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Journals / An International Journal of Optimization and Control: Theories & Applications (IJOCTA) / 2021 / Cilt: 11 - Sayı: 1

Performance evaluation for distributionally robust optimization with uncertain binary entries

Shunichi OHMORI
Kazuho YOSHIMOTO
Pages
1–9
Publication Date
2022-10-11
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

We investigate the out-of-sample performance of the resulting optimal decision and analyze its dependence on the sparsity of the problem.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:We consider the data-driven stochastic programming problem with binary en tries where the probability of existence of each entry is not known, instead realization of data is provided.
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Key Findings & Data:We applied the distributionally robust opti mization technique to minimize the worst-case expected cost taken over the ambiguity set based on the Kullback-Leibler divergence.
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Conclusion & Impact:We investigate the out-of-sample performance of the resulting optimal decision and analyze its dependence on the sparsity of the problem.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

We consider the data-driven stochastic programming problem with binary en tries where the probability of existence of each entry is not known, instead realization of data is provided. We applied the distributionally robust opti mization 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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Cite This Article

OHMORI, S., YOSHIMOTO, K. (2022). Performance evaluation for distributionally robust optimization with uncertain binary entries. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 11 - Sayı: 1, 1–9. https://ajindex.com/articles/440550

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References (17)

  1. Bertsimas, D., & Kallus, N. (2020). From predictive to prescriptive analytics. Management Science, 66(3), 1025-1044
  2. Smith, J. E., & Winkler, R. L. (2006). The optimizer’s curse: Skepticism and postdecision surprise in decision analysis. Management Science, 52(3), 311-322
  3. Esfahani, P. M., & Kuhn, D. (2018). Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. Mathematical Programming, 171(1-2), 115-166
  4. Charnes, A., & Cooper, W. W. (1959). Chance-constrained programming. Management science, 6(1), 73-79
  5. Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations research, 21(5), 1154-1157
  6. Ben-Tal, A., & Nemirovski, A. (1998). Robust convex optimization. Mathematics of operations research, 23(4), 769-805
  7. Ben-Tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations research letters, 25(1), 1-13
  8. Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming, 88(3), 411-424
  9. El Ghaoui, L., & Lebret, H. (1997). Robust solutions to least-squares problems with uncertain data. SIAM Journal on matrix analysis and applications, 18(4), 1035-1064
  10. El Ghaoui, L., Oustry, F., & Lebret, H. (1998). Robust solutions to uncertain semidefinite programs. SIAM Journal on Optimization, 9(1), 33-52
  11. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53
  12. Ben-Tal, A., Den Hertog, D., & Vial, J. P. (2015). Deriving robust counterparts of nonlinear uncertain inequalities. Mathematical programming, 149(1-2), 265-299
  13. Ben-Tal, A., & Nemirovski, A. (2008). Selected topics in robust convex optimization. Mathematical Programming, 112(1), 125-158
  14. Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2009). Robust optimization (Vol. 28). Princeton University Press
  15. Gorissen, B. L., Yanıkoglu, ˙I., & den Hertog, D. (2015). A practical guide to robust optimization. Omega, 53, 124-137
  16. Gabrel, V., Murat, C., & Thiele, A. (2014). Recent advances in robust optimization: An overview. European journal of operational research, 235(3), 471-483
  17. Sozuer, S., & Thiele, A. C. (2016). The state of robust optimization. In Robustness Analysis in Decision Aiding, Optimization, and Analytics (pp. 89-112). Springe