题目：Dynamic Stochastic Approximation for Multi-stage Stochastic Optimization with Applications in Asset Allocation

时间：2019年7月8日 15:00-16:30

地点：suncitygroup太阳新城官网 F301会议室

邀请人：蒋祖华 教授（工业工程与管理系）

Biography

Guanghui (George) Lan is an Associate Professor and A. Russell Chandler III Career
Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology since January 2016. Before that he had served as a faculty member in the Department of Industrial and Systems Engineering at the University of Florida from 2009 to 2015, after he earned his Ph.D. degree from Georgia Institute of Technology in August, 2009. He graduated with a master degree in Mechanical Engineering from Shanghai Jiao Tong University. His main research interests lie in optimization and machine learning. His research has been supported by the National Science Foundation, Office of Naval Research and Army Research Office. The academic honors that he received include the INFORMS Computing Society Student Paper Competition First Place (2008), INFORMS George Nicholson Paper Competition Second Place (2008), Mathematical Optimization Society Tucker Prize Finalist (2012), INFORMS Junior Faculty Interest Group (JFIG) Paper Competition First Place (2012) and the National Science Foundation CAREER Award (2013). Dr. Lan serves as the associate editor for a few leading optimization journals including Mathematical Programming, SIAM Journal on Optimization and Computational Optimization and Applications.

Abstract

In this talk, we first review some basic modeling techniques and solution methods for multi-stage stochastic optimization which has wide applications in operations management, such as inventory control and asset allocation. We then present a new stochastic first-order method, namely the dynamic stochastic approximation (DSA) algorithm, for solving these types of stochastic optimization problems. In particular, we show that DSA will be efficient in solving problems with a large number of decision variables, but a relatively small number of stages. We illustrate the advantages of this method over existing ones for solving a classic multi-stage asset allocation problem.