j9九游会

报告问题 (Title)：Stochastic approximation methods for nonconvex constrained optimization （非凸约束优化问题的随机迫近算法）

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

报告所在 (Place)：校本部F309

约请人(Inviter)：徐姿 教授

主理部分：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.