2_3_2,c3_2_3_2,c1_3_3_2,c2_3_3_2,c3_3_3_2,c1_1_4_2,c2_1_4_2,c3_1_4_2,c1_2_4_2,c2_2_4_2,c3_2_4_2,c1_3_4_2,c2_3_4_2,c3_3_4_2,c1_1_5_2,c2_1_5_2,c3_1_5_2,c1_2_5_2,c2_2_5_2,c3_2_5_2,c1_3_5_2,c2_3_5_2,c3_3_5_2,c1_1_6_2,c2_1_6_2,c3_1_6_2,c1_2_6_2,c2_2_6_2,c3_2_6_2,c1_3_6_2,c2_3_6_2,c3_3_6_2,c1_1_7_2,c2_1_7_2,c3_1_7_2,c1_2_7_2,c2_2_7_2,c3_2_7_2,c1_3_7_2,c2_3_7_2,c3_3_7_2,c1_1_8_2,c2_1_8_2,c3_1_8_2,c1_2_8_2,c2_2_8_2,c3_2_8_2,c1_3_8_2,c2_3_8_2,c3_3_8_2,c1_1_9_2,c2_1_9_2,c3_1_9_2,c1_2_9_2,c2_2_9_2,c3_2_9_2,c1_3_9_2,c2_3_9_2,c3_3_9_2,c1_1_10_2,c2_1_10_2,c3_1_10_2,c1_2_10_2,c2_2_10_2,c3_2_10_2,c1_3_10_2,c2_3_10_2,c3_3_10_2)(c2_1_3_2.*log(4.661334020125402e-5./c2_1_3_2+1.0).*2.700000000000003)./log(2.0)+(c1_2_9_2.*log(1.213086237114686e-4./c1_2_9_2+1.0).*2.700000000000003)./log(2.0)+(c2_2_3_1.*log(1.973717331697037e-4./c2_2_3_1+1.0).*6.27e1)./log(2.0)+(c2_1_2_1.*log(2.384204117902418e-4./c2_1_2_1+1.0).*2.700000000000003)./log(2.0)+(c2_2_8_1.*log(2.04206324308796e-5./c2_2_8_1+1.0).*6.27e1)./log(2.0)+(c2_2_4_2.*log(5.729097402850286e-4./c2_2_4_2+1.0).*6.27e1)./log(2.0)+(c3_3_2_2.*log(5.557877214371519e-5./c3_3_2_2+1.0).*2.700000000000003)./log(2.0)+(c2_3_2_1.*log(4.37974363291977e-5./c2_3_2_1+1.0).*6.27e1)./log(2.0)+(c3_2_5_2.*log(7.522069025134307e-4./c3_2_5_2+1.0).*6.27e1)./log(2.0)+(c2_3_8_2.*log(3.200131464345081e-4./c2_3_8_2+1.0).*2.700000000000003)./log(2.0)+(c1_3_2_2.*log(3.969794324835312e-5./c1_3_2_2+1.0).*6.27e1)./log(2.0)+(c3_2_1_2.*log(4.595366595148829e-4./c3_2_1_2+1.0).*6.27e1)./log(2.0)+(c2_1_7_2.*log(4.729830018171281e-5./c2_1_7_2+1.0).*2.700000000000003)./log(2.0)+(c2_3_4_2.*log(3.553923526702777e-4./c2_3_4_2+1.0).*2.700000000000003)./log(2.0)+(c3_3_4_2.*log(6.73672598667724e-5./c3_3_4_2+1.0).*2.700000000000003)./log(2.0)+(c3_3_4_2.*log(6.736725986695873e-5./c3_3_4_2+1.0).*6.27e1)./log(2.0)+(c2_1_6_2.*log(3.175894054653654e-5./c2_1_6_2+1.0).*2.700000000000003)./log(2.0)+(c3_1_7_2.*log(5.594025491556619e-5./c3_1_7_2+1.0).*2.700000000000003)./log(2.0)+(c3_2_8_1.*log(1.958377898353446e-4./c3_2_8_1+1.0).*6.27e1)./log(2.0)+(c2_2_3_1.*log(1.97370972985957e-4./c2_2_3_1+1.0).*2.700000000000003)./log(2.0)+(c2_1_8_1.*log(9.076779160220973e-5./c2_1_8_1+1.0).*6.27e1)./log(2.0)+(c1_3_9_2.*log(2.008761598043622e-5./c1_3_9_2+1.0).*2.700000000000003)./log(2.0)+(c1_3_6_1.*log(5.912517056203199e-5./c1_3_6_1+1.0).*6.27e1)./log(2.0)+(c2_1_1_1.*log(4.469878063836057e-5./c2_1_1_1+1.0).*6.27e1)./log(2.0)+(c3_2_9_2.*log(4.984847383089513e-6./c3_2_9_2+1.0).*2.700000000000003)./log(2.0)+(c2_2_9_2.*log(8.485904363436042e-4./c2_2_9_2+1.0).*2.700000000000003)./log(2.0)+(c2_3_4_2.*log(3.553923531715167e-4./c2_3_4_2+1.0).*6.27e1)./log(2.0)+(c3_1_3_1.*log(5.688006180694598e-4./c3_1_3_1+1.0).*2.700000000000003)./log(2.0)+(c3_2_9_2.*log(4.984849236683792e-6./c3_2_9_2+1.0).*6.27e1)./log(2.0)+(c3_3_6_1.*log(1.456764732041037e-4./c3_3_6_1+1.0).*2.700000000000003)./log(2.0)+(c2_2_6_2.*log(1.698824113637671e-4./c2_2_6_2+1.0).*2.700000000000003)./log(2.0)+(c1_1_6_1.*log(1.428026935615791e-4./c1_1_6_1+1.0).*6.27e1)./log(2.0)+(c1_2_10_1.*log(4.461843721426979e-5./c1_2_10_1+1.0).*6.27e1)./log(2.0)+(c1_2_9_1.*log(2.482724973331773e-4./c1_2_9_1+1.0).*6.27e1)./log(2.0)+(c3_1_2_1.*log(3.472779552279211e-5./c3_1_2_1+1.0).*2.700000000000003)./log(2.0)+(c3_2_7_1.*log(9.256119773541176e-6./c3_2_7_1+1.0).*6.27e1)./log(2.0)+(c1_2_9_1.