报告题目：Stochastic approximation methods for nonconvex constrained optimization

报 告 人：王晓 教授（中山大学）

报告时间：2026年4月24日（星期五）9:30—10:30

报告地点：6776永利集团114（小报告厅）      

校内联系人：张立卫 教授         联系方式：84708351-8320

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.

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。