报告人：王晓 教授
报告题目：Stochastic approximation methods for nonconvex constrained optimization
报告时间：2026年5月25日（周一）上午10：30
报告地点：腾讯会议：617-562-709
主办单位：数学与统计学院、数学研究院、科学技术研究院
报告人简介：
王晓，中山大学教授、博士生导师。研究方向为大规模非凸优化算法和理论。部分成果发表在SIAM系列期刊、Math. Oper. Res.、Math. Comp.、J. Mach. Learn. Res.等期刊。入选国家级青年人才计划。曾荣获中国工业与应用数学学会应用数学青年科技奖、中国运筹学会青年科技奖。目前担任中国运筹学会理事、中国工业与应用数学学会优化及其应用专业委员会（筹）秘书长、广东省运筹学会副理事长。
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.