*log(2.482724155699465e-4./c1_2_9_1+1.0).*2.700000000000003)./log(2.0)+(c3_3_2_2.*log(5.557877214512173e-5./c3_3_2_2+1.0).*6.27e1)./log(2.0)+(c3_1_4_2.*log(1.53543091944615e-4./c3_1_4_2+1.0).*2.700000000000003)./log(2.0)+(c1_1_5_2.*log(1.319447995648612e-4./c1_1_5_2+1.0).*2.700000000000003)./log(2.0)+(c2_3_1_1.*log(1.357167375932804e-5./c2_3_1_1+1.0).*6.27e1)./log(2.0)+(c2_1_9_2.*log(9.440168614601304e-5./c2_1_9_2+1.0).*2.700000000000003)./log(2.0)+(c2_2_5_2.*log(8.432664626625371e-6./c2_2_5_2+1.0).*6.27e1)./log(2.0)+(c1_2_4_2.*log(7.110336694826498e-5./c1_2_4_2+1.0).*2.700000000000003)./log(2.0)+(c3_1_8_1.*log(8.096486953587943e-5./c3_1_8_1+1.0).*2.700000000000003)./log(2.0)+(c1_3_7_1.*log(1.405180023044887e-4./c1_3_7_1+1.0).*6.27e1)./log(2.0)+(c2_1_9_2.*log(9.440174332522934e-5./c2_1_9_2+1.0).*6.27e1)./log(2.0)+(c1_1_7_2.*log(8.569384644204782e-4./c1_1_7_2+1.0).*2.700000000000003)./log(2.0)+(c2_3_2_2.*log(1.000426840357526e-3./c2_3_2_2+1.0).*2.700000000000003)./log(2.0)+(c1_3_6_1.*log(5.912516691563455e-5./c1_3_6_1+1.0).*2.700000000000003)./log(2.0)+(c1_3_4_2.*log(1.538394094554665e-4./c1_3_4_2+1.0).*2.700000000000003)./log(2.0)+(c1_3_8_2.*log(5.921927196211666e-5./c1_3_8_2+1.0).*2.700000000000003)./log(2.0)+(c3_2_8_2.*log(4.025084801366024e-6./c3_2_8_2+1.0).*6.27e1)./log(2.0)+(c2_3_5_1.*log(1.663444628439551e-5./c2_3_5_1+1.0).*2.700000000000003)./log(2.0)+(c1_3_6_2.*log(1.293081749362346e-5./c1_3_6_2+1.0).*2.700000000000003)./log(2.0)+(c3_3_8_1.*log(2.150928013852878e-4./c3_3_8_1+1.0).*6.27e1)./log(2.0)+(c1_2_4_1.*log(1.913709359525297e-4./c1_2_4_1+1.0).*2.700000000000003)./log(2.0)+(c3_1_1_1.*log(4.940630101675829e-4./c3_1_1_1+1.0).*6.27e1)./log(2.0)+(c3_3_3_2.*log(2.284558083424065e-4./c3_3_3_2+1.0).*2.700000000000003)./log(2.0)+(c1_2_10_1.*log(4.461843621269375e-5./c1_2_10_1+1.0).*2.700000000000003)./log(2.0)+(c2_1_7_1.*log(1.113855958188974e-3./c2_1_7_1+1.0).*6.27e1)./log(2.0)+(c1_1_9_1.*log(1.01374617819942e-4./c1_1_9_1+1.0).*2.700000000000003)./log(2.0)+(c3_3_5_2.*log(2.611169342698065e-4./c3_3_5_2+1.0).*2.700000000000003)./log(2.0)+(c3_2_6_1.*log(3.679956897528039e-5./c3_2_6_1+1.0).*6.27e1)./log(2.0)+(c2_1_6_1.*log(9.392761080009399e-5./c2_1_6_1+1.0).*6.27e1)./log(2.0)+(c1_3_4_2.*log(1.538394147938948e-4./c1_3_4_2+1.0).*6.27e1)./log(2.0)+(c2_2_4_1.*log(4.9678277840158e-6./c2_2_4_1+1.0).*2.700000000000003)./log(2.0)+(c2_2_8_1.*log(2.042063040438785e-5./c2_2_8_1+1.0).*2.700000000000003)./log(2.0)+(c1_1_3_2.*log(1.232503168480349e-3./c1_1_3_2+1.0).*2.700000000000003)./log(2.0)+(c3_2_6_2.*log(3.608381738328319e-4./c3_2_6_2+1.0).*6.27e1)./log(2.0)+(c3_2_4_1.*log(5.449377668625627e-4./c3_2_4_1+1.0).*6.27e1)./log(2.0)+(c1_3_2_1.*log(5.187942724293274e-5./c1_3_2_1+1.0).*6.27e1)./log(2.0)+(c2_2_4_2.*log(5.729093566329847e-4./c2_2_4_2+1.0).*2.700000000000003)./log(2.0)+(c2_1_6_1.*log(9.392740737458586e-5./c2_1_6_1+1.0).*2.700000000000003)./log(2.0)+(c1_1_10_2.*log(1.354274790070846e-4./c1_1_10_2+1.0).*2.700000000000003)./log(2.0)+(c3_3_3_1.*log(1.275067422731125e-4./c3_3_3_1+1.0).*6.27e1)./log(2.0)+(c3_3_10_2.*log(7.675482745790704e-4./c3_3_10_2+1.0).*6.27e1)./log(2.0)+(c2_1_2_1.*log(2.384205964792891e-4./c2_1_2_1+1.0).*6.27e1)./log(2.0)+(c3_2_1_2.*log(4.595366095966422e-4./c3_2_1_2+1.0).*2.700000000000003)./log(2.0)+(c1_3_10_1.*log(6.740522878020518e-5./c1_3_10_1+1.0).*2.700000000000003)./log(2.0)+(c3_1_4_1.