学术报告报告题目：Iteration-Complexity of First-Order Augmented Lagrangian Methods for Convex Conic Programming
报 告 人：吕召松 教授（西蒙弗雷泽大学）
报告时间：2019年7 月11日（星期四上午）10:00-11:00
报告地点：4号楼318 室
邀 请 人：潘少华 教授
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数学学院
2019年7月9日
报告摘要：
In this talk, we propose some first-order augmented Lagrangian (AL) methods for solving a class of convex conic programming with adaptive update on penalty parameters and inexactness associated with the AL subproblems. We establish their first-order oracle complexity for finding an approximate KKT point. To our best knowledge, our complexity is lowest in the literature for finding such an approximate solution.
报告人简介：
Dr. Zhaosong Lu is a full Professor of Mathematics and an associate faculty member in Statistics and Actuarial Science at Simon Fraser University. 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. He was a Visiting Assistant Professor of Mathematical Sciences at Carnegie Mellon University during 2005-2006. He was also a Visiting Associate Professor at Texas A&M University and Arizona State University, and a Visiting Researcher at Microsoft Research, Redmond during 2012-2013. 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.