An analytical framework for a consensus-based global optimization method

2016/01/31 by José A. Carrillo, Young-Pil Choi, Carrillo, José A. +5 · 21 citations
Mathematics · #Analysis of PDEs (math.AP) #FOS: Mathematics #math.AP

paper · pdf · doi:10.48550/arxiv.1602.00220

arxiv created 2018/02/07 · arxiv updated 2018/02/08

Abstract

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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• Kinetic simulated annealing optimization with entropy-based cooling rate
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• Controlled stochastic processes for simulated annealing

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