Uniform-in-time propagation of chaos for Consensus-Based Optimization
Abstract
We study the derivative-free global optimization algorithm Consensus-Based Optimization (CBO), establishing uniform-in-time propagation of chaos as well as an almost uniform-in-time stability result for the microscopic particle system. Moreover, we prove almost sure exponential convergence of the microscopic CBO system around a point close to the global minimizer. The proof of these results is based on a novel stability estimate for the weighted mean and on a quantitative concentration inequality for the microscopic particle system around the empirical mean. Our propagation of chaos result recovers the classical Monte Carlo rate, with a prefactor that depends explicitly on the parameters of the problem. Notably, in the case of CBO with anisotropic noise, this prefactor is independent of the problem dimension.
Keywords
Cite
@article{arxiv.2505.08669,
title = {Uniform-in-time propagation of chaos for Consensus-Based Optimization},
author = {Nicolai Gerber and Franca Hoffmann and Dohyeon Kim and Urbain Vaes},
journal= {arXiv preprint arXiv:2505.08669},
year = {2026}
}