时间：2010年6月30日（周三） 上午10：00-11：30
所在：伟易博治理学院二号楼 217 课堂
Title: Stochastic Gradient Estimation: Survey and Recent Research
Abstract:
We review the main techniques for stochastic gradient estimation, where direct unbiased estimators are obtained for simulation models.The techniques discussed are perturbation analysis, the likelihood ratio (score function) method, and weak derivatives. Some recent research results are also presented.
MICHAEL C. FU is Ralph J. Tyser Professor of Management Science in the Robert H. Smith School of Business, with a joint appointment in the Institute for Systems Research and affiliate faculty appointment in the Department of Electrical and Computer Engineering, all at the University of Maryland, where he has been since 1989.He received degrees in mathematics and EE/CS from MIT, and a Ph.D. in applied mathematics from Harvard University. His research interests include simulation optimization and applied probability, with applications in supply chain management and financial engineering. He has published four books: Conditional Monte Carlo: Gradient Estimation and Optimization Applications (1997), which received the INFORMS Simulation Society Outstanding Publication Award in 1998; Simulation-based Algorithms for Markov Decision Processes (2007); Perspectives in Operations Research (2006); and Advances in Mathematical Finance (2007). He served as Stochastic Models and Simulation Department Editor of Management Science from 2006--2008, and IEEE Transactions on Automatic Control, and as Simulation Area Editor of Operations Research 2000--2005, and is Program Chair for the 2011 Winter Simulation Conference. He was named a University of Maryland Distinguished Scholar-Teacher for 2004--2005, and is a Fellow of INFORMS and IEEE.
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