*log(1.38500161753365e-5./c3_1_4_1+1.0).*6.27e1)./log(2.0)+(c3_3_5_1.*log(1.829910896120699e-4./c3_3_5_1+1.0).*2.700000000000003)./log(2.0)+(c2_2_2_1.*log(7.558763967022339e-5./c2_2_2_1+1.0).*6.27e1)./log(2.0)+(c3_1_9_2.*log(9.967439060958481e-5./c3_1_9_2+1.0).*2.700000000000003)./log(2.0)+(c3_2_2_2.*log(1.228106886125817e-4./c3_2_2_2+1.0).*6.27e1)./log(2.0)+(c3_1_9_2.*log(9.967439090643528e-5./c3_1_9_2+1.0).*6.27e1)./log(2.0)+(c3_2_9_1.*log(1.648898267488357e-4./c3_2_9_1+1.0).*2.700000000000003)./log(2.0)+(c2_2_1_1.*log(1.692176363778982e-4./c2_2_1_1+1.0).*6.27e1)./log(2.0)+(c1_1_8_1.*log(4.780980190502675e-4./c1_1_8_1+1.0).*6.27e1)./log(2.0)+(c2_2_2_1.*log(7.558745437360602e-5./c2_2_2_1+1.0).*2.700000000000003)./log(2.0)+(c3_3_3_1.*log(1.275067422554162e-4./c3_3_3_1+1.0).*2.700000000000003)./log(2.0)+(c1_3_10_1.*log(6.740524271678065e-5./c1_3_10_1+1.0).*6.27e1)./log(2.0)+(c1_3_9_2.*log(2.008763326871702e-5./c1_3_9_2+1.0).*6.27e1)./log(2.0)+(c1_1_5_1.*log(1.537975575114146e-4./c1_1_5_1+1.0).*6.27e1)./log(2.0)+(c3_1_4_1.*log(1.385001615467321e-5./c3_1_4_1+1.0).*2.700000000000003)./log(2.0)+(c3_2_5_2.*log(7.522068922353348e-4./c3_2_5_2+1.0).*2.700000000000003)./log(2.0)+(c1_1_6_1.*log(1.42802488195388e-4./c1_1_6_1+1.0).*2.700000000000003)./log(2.0)+(c1_1_5_2.*log(1.319453892704594e-4./c1_1_5_2+1.0).*6.27e1)./log(2.0)+(c2_3_8_2.*log(3.200131466622681e-4./c2_3_8_2+1.0).*6.27e1)./log(2.0)+(c1_3_7_2.*log(2.120050041519761e-4./c1_3_7_2+1.0).*2.700000000000003)./log(2.0)+(c3_2_3_1.*log(4.834243388523898e-4./c3_2_3_1+1.0).*6.27e1)./log(2.0)+(c1_2_4_1.*log(1.913710149071611e-4./c1_2_4_1+1.0).*6.27e1)./log(2.0)+(c3_2_2_1.*log(6.325185963440156e-5./c3_2_2_1+1.0).*2.700000000000003)./log(2.0)+(c1_2_4_2.*log(7.1103380851198e-5./c1_2_4_2+1.0).*6.27e1)./log(2.0)+(c2_3_6_2.*log(4.126727905476189e-4./c2_3_6_2+1.0).*2.700000000000003)./log(2.0)+(c1_1_8_1.*log(4.780938150051171e-4./c1_1_8_1+1.0).*2.700000000000003)./log(2.0)+(c2_2_9_2.*log(8.4859160226389e-4./c2_2_9_2+1.0).*6.27e1)./log(2.0)+(c1_3_9_1.*log(1.935733550895296e-4./c1_3_9_1+1.0).*6.27e1)./log(2.0)+(c1_1_3_2.*log(1.232522570075739e-3./c1_1_3_2+1.0).*6.27e1)./log(2.0)+(c1_2_9_2.*log(1.213086598553184e-4./c1_2_9_2+1.0).*6.27e1)./log(2.0)+(c3_2_9_1.*log(1.648898281385053e-4./c3_2_9_1+1.0).*6.27e1)./log(2.0)+(c3_1_3_1.*log(5.688006185884601e-4./c3_1_3_1+1.0).*6.27e1)./log(2.0)+(c2_3_5_1.*log(1.663444646315982e-5./c2_3_5_1+1.0).*6.27e1)./log(2.0)+(c1_3_7_1.*log(1.405180013356742e-4./c1_3_7_1+1.0).*2.700000000000003)./log(2.0)+(c2_3_7_2.*log(7.581885797095326e-5./c2_3_7_2+1.0).*2.700000000000003)./log(2.0)+(c1_2_2_1.*log(6.343877445868848e-4./c1_2_2_1+1.0).*6.27e1)./log(2.0)+(c3_3_5_2.*log(2.611169351273466e-4./c3_3_5_2+1.0).*6.27e1)./log(2.0)+(c3_2_6_2.*log(3.608381680908266e-4./c3_2_6_2+1.0).*2.700000000000003)./log(2.0)+(c3_3_6_1.*log(1.456764732211091e-4./c3_3_6_1+1.0).*6.27e1)./log(2.0)+(c2_1_6_2.*log(3.176308680274093e-5./c2_1_6_2+1.0).*6.27e1)./log(2.0)+(c1_1_5_1.*log(1.537972120011483e-4./c1_1_5_1+1.0).*2.700000000000003)./log(2.0)+(c2_1_1_1.*log(4.469876539306762e-5./c2_1_1_1+1.0).*2.700000000000003)./log(2.0)+(c1_2_2_1.*log(6.343875024015616e-4./c1_2_2_1+1.0).*2.700000000000003)./log(2.0)+(c1_3_8_2.*log(5.921927406820357e-5./c1_3_8_2+1.0).*6.27e1)./log(2.0)+(c3_3_3_2.*log(2.284558084755119e-4./c3_3_3_2+1.0).*6.27e1)./log(2.0)+(c3_1_4_2.*log(1.535430942883223e-4./c3_1_4_2+1.0).