Data-Driven Optimization

【时间】2014年7月2日（周三）10:00-12:00

【地点】清华经管学院 伟伦楼335

【语言】英语

【摘要】We present few optimization models and algorithms dealing with uncertain or massive data. Specifically, we discuss Distributionally Robust Decision Models, where many problems can be efficiently solved when the associated uncertain data possess no priori distributions; Near-Optimal Online Linear Programming Algorithms, where the constraint matrix is revealed column by column along with the objective function, and a decision has to be made as soon as a variable arrives; Alternating Direction Method of Multipliers (ADMM), where an example is given to show that the direct extension of ADMM for three-block convex minimization problems is not necessarily convergent, and possible convergent variants would be introduced.

【简历】Yinyu Ye is currently the Kwoh-Ting Li Professor in the School of Engineering at the Department of Management Science and Engineering and Institute of Computational and Mathematical Engineering and the Director of the MS&E Industrial Affiliates Program, Stanford University. He received the B.S. degree in System Engineering from the Huazhong University of Science and Technology, China, and the M.S. and Ph.D. degrees in Engineering-Economic Systems and Operations Research from Stanford University. Ye's research interests lie in the areas of optimization, complexity theory, algorithm design and analysis, and applications of mathematical programming, operations research and system engineering. He is also interested in developing optimization software for various real-world applications. Current research topics include Liner Programming Algorithms, Markov Decision Processes, Computational Game/Market Equilibrium, Metric Distance Geometry, Dynamic Resource Allocation, and Stochastic and Robust Decision Making, etc. He is an INFORMS (The Institute for Operations Research and The Management Science) Fellow, and has received several research awards including the inaugural 2012 ISMP Tseng Lectureship Prize for outstanding contribution to continuous optimization, the 2009 John von Neumann Theory Prize for fundamental sustained contributions to theory in Operations Research and the Management Sciences, the inaugural 2006 Farkas prize on Optimization, and the 2009 IBM Faculty Award. He has supervised numerous doctoral students at Stanford who received the 2008 NicholsonPrize and the 2006 and 2010 INFORMS Optimization Prizes for Young Researchers. Ye teaches courses on Optimization, Network and Integer Programming, Semidefinite Programming, etc. He has written extensively on Interior-Point Methods, Approximation Algorithms, Conic Optimization, and their applications; and served as a consultant or technical board member to a variety of industries, including MOSEK.