Performance evaluation for distributionally robust optimization with uncertain binary entries

Shunichi Ohmori*, Kazuho Yoshimoto

*この研究の対応する著者

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)1-9
ページ数9
ジャーナルInternational Journal of Optimization and Control: Theories and Applications
11
1
DOI
出版ステータスPublished - 2020

ASJC Scopus subject areas

  • 制御と最適化
  • 応用数学

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