*6.27e1)./log(2.0)+(c2_3_2_1.*log(4.379743614195532e-5./c2_3_2_1+1.0).*2.700000000000003)./log(2.0)+(c3_2_2_1.*log(6.325185997050446e-5./c3_2_2_1+1.0).*6.27e1)./log(2.0)+(c3_2_4_1.*log(5.44937712788617e-4./c3_2_4_1+1.0).*2.700000000000003)./log(2.0)+(c3_1_7_2.*log(5.594025513949421e-5./c3_1_7_2+1.0).*6.27e1)./log(2.0)+(c2_3_7_2.*log(7.581885818144822e-5./c2_3_7_2+1.0).*6.27e1)./log(2.0)+(c2_2_10_1.*log(3.219742241443294e-5./c2_2_10_1+1.0).*2.700000000000003)./log(2.0)+(c2_2_6_2.*log(1.698824573407164e-4./c2_2_6_2+1.0).*6.27e1)./log(2.0)+(c2_2_10_1.*log(3.219742266689613e-5./c2_2_10_1+1.0).*6.27e1)./log(2.0)+(c3_3_10_2.*log(7.675482739484303e-4./c3_3_10_2+1.0).*2.700000000000003)./log(2.0)+(c2_2_5_2.*log(8.432663232716602e-6./c2_2_5_2+1.0).*2.700000000000003)./log(2.0)+(c1_1_9_1.*log(1.013753693919345e-4./c1_1_9_1+1.0).*6.27e1)./log(2.0)+(c1_1_3_1.*log(1.276130161564401e-4./c1_1_3_1+1.0).*2.700000000000003)./log(2.0)+(c2_2_4_1.*log(4.96783374599404e-6./c2_2_4_1+1.0).*6.27e1)./log(2.0)+(c3_2_3_1.*log(4.834243294443376e-4./c3_2_3_1+1.0).*2.700000000000003)./log(2.0)+(c1_3_6_2.*log(1.293081766514975e-5./c1_3_6_2+1.0).*6.27e1)./log(2.0)+(c2_3_6_2.*log(4.126727964900137e-4./c2_3_6_2+1.0).*6.27e1)./log(2.0)+(c3_2_7_1.*log(9.2561196502388e-6./c3_2_7_1+1.0).*2.700000000000003)./log(2.0)+(c3_1_1_1.*log(4.940629619241917e-4./c3_1_1_1+1.0).*2.700000000000003)./log(2.0)+(c1_1_7_2.*log(8.569514955754829e-4./c1_1_7_2+1.0).*6.27e1)./log(2.0)+(c3_3_5_1.*log(1.829910896401041e-4./c3_3_5_1+1.0).*6.27e1)./log(2.0)+(c1_1_10_2.*log(1.354286503397928e-4./c1_1_10_2+1.0).*6.27e1)./log(2.0)+(c2_1_3_2.*log(4.661338283378261e-5./c2_1_3_2+1.0).*6.27e1)./log(2.0)+(c2_1_8_1.*log(9.076700315728709e-5./c2_1_8_1+1.0).*2.700000000000003)./log(2.0)+(c3_2_1_1.*log(1.141724959620909e-4./c3_2_1_1+1.0).*6.27e1)./log(2.0)+(c2_3_1_1.*log(1.357167373702702e-5./c2_3_1_1+1.0).*2.700000000000003)./log(2.0)+(c2_3_2_2.*log(1.000426875140687e-3./c2_3_2_2+1.0).*6.27e1)./log(2.0)+(c3_2_8_1.*log(1.958377893792828e-4./c3_2_8_1+1.0).*2.700000000000003)./log(2.0)+(c2_1_7_2.*log(4.729837054677384e-5./c2_1_7_2+1.0).*6.27e1)./log(2.0)+(c3_2_8_2.*log(4.025084653027512e-6./c3_2_8_2+1.0).*2.700000000000003)./log(2.0)+(c2_1_4_1.*log(9.725914801147167e-5./c2_1_4_1+1.0).*6.27e1)./log(2.0)+(c1_3_2_1.*log(5.187942328740618e-5./c1_3_2_1+1.0).*2.700000000000003)./log(2.0)+(c1_3_7_2.*log(2.120050489483678e-4./c1_3_7_2+1.0).*6.27e1)./log(2.0)+(c3_3_8_1.*log(2.150928013802931e-4./c3_3_8_1+1.0).*2.700000000000003)./log(2.0)+(c2_1_4_1.*log(9.725899883126643e-5./c2_1_4_1+1.0).*2.700000000000003)./log(2.0)+(c1_3_9_1.*log(1.935733343037886e-4./c1_3_9_1+1.0).*2.700000000000003)./log(2.0)+(c3_2_1_1.*log(1.141724952363403e-4./c3_2_1_1+1.0).*2.700000000000003)./log(2.0)+(c3_1_8_1.*log(8.096487120890757e-5./c3_1_8_1+1.0).*6.27e1)./log(2.0)+(c3_1_2_1.*log(3.47277955629085e-5./c3_1_2_1+1.0).*6.27e1)./log(2.0)+(c3_2_6_1.*log(3.679956878358732e-5./c3_2_6_1+1.0).*2.700000000000003)./log(2.0)+(c2_2_1_1.*log(1.692175519062596e-4./c2_2_1_1+1.0).*2.700000000000003)./log(2.0)+(c2_1_7_1.*log(1.113848483250759e-3./c2_1_7_1+1.0).*2.700000000000003)./log(2.0)+(c1_1_3_1.*log(1.276131398152123e-4./c1_1_3_1+1.0).*6.27e1)./log(2.0)+(c1_3_2_2.*log(3.969790510835165e-5./c1_3_2_2+1.0).*2.700000000000003)./log(2.0)+(c3_2_2_2.*log(1.228106876176335e-4./c3_2_2_2+1.0).*2.700000000000003)./log(2.0)

