Papers › An analytical framework for a consensus-based global optimization method
An analytical framework for a consensus-based global optimization method
José A. Carrillo, Young-Pil Choi, Claudia Totzeck, Oliver Tse
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In this paper we provide an analytical framework for investigating the efficiency of a consensus-based model for tackling global optimization problems. This work justifies the optimization algorithm in the mean-field sense showing the convergence to the global minimizer for a large class of functions. Theoretical results on consensus estimates are then illustrated by numerical simulations where variants of the method including nonlinear diffusion are introduced.
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