I'm attempting to optimize a smooth unconstrained function which has 77 unknown parameters. I have vectorized the optimization across observations and am using lsqnonlin to solve.
opt = optimset('TolX', 1E-6, 'TolFun', 1E-6, 'MaxFunEvals', 100000, 'MaxIter', 5000, 'DiffMinChange', 1e-2, 'Algorithm', 'levenberg-marquardt', 'UseParallel', false);
f = @(parameters)ProbDiff(parameters, y, X);
[EstBetaLS, resnorm, residual] = lsqnonlin(f, Beta_init, , , opt);
I run this optimization on data I simulated by setting all unknown parameters equal to 1.
I currently get the following message when running the optimization: "Local minimum found. Optimization completed because the size of the gradient is less than
1e-4 times the selected value of the function tolerance."
However, the parameters values found are very sensitive to the initial guess and never converge to the true value of 1. This leads me to believe the objective function is very flat.
What can I do to address this problem? In particular, will supplying the gradient help?