Error in fmincon (line 536)

initVals.f = feval(funfcn{3},X,varargin{:});

Error in test_Lagrangian (line 215)

[x,fval]=fmincon(objfun,c0,A,b,[],[],lb,ub);

Caused by:

Failure in initial objective function evaluation. FMINCON cannot continue.

[Susan]

Any idea how to solve this issue?

[Walter Roberson]

Use

'vars', {reshape(c,1,[])}

instead of

'vars', c

in matlabFunction

[Susan]

thank you so much !!!

[Susan]

I got the following error now

Index exceeds matrix dimensions.

Error in

symengine>@(in1)(in1(:,78).*log(3.700708093693542e-4./in1(:,78)+1.0).*(-6.27e1))./log(2.0)-(in1(:,104).*log(4.669739411141582e-5./in1(:,104)+1.0).*6.27e1)./log(2.0)-(in1(:,51).*log(2.443358537890334e-4./in1(:,51)+1.0).*2.700000000000003)./log(2.0)-(in1(:,139).*log(2.514556287395105e-5./in1(:,139)+1.0).*2.700000000000003)./log(2.0)-(in1(:,139).*log(2.514556287930596e-5./in1(:,139)+1.0).*6.27e1)./log(2.0)-(in1(:,134).*log(2.570669932016933e-5./in1(:,134)+1.0).*2.700000000000003)./log(2.0)-(in1(:,117).*log(5.735376164543904e-5./in1(:,117)+1.0).*2.700000000000003)./log(2.0)-(in1(:,49).*log(1.867966529202098e-5./in1(:,49)+1.0).*2.700000000000003)./log(2.0)-(in1(:,22).*log(2.283723279140077e-5./in1(:,22)+1.0).*2.700000000000003)./log(2.0)-(in1(:,36).*log(4.870005093830326e-4./in1(:,36)+1.0).*6.27e1)./log(2.0)-(in1(:,168).*log(8.609158640878199e-5./in1(:,168)+1.0).*2.700000000000003)./log(2.0)-(in1(:,19).*log(4.298551229608035e-4./in1(:,19)+1.0).*2.700000000000003)./log(2.0)-(in1(:,18).*log(2.876034194067673e-4./in1(:,18)+1.0).*6.27e1)./log(2.0)-(in1(:,9).*log(7.764417568200059e-6./in1(:,9)+1.0).*2.700000000000003)./log(2.0)-(in1(:,148).*log(9.926594773194256e-6./in1(:,148)+1.0).*6.27e1)./log(2.0)-(in1(:,12).*log(1.582010745128305e-3./in1(:,12)+1.0).*6.27e1)./log(2.0)-(in1(:,28).*log(1.795141903330641e-4./in1(:,28)+1.0).*6.27e1)./log(2.0)-(in1(:,157).*log(1.285490118581143e-4./in1(:,157)+1.0).*2.700000000000003)./log(2.0)-(in1(:,61).*log(9.1145743322635e-4./in1(:,61)+1.0).*6.27e1)./log(2.0)-(in1(:,94).*log(4.455056376508116e-5./in1(:,94)+1.0).*2.700000000000003)./log(2.0)-(in1(:,71).*log(5.169490071789642e-5./in1(:,71)+1.0).*2.700000000000003)./log(2.0)-(in1(:,119).*log(6.720156612960165e-4./in1(:,119)+1.0).*2.700000000000003)./log(2.0)-(in1(:,12).*log(1.581969779598259e-3./in1(:,12)+1.0).*2.700000000000003)./log(2.0)-(in1(:,113).*log(5.320070826226837e-5./in1(:,113)+1.0).*2.700000000000003)./log(2.0)-(in1(:,113).*log(5.320070826369415e-5./in1(:,113)+1.0).*6.27e1)./log(2.0)-(in1(:,39).*log(7.712968431102731e-4./in1(:,39)+1.0).*2.700000000000003)./log(2.0)-(in1(:,14).*log(2.514300108820583e-5./in1(:,14)+1.0).*6.27e1)./log(2.0)-(in1(:,89).*log(2.149568195991095e-4./in1(:,89)+1.0).*6.27e1)./log(2.0)-(in1(:,151).*log(3.718849429834584e-5./in1(:,151)+1.0).*2.700000000000003)./log(2.0)-(in1(:,18).*log(2.876034182814147e-4./in1(:,18)+1.0).*2.700000000000003)./log(2.0)-(in1(:,134).*log(2.57066993398925e-5./in1(:,134)+1.0).*6.27e1)./log(2.0)-(in1(:,133).*log(5.617109026677226e-4./in1(:,133)+1.0).*6.27e1)./log(2.0)-(in1(:,32).*log(2.918025663930002e-5./in1(:,32)+1.0).*2.700000000000003)./log(2.0)-(in1(:,180).*log(2.489140391603624e-4./in1(:,180)+1.0).*6.27e1)./log(2.0)-(in1(:,48).*log(4.906767035366405e-5./in1(:,48)+1.0).*6.27e1)./log(2.0)-(in1(:,96).*log(7.394538106764381e-4./in1(:,96)+1.0).*6.27e1)./log(2.0)-(in1(:,9).*log(7.764417606133432e-6./in1(:,9)+1.0).*6.27e1)./log(2.0)-(in1(:,14).*log(2.514300107956375e-5./in1(:,14)+1.0).*2.700000000000003)./log(2.0)-(in1(:,23).*log(2.980398721805458e-4./in1(:,23)+1.0).*2.700000000000003)./log(2.0)-(in1(:,154).*log(2.536734576857251e-5./in1(:,154)+1.0).*6.27e1)./log(2.0)-(in1(:,57).*log(1.090879257850063e-5./in1(:,57)+1.0).*6.27e1)./log(2.0)-(in1(:,76).*log(1.129706506851571e-4./in1(:,76)+1.0).*2.700000000000003)./log(2.0)-(in1(:,109).*log(2.558079924151623e-5./in1(:,109)+1.0).*6.27e1)./log(2.0)-(in1(:,75).*log(3.654172086781249e-4./in1(:,75)+1.0).*2.700000000000003)./log(2.0)-(in1(:,109).*log(2.558079904087554e-5./in1(:,109)+1.0).*2.700000000000003)./log(2.0)-(in1(:,22).*log(2.283723279692574e-5./in1(:,22)+1.0).*6.27e1)./log(2.0)-(in1(:,36).*log(4.870005063321393e-4./in1(:,36)+1.0).*2.700000000000003)./log(2.0)-(in1(:,95).*log(7.962761634294468e-5./in1(:,95)+1.0).*6.27e1)./log(2.0)-(in1(:,68).*log(2.783681217497318e-4./in1(:,68)+1.0).*6.27e1)./log(2.0)-(in1(:,70).*log(1.70845387062621e-3./in1(:,70)+1.0).*2.700000000000003)./log(2.0)-(in1(:,87).*log(9.406921605195156e-4./in1(:,87)+1.0).*2.700000000000003)./log(2.0)-(in1(:,26).*log(1.12864534571323e-5./in1(:,26)+1.0).*2.700000000000003)./log(2.0)-(in1(:,38).*log(7.306094355426823e-5./in1(:,38)+1.0).*2.700000000000003)./log(2.0)-(in1(:,28).*log(1.795141894854763e-4./in1(:,28)+1.0).*2.700000000000003)./log(2.0)-(in1(:,26).*log(1.128645347997953e-5./in1(:,26)+1.0).*6.27e1)./log(2.0)-(in1(:,57).