报告题目：First-order Methods for Convex Optimization and Monotone Inclusions under Local Lipschitz Conditions

报告时间：2024年6月26日（周三）下午16:00-18:00

报告地点：南一120

主讲人：Zhaosong Lu 教授

摘要：In this talk, I will discuss first-order methods for two problem classes: convex optimization with locally Lipschitz continuous gradient and monotone inclusions with locally Lipschitz continuous point-valued operators. For convex optimization, we will propose a first-order method to find an epsilon-KKT solution, while for monotone inclusions, a primal-dual extrapolation method will be presented to find an epsilon-residual solution. These problem classes extend beyond the well-studied ones in the literature. The proposed methods are parameter-free, with verifiable termination criteria, and exhibit nearly optimal complexity. I will also share some preliminary numerical results to demonstrate their performance.

Zhaosong Lu is 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.