报告题目：A study of the classical consensus-based optimization model

报告人：黄辉，奥地利格拉茨大学

报告时间：2025年4月3日（周四）16:00-17:00

报告地点：腾讯会议：742 787 181

报告摘要：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.

报告人简介：黄辉，奥地利格拉茨大学研究助理，于2017年毕业于清华大学数学科学系，师从简怀玉教授和刘建国教授。博士毕业后曾先后在西蒙弗雷泽大学，慕尼黑工业大学以及卡尔加里大学从事博士后研究工作。主要研究方向为多粒子模型以及他们在最优化领域中的应用，成果发表于J. Mach. Learn. Res., SIAM J. Optim. , SIAM J. Control Optim., Math. Comput., SIAM J. Math. Anal., M3AS, 以及JDE等杂志。

邀请人：非线性分析与PDE团队