[SciPy-User] OT: global optimization, hybrid global local search
Sun Apr 22 10:00:44 CDT 2012
I'm looking at nonlinear regression, function estimation again.
I'm interested in combining global search with local optimizers, which
I think should be much faster in many of our problems.
Anyone with ideas, experience, code?
I just searched and browsed a few papers, but it's not my specialization.
"In particular, the problems which are very hard to be solved by gradient
descent-based methods, or the ones which have computationally
expensive objective functions are very good candidates to be solved
by the proposed methods." *)
*) Hybrid optimization with improved tabu search
Genetic and Nelder–Mead algorithms hybridized for a more accurate
global optimization of continuous multiminima functions
A hybrid method combining continuous tabu search and Nelder–Mead
simplex algorithms for the global optimization of multiminima
Hybridizing exact methods and metaheuristics: A taxonomy
mostly integer programming
Hybrid simulated annealing and direct search method for nonlinear
unconstrained global optimization
(Why does scipy not have any good global optimizers?)
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