I have a square matrix that I need to use with fminsearch. Some of the values of the matrix need to be variable because they are the values that I will be using with fminsearch, and I need to preserve their location in the matrix. So for example, with
X=[1,2,3;4,5,6;7,8,9];
I would like make this
p(1)=a1;
p(2)=a2;
p(3)=a3;
p(4)=a4;
X=[1, 2, a1 ; a2, 5, a3 ; 7, 8, a4];
So that I could do operations on X to create something to be minimized with fminsearch. For example, suppose I wanted to find a1, a2, a3, and a4 so that I minimized C in the following code, which computes the summed entropy of a given matrix:
Ent=zeros(size(X,1),1);
for k=1:size(X,2);
const=X(k,:);
logX=log(X(k,:));
logX=logX';
Ent(k,:)=const*logX;
end
Ent=-Ent;
C=sum(Ent);
Is this possible with MATLAB? Suppose further I had an nxn matrix with q paramaters, how would I adjust the idea to minimize the same C?
EDIT:
I figured out how to do what I want. However it has come to me that a gradient descent algorithm is really a better way of doing this. I'll post a sample of what I was doing:
function test()
[new_param entropy]=fminsearch(@Cost,[3,3,3]);
function C=Cost(p)
X=rand(5);
X(1,1)=p(1);
X(2,2)=p(2);
X(3,3)=p(3);
Ent=zeros(size(X,1),1);
for k=1:size(X,2);
const=X(k,:);
logX=log(X(k,:));
logX=logX';
Ent(k,:)=const*logX;
end
Ent=-Ent;
C=sum(Ent);
end
X(1,1)=new_param(1);
X(2,2)=new_param(2);
X(3,3)=new_param(3);
X
new_param
entropy
end
I think that you could try something like this:
A = @(x) [1 2 x(1); 3 4 x(2); 5 6 x(3)];
or, alternatively, making use of the Symbolic Toolbox
:
syms x y z
A = [1 2 x; 3 4 y; 5 6 z];