Dr. Ted Ralphs, Professor, Department of Industrial and Systems Engineering, Lehigh University, Multilevel Optimization and Game Theory: Applications and Algorithms, 15:00–16:00, March 14th, 2018, Room South 529, Shunde Building 2018.03.13

【Title】Multilevel Optimization and Game Theory: Applications and Algorithms
【Speaker】Dr. Ted Ralphs, Professor, Department of Industrial and Systems Engineering, Lehigh University
【Host】Dr. Xiaolei Xie
【Time】15:00–16:00, March 14th, 2018
【Location】 Room South 529, Shunde Building
【Abstract】This talk addresses applications of and solution methods for certain optimization problems arising in game theory. We focus on the case of Stackelberg games in which one player, designated as the "leader," must decide on an optimal course of action under the assumption that the second player, designated as the "follower," will react in accordance with a different, possibly competing, objective. Analysis of such models is necessary in a wide array of applications and requires a deep examination of the duality that arises in the theory of optimization. We discuss applications of this framework and how these problems are solved in practice.
【Short Bio】Dr. Ted Ralphs received his Ph.D. in Operations Research from Cornell University in 1995. He is currently a professor in the Department of Industrial and Systems Engineering (ISE) at Lehigh University, where he is director of the Laboratory for Computational Optimization Research at Lehigh (COR@L) and chair of Lehigh's Research Computing Steering Committee. He is a co-founder of the COIN-OR Foundation, a non-profit foundation promoting the development of open source software for operations research and is currently chair of the Technical Leadership Council and a member of the Strategic Leadership Board, as well as project manager of a number of projects hosted in the COIN-OR open source software repository. His research interests include development of methodology for solving discrete optimization problems, including those with multiple levels or multiple objectives; development of parallel search algorithms; development of open source software; and applications of discrete optimization.
 
All interested are welcome!

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