讲座题目：

Some Nonsmooth Function Classes and Their Optimization

讲座时间：

November 26, 2021 Friday

10:00 am - 11:30 am Beijing Time

Zoom会议注册：

https://zoom.us/meeting/register/tJYvde6vqDwiHtw39uQVr0aRVQC8IR9EQ5z4

演讲人：

Jong-Shi Pang

The Daniel J.Epstein Department of Industrial and Systems Engineering

University of Southern California

美国工程院院士，南加利福尼亚大学工业与系统工程系

主持人：

宋洁长江特聘教授、副院长

北京大学工学院工业工程与管理系

Jong-Shi Pang

美国工程院院士

南加利福尼亚大学工业与系统工程系

演讲学者简介

Peking University ES Seminars

Elected as a member of the National Academy of Engineering in February 2021, Jong-Shi Pang joined the University of Southern California as the Epstein Family Chair and Professor of Industrial and Systems Engineering in August 2013. Prior to this position, he was the Caterpillar Professor and Head of the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champagne for six years between 2007 and 2013. He held the position of the Margaret A.Darrin Distinguished Professor in Applied Mathematics in the Department of Mathematical Sciences and was a Professor of Decision Sciences and Engineering Systems at Rensselaer Polytechnic Institute from 2003 to 2007.He was a Professor in the Department of Mathematical Sciences at the Johns Hopkins University from 1987 to 2003, an Associate Professor and then Professor in the School of Management from 1982 to 1987 at the University of Texas at Dallas, and an Assistant and then an Associate Professor in the Graduate Schoolof Industrial Administration at Carnegie-Mellon University from 1977 to 1982. During 1999 and 2001 (full time) and 2002 (part-time), he was a Program Director in the Division of Mathematical Sciences at the National Science Foundation. Professor Pang has served as the Department Academic Advisor of the Department of Mathematics at the Hong Kong Polytechnic University. He has given many distinguished lectures at universities worldwide and plenary lectures at international conferences.

讲座摘要

Peking University ES Seminars

Optimization problems with coupled nonsmoothness and nonconvexity are pervasive in statistical learning and many engineering areas. They are very challenging to analyze and solve. In particular, since the computation of their minimizers, both local and global, is generally intractable, one should settle for computable solutions with guaranteed properties and practical significance. In the case when these problems arise from empirical risk minimization in statistical estimation,inferences should be applied to the computed solutions to bridge the gap between statistical analysis and computational results. This talk gives an overview of several nonsmooth function classes and their connections and sketches an iterative surrogation-based algorithm for the minimization of on eparticular class of non-Clarke regular composite optimization problems. We will also very briefly touch on the general surrogation approach supplemented by exact penalization to handle challenging constraints. This talk is based on the monograph titled “Modern Nonconvex Nondifferentiable Optimization” joint with Professor Ying Cui at the University of Minnesota, to be published in mid-November 2021.

欢迎各位同仁莅临！

Peking University ES Seminars

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