报告题目：Variance-reduced First-order Methods for Stochastic Optimization with Deterministic Constraints

报 告人：Zhaosong Lu（University of Minnesota）

报告时间：2024年12月23日14:30-15:30

地 点：数学与统计学院245会议室

报告摘要：We consider stochastic optimization problems with deterministic constraints. Existing methods typically focus on finding an approximate stochastic solution that ensures the expected constraint violations and optimality conditions meet a prescribed accuracy. However, such an approximate solution can possibly lead to significant constraint violations in practice. To address this issue, we propose variance-reduced first-order methods that treat the objective and constraints differently. Under suitable assumptions, our proposed methods achieve stronger approximate stochastic solutions with complexity guarantees, offering more reliable constraint satisfaction than existing approaches.

Zhaosong Luis a Full Professor in the Department of Industrial and Systems Engineering at the University of Minnesota. He received PhD in operations research from Georgia Institute of Technology. His research interests include theory and algorithms for continuous optimization, and applications in data science and machine learning. He has published numerous papers in top-tier journals of his research areas such as SIAM Journal on Optimization, SIAM Journal on Numerical Analysis, SIAM Journal on Scientific Computing, SIAM Journal on Matrix Analysis and Application, Mathematical Programming, and Mathematics of Operations Research. His research has been supported by NSERC and NSF. He was a finalist of INFORMS George Nicholson Prize. He also served on this prize committee in the past. Additionally, he has served as an Associate Editor for SIAM Journal on Optimization, Computational Optimization and Applications, and Big Data and Information Analytics.