Performance evaluation for distributionally robust optimization with binary entries

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...

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Main Authors: Shunichi Ohmori, Kazuho Yoshimoto
Format: Article
Language:English
Published: Balikesir University 2020-09-01
Series:An International Journal of Optimization and Control: Theories & Applications
Subjects:
Online Access:http://www.ijocta.org/index.php/files/article/view/911
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author Shunichi Ohmori
Kazuho Yoshimoto
author_facet Shunichi Ohmori
Kazuho Yoshimoto
author_sort Shunichi Ohmori
collection DOAJ
description 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.
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spelling doaj.art-980244932de24aa6a364ce0cd0ce09802023-02-15T16:19:27ZengBalikesir UniversityAn International Journal of Optimization and Control: Theories & Applications2146-09572146-57032020-09-0111110.11121/ijocta.01.2021.00911Performance evaluation for distributionally robust optimization with binary entriesShunichi Ohmori0Kazuho Yoshimoto1Waseda UniversityWaseda UniversityWe 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.http://www.ijocta.org/index.php/files/article/view/911Distributionally Robust OptimizationRobust OptimizationStochastic ProgrammingConvex Optimization
spellingShingle Shunichi Ohmori
Kazuho Yoshimoto
Performance evaluation for distributionally robust optimization with binary entries
An International Journal of Optimization and Control: Theories & Applications
Distributionally Robust Optimization
Robust Optimization
Stochastic Programming
Convex Optimization
title Performance evaluation for distributionally robust optimization with binary entries
title_full Performance evaluation for distributionally robust optimization with binary entries
title_fullStr Performance evaluation for distributionally robust optimization with binary entries
title_full_unstemmed Performance evaluation for distributionally robust optimization with binary entries
title_short Performance evaluation for distributionally robust optimization with binary entries
title_sort performance evaluation for distributionally robust optimization with binary entries
topic Distributionally Robust Optimization
Robust Optimization
Stochastic Programming
Convex Optimization
url http://www.ijocta.org/index.php/files/article/view/911
work_keys_str_mv AT shunichiohmori performanceevaluationfordistributionallyrobustoptimizationwithbinaryentries
AT kazuhoyoshimoto performanceevaluationfordistributionallyrobustoptimizationwithbinaryentries