Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Sinkhorn Distributionally Robust Optimization. |
| Συγγραφείς: |
Wang, Jie1 (AUTHOR) jwang@cuhk.edu.cn, Gao, Rui2 (AUTHOR) rui.gao@mccombs.utexas.edu, Xie, Yao3 (AUTHOR) yao.xie@isye.gatech.edu |
| Πηγή: |
Operations Research. May/Jun2026, Vol. 74 Issue 3, p1581-1603. 23p. |
| Θεματικοί όροι: |
*Mathematical optimization, Robust optimization, Optimization algorithms, Convex programming |
| Περίληψη: |
Uncertainty in data poses a central challenge in operations research. Distributionally robust optimization (DRO) offers a principled framework for addressing this challenge by producing solutions resilient to distributional variations. Among various DRO approaches, the Wasserstein DRO has received significant attention though its computational efficiency relies on stringent assumptions, and its worst case distributions are typically discrete. In "Sinkhorn Distributionally Robust Optimization," Wang, Gao, and Xie leverage the Sinkhorn distance—an entropy-regularized variant of the Wasserstein distance—to more realistically model uncertainty, enhancing computational efficiency. The authors establish a strong duality reformulation and propose a first order stochastic mirror descent algorithm with provable complexity guarantees for general loss functions. Unlike Wasserstein DRO, Sinkhorn DRO yields continuous worst case distributions, offering a more flexible representation of practical uncertainties. Extensive experiments in the newsvendor problem, portfolio optimization, and adversarial classification demonstrate its superior performance in both out-of-sample performance and efficiency. We study distributionally robust optimization with Sinkhorn distance: a variant of Wasserstein distance based on entropic regularization. We derive a convex programming dual reformulation for general nominal distributions, transport costs, and loss functions. To solve the dual reformulation, we develop a stochastic mirror descent algorithm with biased subgradient estimators and derive its computational complexity guarantees. Finally, we provide numerical examples using synthetic and real data to demonstrate its superior performance. Funding: The work of Y. Xie is partially supported by the National Science Foundation [Grant DMS-2134037] and the Coca-Cola Foundation. Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2023.0294. [ABSTRACT FROM AUTHOR] |
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