An analytical framework for consensus-based global optimization method

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

Research output: Contribution to journalArticlepeer-review

20 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
JournalMathematical Models and Methods in Applied Sciences
Volume28
Issue number6
DOIs
Publication statusPublished - 2018 Jun 15

Bibliographical note

Funding Information:
JAC was partially supported by the Royal Society by a Wolfson Research Merit Award and by EPSRC Grant Number EP/P031587/1. Y-PC was supported by the Alexander Humboldt Foundation through the Humboldt Research Fellowship for Postdoctoral Researchers. Y-PC was also supported by NRF Grants (NRF-2017R1C1B2012918 and 2017R1A4A1014735). CT was partially supported by a “Kurzstipendium für Doktorandinnen und Doktoranden” by the German Academic Exchange Service. OT is thankful to Jim Portegies for stimulating discussions.

Publisher Copyright:
© 2018 World Scientific Publishing Company.

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Applied Mathematics

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