*log(1.090879065416532e-5./in1(:,57)+1.0).*2.700000000000003)./log(2.0)-(in1(:,27).*log(9.043708046285206e-5./in1(:,27)+1.0).*2.700000000000003)./log(2.0)-(in1(:,65).*log(8.073642583575628e-4./in1(:,65)+1.0).*6.27e1)./log(2.0)-(in1(:,92).*log(3.039633915786994e-5./in1(:,92)+1.0).*2.700000000000003)./log(2.0)-(in1(:,96).*log(7.394537855022456e-4./in1(:,96)+1.0).*2.700000000000003)./log(2.0)-(in1(:,153).*log(8.012810723870263e-5./in1(:,153)+1.0).*6.27e1)./log(2.0)-(in1(:,31).*log(1.248232405768023e-4./in1(:,31)+1.0).*6.27e1)./log(2.0)-(in1(:,151).*log(3.718852719025231e-5./in1(:,151)+1.0).*6.27e1)./log(2.0)-(in1(:,120).*log(1.374120958261478e-4./in1(:,120)+1.0).*6.27e1)./log(2.0)-(in1(:,168).*log(8.60915874098138e-5./in1(:,168)+1.0).*6.27e1)./log(2.0)-(in1(:,76).*log(1.129706507149941e-4./in1(:,76)+1.0).*6.27e1)./log(2.0)-(in1(:,60).*log(1.107546343089073e-5./in1(:,60)+1.0).*6.27e1)./log(2.0)-(in1(:,135).*log(1.432099253515767e-4./in1(:,135)+1.0).*6.27e1)./log(2.0)-(in1(:,106).*log(7.186634489993739e-5./in1(:,106)+1.0).*2.700000000000003)./log(2.0)-(in1(:,33).*log(6.11109985272239e-4./in1(:,33)+1.0).*2.700000000000003)./log(2.0)-(in1(:,177).*log(3.152473575227821e-5./in1(:,177)+1.0).*2.700000000000003)./log(2.0)-(in1(:,146).*log(2.462395702655356e-4./in1(:,146)+1.0).*2.700000000000003)./log(2.0)-(in1(:,5).*log(6.511066818378382e-5./in1(:,5)+1.0).*6.27e1)./log(2.0)-(in1(:,157).*log(1.285490118876367e-4./in1(:,157)+1.0).*6.27e1)./log(2.0)-(in1(:,89).*log(2.149568190248988e-4./in1(:,89)+1.0).*2.700000000000003)./log(2.0)-(in1(:,39).*log(7.712972009042298e-4./in1(:,39)+1.0).*6.27e1)./log(2.0)-(in1(:,148).*log(9.926594772798826e-6./in1(:,148)+1.0).*2.700000000000003)./log(2.0)-(in1(:,51).*log(2.44335853972425e-4./in1(:,51)+1.0).*6.27e1)./log(2.0)-(in1(:,135).*log(1.432099251104773e-4./in1(:,135)+1.0).*2.700000000000003)./log(2.0)-(in1(:,1).*log(1.592791694124719e-4./in1(:,1)+1.0).*6.27e1)./log(2.0)-(in1(:,2).*log(3.976340549497248e-7./in1(:,2)+1.0).*6.27e1)./log(2.0)-(in1(:,71).*log(5.16949011772525e-5./in1(:,71)+1.0).*6.27e1)./log(2.0)-(in1(:,103).*log(1.816960919178301e-4./in1(:,103)+1.0).*2.700000000000003)./log(2.0)-(in1(:,153).*log(8.012810721799162e-5./in1(:,153)+1.0).*2.700000000000003)./log(2.0)-(in1(:,31).*log(1.248232405766132e-4./in1(:,31)+1.0).*2.700000000000003)./log(2.0)-(in1(:,95).*log(7.962761632634943e-5./in1(:,95)+1.0).*2.700000000000003)./log(2.0)-(in1(:,177).*log(3.15247365894401e-5./in1(:,177)+1.0).*6.27e1)./log(2.0)-(in1(:,146).*log(2.462396542779665e-4./in1(:,146)+1.0).*6.27e1)./log(2.0)-(in1(:,59).*log(5.690307924246731e-4./in1(:,59)+1.0).*6.27e1)./log(2.0)-(in1(:,123).*log(4.41441858608037e-4./in1(:,123)+1.0).*6.27e1)./log(2.0)-(in1(:,75).*log(3.654178013425385e-4./in1(:,75)+1.0).*6.27e1)./log(2.0)-(in1(:,59).*log(5.690307884547468e-4./in1(:,59)+1.0).*2.700000000000003)./log(2.0)-(in1(:,160).*log(1.248055155948976e-3./in1(:,160)+1.0).*2.700000000000003)./log(2.0)-(in1(:,73).*log(4.615120659756858e-6./in1(:,73)+1.0).*2.700000000000003)./log(2.0)-(in1(:,130).*log(9.102200613061163e-6./in1(:,130)+1.0).*2.700000000000003)./log(2.0)-(in1(:,69).*log(3.547103802825236e-4./in1(:,69)+1.0).*2.700000000000003)./log(2.0)-(in1(:,84).*log(2.620542915143757e-4./in1(:,84)+1.0).*2.700000000000003)./log(2.0)-(in1(:,38).*log(7.306100014736255e-5./in1(:,38)+1.0).*6.27e1)./log(2.0)-(in1(:,164).*log(7.253294381388299e-5./in1(:,164)+1.0).*6.27e1)./log(2.0)-(in1(:,73).*log(4.615120983522106e-6./in1(:,73)+1.0).*6.27e1)./log(2.0)-(in1(:,169).*log(2.093498022392337e-4./in1(:,169)+1.0).*6.27e1)./log(2.0)-(in1(:,160).*log(1.248074458990539e-3./in1(:,160)+1.0).*6.27e1)./log(2.0)-(in1(:,120).*log(1.374120831903159e-4./in1(:,120)+1.0).*2.700000000000003)./log(2.0)-(in1(:,2).*log(3.976340448461962e-7./in1(:,2)+1.0).*2.700000000000003)./log(2.0)-(in1(:,55).*log(1.69583842010779e-4./in1(:,55)+1.0).*2.700000000000003)./log(2.0)-(in1(:,78).*log(3.700708059146551e-4./in1(:,78)+1.0).*2.700000000000003)./log(2.0)-(in1(:,124).*log(1.493669425823087e-4./in1(:,124)+1.0).*2.700000000000003)./log(2.0)-(in1(:,137).*log(5.321326078913253e-4./in1(:,137)+1.0).*2.700000000000003)./log(2.0)-(in1(:,180).*log(2.489140374246951e-4./in1(:,180)+1.0).*2.700000000000003)./log(2.0)-(in1(:,47).*log(2.007765642116442e-4./in1(:,47)+1.0).*6.27e1)./log(2.0)-(in1(:,10).*log(7.773708537473517e-6./in1(:,10)+1.0).*6.27e1)./log(2.0)-(in1(:,123).*log(4.414416056151271e-4./in1(:,123)+1.0).*2.700000000000003)./log(2.0)-(in1(:,92).*log(3.039634990991273e-5./in1(:,92)+1.0).*6.27e1)./log(2.0)-(in1(:,69).*log(3.547112562110829e-4./in1(:,69)+1.0).*6.27e1)./log(2.0)-(in1(:,86).*log(3.256307174410304e-5./in1(:,86)+1.0).*6.27e1)./log(2.0)-(in1(:,104).*log(4.669739409129287e-5./in1(:,104)+1.0).*2.700000000000003)./log(2.0)-(in1(:,99).*log(8.406741282930918e-5./in1(:,99)+1.0).*6.27e1)./log(2.0)-(in1(:,145).*log(2.62131948569829e-4./in1(:,145)+1.0).