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