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 th...
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Balikesir University
2020-09-01
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| Series: | An International Journal of Optimization and Control: Theories & Applications |
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| Online Access: | http://www.ijocta.org/index.php/files/article/view/911 |
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doaj-980244932de24aa6a364ce0cd0ce09802021-03-09T02:14:05ZengBalikesir UniversityAn International Journal of Optimization and Control: Theories & Applications 2146-09572146-57032020-09-0111110.11121/ijocta.01.2021.00911Performance evaluation for distributionally robust optimization with binary entriesShunichi Ohmori0Kazuho Yoshimoto1Waseda UniversityWaseda University 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. http://www.ijocta.org/index.php/files/article/view/911Distributionally Robust OptimizationRobust OptimizationStochastic ProgrammingConvex Optimization |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Shunichi Ohmori Kazuho Yoshimoto |
| 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 |
| author_facet |
Shunichi Ohmori Kazuho Yoshimoto |
| author_sort |
Shunichi Ohmori |
| title |
Performance evaluation for distributionally robust optimization with binary entries |
| title_short |
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_sort |
performance evaluation for distributionally robust optimization with binary entries |
| publisher |
Balikesir University |
| series |
An International Journal of Optimization and Control: Theories & Applications |
| issn |
2146-0957 2146-5703 |
| publishDate |
2020-09-01 |
| 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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| 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 |
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1724228257068351488 |