*6.27e1)./log(2.0)-(in1(:,60).*log(1.107546330203636e-5./in1(:,60)+1.0).*2.700000000000003)./log(2.0)-(in1(:,174).*log(1.505018202887781e-4./in1(:,174)+1.0).*2.700000000000003)./log(2.0)-(in1(:,27).*log(9.04370809390637e-5./in1(:,27)+1.0).*6.27e1)./log(2.0)-(in1(:,130).*log(9.102200613261273e-6./in1(:,130)+1.0).*6.27e1)./log(2.0)-(in1(:,72).*log(8.935993089324253e-6./in1(:,72)+1.0).*6.27e1)./log(2.0)-(in1(:,124).*log(1.493705004737316e-4./in1(:,124)+1.0).*6.27e1)./log(2.0)-(in1(:,164).*log(7.25329349290908e-5./in1(:,164)+1.0).*2.700000000000003)./log(2.0)-(in1(:,158).*log(8.221716446120684e-4./in1(:,158)+1.0).*2.700000000000003)./log(2.0)-(in1(:,158).*log(8.221716451343319e-4./in1(:,158)+1.0).*6.27e1)./log(2.0)-(in1(:,128).*log(5.401281926973628e-5./in1(:,128)+1.0).*2.700000000000003)./log(2.0)-(in1(:,23).*log(2.980398724789611e-4./in1(:,23)+1.0).*6.27e1)./log(2.0)-(in1(:,174).*log(1.50504874087289e-4./in1(:,174)+1.0).*6.27e1)./log(2.0)-(in1(:,32).*log(2.918025664553941e-5./in1(:,32)+1.0).*6.27e1)./log(2.0)-(in1(:,114).*log(1.136962927302141e-4./in1(:,114)+1.0).*6.27e1)./log(2.0)-(in1(:,154).*log(2.536734362833077e-5./in1(:,154)+1.0).*2.700000000000003)./log(2.0)-(in1(:,128).*log(5.401286334394613e-5./in1(:,128)+1.0).*6.27e1)./log(2.0)-(in1(:,21).*log(4.673615013499751e-5./in1(:,21)+1.0).*2.700000000000003)./log(2.0)-(in1(:,70).*log(1.708530984318943e-3./in1(:,70)+1.0).*6.27e1)./log(2.0)-(in1(:,106).*log(7.186635938960522e-5./in1(:,106)+1.0).*6.27e1)./log(2.0)-(in1(:,34).*log(1.873550783324951e-4./in1(:,34)+1.0).*6.27e1)./log(2.0)-(in1(:,84).*log(2.620545610936377e-4./in1(:,84)+1.0).*6.27e1)./log(2.0)-(in1(:,65).*log(8.073636689898464e-4./in1(:,65)+1.0).*2.700000000000003)./log(2.0)-(in1(:,68).*log(2.783681216061339e-4./in1(:,68)+1.0).*2.700000000000003)./log(2.0)-(in1(:,33).*log(6.111099903763513e-4./in1(:,33)+1.0).*6.27e1)./log(2.0)-(in1(:,21).*log(4.673615342732187e-5./in1(:,21)+1.0).*6.27e1)./log(2.0)-(in1(:,61).*log(9.114559633645742e-4./in1(:,61)+1.0).*2.700000000000003)./log(2.0)-(in1(:,55).*log(1.695838425758367e-4./in1(:,55)+1.0).*6.27e1)./log(2.0)-(in1(:,63).*log(9.322248199040759e-5./in1(:,63)+1.0).*2.700000000000003)./log(2.0)-(in1(:,63).*log(9.322248207922858e-5./in1(:,63)+1.0).*6.27e1)./log(2.0)-(in1(:,72).*log(8.93599303138855e-6./in1(:,72)+1.0).*2.700000000000003)./log(2.0)-(in1(:,1).*log(1.592791689012414e-4./in1(:,1)+1.0).*2.700000000000003)./log(2.0)-(in1(:,145).*log(2.621319462549545e-4./in1(:,145)+1.0).*2.700000000000003)./log(2.0)-(in1(:,103).*log(1.816960919424889e-4./in1(:,103)+1.0).*6.27e1)./log(2.0)-(in1(:,119).*log(6.720157169499005e-4./in1(:,119)+1.0).*6.27e1)./log(2.0)-(in1(:,19).*log(4.298551340774251e-4./in1(:,19)+1.0).*6.27e1)./log(2.0)-(in1(:,30).*log(1.905149359027724e-4./in1(:,30)+1.0).*2.700000000000003)./log(2.0)-(in1(:,117).*log(5.735376181156458e-5./in1(:,117)+1.0).*6.27e1)./log(2.0)-(in1(:,49).*log(1.867966529448703e-5./in1(:,49)+1.0).*6.27e1)./log(2.0)-(in1(:,147).*log(5.175910738214906e-5./in1(:,147)+1.0).*6.27e1)./log(2.0)-(in1(:,171).*log(5.345736855582846e-5./in1(:,171)+1.0).*6.27e1)./log(2.0)-(in1(:,5).*log(6.511066810885859e-5./in1(:,5)+1.0).*2.700000000000003)./log(2.0)-(in1(:,156).*log(5.473531387638728e-4./in1(:,156)+1.0).*6.27e1)./log(2.0)-(in1(:,10).*log(7.77370840804361e-6./in1(:,10)+1.0).*2.700000000000003)./log(2.0)-(in1(:,175).*log(7.862430970288111e-5./in1(:,175)+1.0).*2.700000000000003)./log(2.0)-(in1(:,114).*log(1.136962823443101e-4./in1(:,114)+1.0).*2.700000000000003)./log(2.0)-(in1(:,48).*log(4.906751287231519e-5./in1(:,48)+1.0).*2.700000000000003)./log(2.0)-(in1(:,94).*log(4.455056377225578e-5./in1(:,94)+1.0).*6.27e1)./log(2.0)-(in1(:,147).*log(5.175910055751504e-5./in1(:,147)+1.0).*2.700000000000003)./log(2.0)-(in1(:,156).*log(5.473529214539608e-4./in1(:,156)+1.0).*2.700000000000003)./log(2.0)-(in1(:,137).*log(5.321332334918264e-4./in1(:,137)+1.0).*6.27e1)./log(2.0)-(in1(:,99).*log(8.406741099696084e-5./in1(:,99)+1.0).*2.700000000000003)./log(2.0)-(in1(:,131).*log(6.584604328680697e-4./in1(:,131)+1.0).*6.27e1)./log(2.0)-(in1(:,86).*log(3.256307173618805e-5./in1(:,86)+1.0).*2.700000000000003)./log(2.0)-(in1(:,30).*log(1.905149676752631e-4./in1(:,30)+1.0).*6.27e1)./log(2.0)-(in1(:,87).*log(9.406921760100818e-4./in1(:,87)+1.0).*6.27e1)./log(2.0)-(in1(:,47).*log(2.007708704812767e-4./in1(:,47)+1.0).*2.700000000000003)./log(2.0)-(in1(:,34).*log(1.873544576329058e-4./in1(:,34)+1.0).*2.700000000000003)./log(2.0)-(in1(:,171).*log(5.345736813477682e-5./in1(:,171)+1.0).*2.700000000000003)./log(2.0)-(in1(:,169).*log(2.093495886752037e-4./in1(:,169)+1.0).*2.700000000000003)./log(2.0)-(in1(:,131).*log(6.584604313711503e-4./in1(:,131)+1.0).*2.700000000000003)./log(2.0)-(in1(:,133).*log(5.617103797100137e-4./in1(:,133)+1.0).*2.700000000000003)./log(2.0)-(in1(:,175).*log(7.862430992279681e-5./in1(:,175)+1.0).*6.27e1)./log(2.0)

