报告题目：First-order Methods for Convex Optimization and Monotone Inclusions under Local Lipschitz Conditions
报告人：吕召松 教授（美国明尼苏达大学）
报告时间：2023年6月21日（周三）下午3:30-4:30
报告地点：数学院425
邀请人：雷渊
报告摘要： 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.
报告人简介：Dr. Zhaosong Lv is a full Professor of University of Minnesota. He received PhD in Operations Research from the School of Industrial and Systems Engineering of Georgia Tech in 2005 under the supervision of Dr. Renato Monteiro and Dr. Arkadi Nemirovski. His research interests include theory and algorithms for continuous optimization, and applications in data analytics, finance, statistics, machine learning, image processing, engineering design, and decision-making under uncertainty. He was a finalist of INFORMS George Nicholson Prize. He has published numerous papers in major 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. He also served on INFORMS George Nicholson Prize Committee in 2014 and 2015. Currently, he is an Associate Editor for SIAM Journal on Optimization, Computational Optimization and Applications, and Big Data and Information Analytics.
