An analytical framework for consensus-based global optimization method

José A. Carrillo, Young Pil Choi, Claudia Totzeck, Oliver Tse

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Article number00276
Pages (from-to)1037-1066
Number of pages30
JournalMathematical Models & Methods in Applied Sciences
Volume28
Issue number6
DOIs
Publication statusPublished - 15 Jun 2018

Keywords

  • agent-based models
  • consensus formation
  • Global optimization
  • mean-field limit
  • opinion dynamics
  • stochastic dynamics

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