Unfortunately, there's not currently a way to register new search algorithms with NLopt, except by hacking the NLopt source code directly.
On Nov 4, 2013, at 5:51 AM, federico vaggi wrote: > Hi everyone, > > I'm currently using the Python wrappers of NLopt. I'm working on a > non-convex least squares problem, where the local search portion of the > optimization is best handled by a LS algorithm like Levemberg Marquardt (the > SciPy implementation, or any other one, really). > > Originally, I was thinking of simply doing something like Latin Hypercube > Sampling to explore the full parameter landscape, but I was wondering if I > could combine the local least-squares optimization step with one of the > global algorithms in NLopt like MLSL to search the whole parameter landscape > more efficiently. > > Federico > _______________________________________________ > NLopt-discuss mailing list > [email protected] > http://ab-initio.mit.edu/cgi-bin/mailman/listinfo/nlopt-discuss _______________________________________________ NLopt-discuss mailing list [email protected] http://ab-initio.mit.edu/cgi-bin/mailman/listinfo/nlopt-discuss