Error in test_Lagrangian>@(c)objfun_AnonymousFunction(c)

Error in fmincon (line 536)

initVals.f = feval(funfcn{3},X,varargin{:});

Error in test_Lagrangian (line 219)

[c_opt,fval_c,flag_c] = fmincon(objfun_c,c0,A_c,b_c,[],[],lb_c,ub_c,[],opts);

Caused by:

Failure in initial objective function evaluation. FMINCON cannot continue.

However, if I use 'vars', {c} it works. But I don't know if it make sense or not.

[Walter Roberson]

What size is your c0 ?

fmincon reshapes the vector of values into the same shape as c0 before passing it in. If your c0 is a 2D array then 'vars', {c} should be fine.

[Susan]

This is the size of c0.

c0 = zeros(max(nr_L(:)), numel(nr_L), K, max(nbrOfSubCarriers(:))); for example(3,3,10,2)

Is still 'vars', {c} fine?

Thanks in advance

[Walter Roberson]

Yes, 'vars', {c} should work for that.

[Susan]

Thanks for your reply.

Answer 2

[Susan]

@ Walter, I am trying to write-down the following linear constraint according to the way you taught me above.

sum_{m} sum_{i} p(i, j,j,k,m) <=P_{k, j} for all k = 1: K and j = 1 : J

m = 1: M and i = 1 : I and P_{k, j} is given and fixed for each specific k and j

I wrote this constraint in the format of

A(something,:) = [zeros(1, SOMETHING), ones(1, I), zeros(1,SOMETHINGELSE)];
b(something) = P_{k,j};

However, I wasn't able to find a specific relationship between the rows of A.

What I found is sth like

A(1, :) = [ones(1, I) zeros(1, (J*J*K -1)*I) ones(1, I)  zeros(1, (J*J*K -1)*I) ] 
A(2, :) = [zeros(1,K) ones(1, I) zeros(1,(J*J*K -1)*I) ones(1, I)  zeros(1,(J*J*K -1)*I) zeros(1,(J*J*K -1)*I -K)]

the pattern "ones(1, I) zeros(1, (J*J*K -1)*I) ones(1, I) zeros(1, (J*J*K -1)*I) " is repeated in all rows but I wasn't able to figure out what is the formulation of the begining zeros(1, SOMETHING), and correspondingly the zeros(1,SOMETHINGELSE) in order to write matrix A.

Could you please let me know what would be the format of A? Thank you so much in advance.

[Walter Roberson]

Use the blkdiag() method I posted as "easier way of generating this". It generates the entire A and b matrix in a small number of lines.

[Susan]

Great! Thanks for all your help. Appreciate that.