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                                                                                  馬里蘭大學Michael Fu教授系列講座:Simulation Optimization: Overview and Recent Developments in Gradient-based Methods
                                                                                  時間:2016-05-23 來源:綜合辦 編輯:zhbgs 訪問次數:700

                                                                                  主題: Simulation Optimization: Overview and Recent Developments in Gradient-based Methods

                                                                                  時間: 2016年6月2日 週四 下午 15:00-17:00
                                                                                         6月3日 週五 下午 15:00-17:00

                                                                                  地點: 钱柜游戏玉泉校區 钱柜老樓 414


                                                                                  聯繫人: 宋春躍 cysong@iipc.zju.edu.cn

                                                                                   

                                                                                  Abstract:
                                                                                  In the first talk, we provide a tutorial overview of most of the main approaches currently used for carrying out simulation optimization, which includes stochastic approximation, response surface methodology, and sample average approximation, as well as some random search methods. Simple examples will be used throughout to illustrate the methods, and the main challenges in applying each approach will be discussed, along with some applications. In the second talk, we will provide a tutorial overview on stochastic gradient estimation techniques such as perturbation analysis, the likelihood ratio or score function method, and weak derivatives (also known as measure-valued differentiation), and describe some recent research on incorporating direct gradient estimates in simulation optimization.


                                                                                  Brief Bio.
                                                                                  Michael C. Fu is Ralph J. Tyser Professor of Management Science in the Decision, Operations and Information Technologies department of the Robert H. Smith School of Business, with a joint appointment in the Institute for Systems Research and an affiliate appointment in the Department of Electrical & Computer Engineering (both in the Clark School of Engineering), all at the University of Maryland, College Park.
                                                                                  He received degrees in mathematics and EECS from MIT, and his Ph.D. in applied mathematics from Harvard University. His research interests include simulation optimization and applied probability, particularly with applications towards supply chain management and financial engineering. He has published six books: Conditional Monte Carlo: Gradient Estimation and Optimization Applications, which was awarded the INFORMS Simulation Society's Outstanding Publication Award in 1998, Simulation-based Algorithms for Markov Decision Processes, Perspectives in Operations Research, Advances in Mathematical Finance, Encyclopedia of Operations Research and Management Science (3rd edition), and Handbook of Simulation Optimization. In 2004 he was named a Distinguished Scholar-Teacher at the University of Maryland. From September 2010 through August 2012, he served as Program Director for the Operations Research Program at the National Science Foundation. He is a Fellow of INFORMS and IEEE.