@article{a0b2f3e3b1c24b97b4f1777f36af3940,
title = "Performance evaluation for distributionally robust optimization with uncertain binary entries",
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.",
keywords = "Convex optimization, Distributionally robust optimization, Robust optimization, Stochastic programming",
author = "Shunichi Ohmori and Kazuho Yoshimoto",
note = "Funding Information: This work was supported by JSPS KAKENHI Grant Number 19K15243. This work was partly executed under the cooperation of organization between Waseda University and KIOXIA Corporation ( former Toshiba Memory Corporation ). Funding Information: This work was supported by JSPS KAKENHI Grant Number 19K15243. This work was partly executed under the cooperation of organization between Waseda University and KIOXIA Corporation (former Toshiba Memory Corporation). Publisher Copyright: {\textcopyright} This work is licensed under a Creative Commons Attribution 4.0 International License. The authors retain ownership of the copyright for their article, but they allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles in IJOCTA, so long as the original authors and source are credited. To see the complete license contents, please visit http://creativecommons.org/licenses/by/4.0/.",
year = "2021",
doi = "10.11121/IJOCTA.01.2021.00911",
language = "English",
volume = "11",
pages = "1--9",
journal = "International Journal of Optimization and Control: Theories and Applications",
issn = "2146-0957",
publisher = "Balikesir University",
number = "1",
}