Question

I have a 101x82 size matrix called A. I am trying to minimize an objective function obj_fun, whose value is computed indirectly using A.

Now in order to minimize this objective function obj_fun, I need to perturb the values of A. I want to check if obj_fun is going down in values or not. If not, then I need to do perturb/change values of A to a certain percentage such that it minimizes obj_fun. Keep on perturbing/changing values of A until we get minimum obj_fun. My average value of A before any perturbation is ~ 1.1529e+003.

Does any one have suggestion how can I do this? Also, I care a bit about speed i.e. the method/algorithm should not be too slow. Thanks.

Answer 1

You can add random Gaussian noise to A:

A = 0; % seed value for A with something more interesting than 0
best = obj_fun(A);

for iter = 1:max_iter % max_iter should be the maximum number of iterations
  newA = A + normrnd(0, 1, size(A));
  newObj = obj_fun(A);
  if( newObj < best )
    best = newObj;
    A = newA;
  end
end