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workaround for handling a large number of variables in the objective function of lsqnonlin

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I want to optimize my objective function
w0=zeros(m,1)
[w,resnorm] = lsqnonlin(@myfun,w0)
How can I dynamically define weights w(1) w(2) w(3) in my function to adapt any possible change in number of variables (m) as follow
function F = myfun(w)
global X % regression matrix of (nxm)
global Y % output vector (nx1)
F = Y - ( w(1)*X(:,1) + w(2)*X(:,2) + w(3)*X(:,3) + .. + w(m)*X(:,m) );
end

Accepted Answer

Stephen Cobeldick
Stephen Cobeldick on 12 May 2020
Edited: Stephen Cobeldick on 12 May 2020
>> X = rand(7,3);
>> w = rand(3,1);
>> w(1)*X(:,1) + w(2)*X(:,2) + w(3)*X(:,3) % what you do now
ans =
0.63892
0.43089
0.59637
0.89806
1.08999
0.98472
0.38443
>> X*w(:) % what you should do: matrix multiply
ans =
0.63892
0.43089
0.59637
0.89806
1.08999
0.98472
0.38443
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