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Activity Number: 71
Type: Contributed
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract #317320
Title: Divergence-Constrained Distributionally Robust Optimization: Data-Driven Strategy
Author(s): Anand Vidyashankar* and Jie Xu
Companies: George Mason University and George Mason University
Keywords: phi-divergence ; robust KDE ; Hellinger distance ; Distributionally robust optimization ; Legendre-Fenchel transform ; computational complexity
Abstract:

Distributionally robust optimization (DRO) problems have received much attention in recent years where the so-called ambiguity is defined using moments or Kullback-Leibler divergence. In this presentaion, we describe a new approach for construction of ambiguity sets using phi-divergences and their related statistically robust estimates. We formulate the resulting minimax problem as a convex optimization problem and study the statistical properties of the optimizers. Additionally, we describe a useful algorithm for implementation and also study the complexity of the algorithm. We present several examples and simulation results to illustrate the ideas.

Authors who are presenting talks have a * after their name.

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