Open AccessPerformance evaluation for distributionally robust optimization with binary entries
Author(s) -
Shunichi Ohmori,
Kazuho Yoshimoto
Publication year - 2020
Publication title -
an international journal of optimization and control: theories and applications/e-an international journal of optimization and control: theories and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.287
H-Index - 6
eISSN - 2146-5703
pISSN - 2146-0957
DOI - 10.11121/ijocta.01.2021.00911
Subject(s) - robust optimization , ambiguity , stochastic programming , mathematical optimization , binary number , realization (probability) , computer science , divergence (linguistics) , set (abstract data type) , probability distribution , kullback–leibler divergence , linear programming , optimization problem , sample (material) , stochastic optimization , mathematics , artificial intelligence , statistics , linguistics , philosophy , chemistry , arithmetic , chromatography , programming language
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.