【学术会议】Distributionally Robust Stochastic and Online Optimization
Title: Distributionally Robust Stochastic and Online Optimization
Speaker: Prof. Yinyu Ye, Stanford University
Time and Date: 3:00 pm – 4:00 pm, Friday, September 14, 2018
Venue: Room 103, Zhi Xin Building
I will discuss situations We present decision/optimization models/problems driven by uncertain and online data, and show how analytical models and computational algorithms can be used to achieve solution efficiency and near optimality.
First, we describe the so-called Distributionally or Likelihood Robust optimization (DRO) models and their algorithms in dealing stochastic decision problems when the exact uncertainty distribution is unknown but certain statistical moments and samples can be estimated.
Secondly, when decisions are made in presence of high dimensional stochastic data, handling joint distribution of correlated random variables can present a formidable task, both in terms of sampling and estimation as well as algorithmic complexity. A common heuristic is to estimate only marginal distributions and substitute joint distribution by independent (product) distribution. Here, we study possible loss incurred on ignoring correlations through the DRO approach, and quantify that loss as Price of Correlations (POC).
Thirdly, we describe an online combinatorial auction problem using online linear programming technologies. We discuss near-optimal algorithms for solving this surprisingly general class of online problems under the assumption of random order of arrivals and some conditions on the data and size of the problem.
Yinyu Ye is currently the K.T. Li Chair Professor of Engineering at Department of Management Science and Engineering and Institute of Computational and Mathematical Engineering, 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. His current research interests include Continuous and Discrete Optimization, Data Science and Application, Algorithm Design and Analysis, Computational Game/Market Equilibrium, Metric Distance Geometry, Dynamic Resource Allocation, and Stochastic and Robust Decision Making, etc. He is an Fellow since 2012, and has received several academic awards including: the 2009 John von Neumann Theory Prize, the 2015 SPS Signal Processing Magazine Best Paper Award, the winner of the 2014 SIAM Optimization Prize, the inaugural 2012 ISMP Tseng Lectureship Prize, the inaugural 2006 Farkas Prize on Optimization, the 2009 IBM Faculty Award, etc. He has also worked on many industrial projects and served as the chief scientists for several companies.
Institute for Data and Decision Analytics
The Chinese University of Hong Kong, Shenzhen