What is your initial point and initial mesh size? Perhaps these already exceeds your bounds.
Edit:
Hmm, what if you normalize your variables and bounds (and modify the objective function too) into similar range? For example:
global lb_ori ub_ori % make global so that these variables can be called inside a function
% Original bounds
lb_ori = [1e-6 -1e6];
ub_ori = [1e-1 -1e4];
% Normalized bounds
lb_norm = [0 0];
ub_norm = [1 1];
% Run pattern search with normalized variables, bounds, and objective function
x0 = rand(1,2); % random number within 0 and 1 (normalized bounds)
[x,fval] = patternsearch(@objective_norm,x0,[],[],[],[],lb,ub)
% Original objective function
function fval = objective_ori(x)
fval = sum(x.^2); % an example objective function
end
% Normalized objective function
function fval = objective_norm(x_norm)
global lb_ori ub_ori
x = x_norm.*(ub_ori - lb_ori) + lb_ori;
fval = sum(x.^2);
end
Not sure if this code works, but do you get my idea of normalization?
I am not sure how the mesh size behaves in Matlab pattern search when the variables have significant different ranges, we can only see one mesh size even though it should be different for each variable. But normalization should solves this.
