Hi! Do I understand correctly that all of the gradient-based optimization methods included in nlopt do not have built-in finite differences or automatic differentiation approximators, such as, for instance NumPy fmin_l_bfgs_b and one has to either provide gradients by programming the analytic formulas, or perform numeric approximations on his own?
In such a case, what would be the recommended code to use for Python? Is it possible to include such a tool in nlopt in the future? -- Sincerely yours, Yury V. Zaytsev _______________________________________________ NLopt-discuss mailing list [email protected] http://ab-initio.mit.edu/cgi-bin/mailman/listinfo/nlopt-discuss
