学术报告（黄辉 2026.4.27）

摘要：Consensus-based optimization (CBO) is a relatively recent class of particle-based methods for global optimization, inspired by collective behavior in nature and rooted in stochastic dynamics. In this talk, we introduce the classical CBO model, where a swarm of agents iteratively updates their positions under the influence of attraction to a weighted consensus point and random perturbations. We present the mathematical formulation of the CBO model and discuss its key features. In particular, we shall establish its global convergence in mean-field law toward the global minimizer of a given objective function under suitable assumptions.