An analytical framework for consensus-based global optimization method

Author: 

Carrillo de la Plata, J
Choi, Y
Totzeck, C
Tse, O

Publication Date: 

11 April 2018

Journal: 

Mathematical Models and Methods in Applied Sciences

Last Updated: 

2020-11-13T18:50:04.03+00:00

Issue: 

6

Volume: 

28

DOI: 

10.1142/S0218202518500276

page: 

1037-1066

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.

Symplectic id: 

1098247

Submitted to ORA: 

Submitted

Publication Type: 

Journal Article