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
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