hi all, I'm glad to inform you that stochastic programming and optimization addon for FuncDesigner v. 0.421 has been released. Now you can use gradient-based solvers for numerical optimization, such as ALGENCAN, IPOPT, ralg, gsubg etc. Usually they work faster than derivative-free (such as scipy_cobyla, BOBYQA) or global (GLP) solvers, e.g. on this example ALGENCAN time elapsed is less than 1 second while scipy_cobyla spend ~20 sec. However pay attention that having function P() in your problem may bring nonconvexity or some other issues to the solver optimization trajectory, thus sometimes you'll have to use derivative-free or GLP solvers (e.g. de) instead. FuncDesigner is free (BSD license) cross-platform Python language written software, while its stochastic programming and optimization addon, written by same authors, is free for small-scaled problems with educational or research purposes only. For more details visit our website http://openopt.org ------------------------------------- Regards, D. http://openopt.org/Dmitrey
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