An analytical framework for a consensus-based global optimization method
Analysis of PDEs
2018-02-08 v5
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.
Cite
@article{arxiv.1602.00220,
title = {An analytical framework for a consensus-based global optimization method},
author = {José A. Carrillo and Young-Pil Choi and Claudia Totzeck and Oliver Tse},
journal= {arXiv preprint arXiv:1602.00220},
year = {2018}
}