报告题目 (Title)：Stochastic approximation methods for nonconvex constrained optimization （非凸约束优化问题的随机逼近算法）
报告人 (Speaker)：王晓 教授（中山大学）
报告时间 (Time)：2026年4月11日（周六）15:00
报告地点 (Place)：校本部F309
邀请人(Inviter)：徐姿 教授
主办部门：理学院数学系
报告摘要：
Nonconvex constrained optimization is a vital research area within the optimization community, encompassing a wide range of applications across various fields. However, addressing nonconvex constrained optimization presents significant challenges due to the large-scale data and inherent uncertainties as well as potentially nonconvex functional constraints in optimization models. In this talk, I will report our recent progress on stochastic approximation methods for nonconvex constrained optimization that include established complexity bounds and/or convergence properties.
