English

Uniform-in-time weak propagation of chaos for consensus-based optimization

Optimization and Control 2025-02-04 v1 Machine Learning Probability

Abstract

We study the uniform-in-time weak propagation of chaos for the consensus-based optimization (CBO) method on a bounded searching domain. We apply the methodology for studying long-time behaviors of interacting particle systems developed in the work of Delarue and Tse (ArXiv:2104.14973). Our work shows that the weak error has order O(N1)O(N^{-1}) uniformly in time, where NN denotes the number of particles. The main strategy behind the proofs are the decomposition of the weak errors using the linearized Fokker-Planck equations and the exponential decay of their Sobolev norms. Consequently, our result leads to the joint convergence of the empirical distribution of the CBO particle system to the Dirac-delta distribution at the global minimizer in population size and running time in Wasserstein-type metrics.

Keywords

Cite

@article{arxiv.2502.00582,
  title  = {Uniform-in-time weak propagation of chaos for consensus-based optimization},
  author = {Erhan Bayraktar and Ibrahim Ekren and Hongyi Zhou},
  journal= {arXiv preprint arXiv:2502.00582},
  year   = {2025}
}

Comments

keywords: Consensus-based optimization, Uniform-in-time propagation of chaos, Weak convergence, Sobolev spaces, Linearized Fokker-Planck equations

R2 v1 2026-06-28T21:29:12.606Z