Performance evaluation for distributionally robust optimization with uncertain binary entries

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Abstract

We consider the data-driven stochastic programming problem with binary entries where the probability of existence of each entry is not known, instead realization of data is provided. We applied the distributionally robust optimization technique to minimize the worst-case expected cost taken over the ambiguity set based on the Kullback-Leibler divergence. We investigate the out-of-sample performance of the resulting optimal decision and analyze its dependence on the sparsity of the problem.

Original languageEnglish
Pages (from-to)1-9
Number of pages9
JournalInternational Journal of Optimization and Control: Theories and Applications
Volume11
Issue number1
DOIs
Publication statusPublished - 2020

Keywords

  • Convex optimization
  • Distributionally robust optimization
  • Robust optimization
  • Stochastic programming

ASJC Scopus subject areas

  • Control and Optimization
  • Applied Mathematics

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