Performance evaluation for distributionally robust optimization with uncertain binary entries

Shunichi Ohmori*, Kazuho Yoshimoto

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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
Issue number1
Publication statusPublished - 2021


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

ASJC Scopus subject areas

  • Control and Optimization
  • Applied Mathematics


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