A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization

作者：Luhao Zhang, Jincheng Yang, Rui Gao · 发表于：Operations Research · 年份：2024 · DOI：10.1287/opre.2023.0135 · 被引用次数：20 · 研究领域：Risk and Portfolio Optimization、Fuzzy Systems and Optimization、Probabilistic and Robust Engineering Design

Wasserstein distributionally robust optimization has emerged as a recent topic with broader applications in operations research and machine learning. Various proofs have been presented in the literature, each differing in assumptions and levels of generality. In “A Short and General Duality Proof for Wasserstein Distributionally Robust Optimization,” Zhang, Yang, and Gao present a novel elementary proof that not only shortens existing frameworks but also offers surprising generalizations. Leveraging classical Legendre—Fenchel duality, they demonstrate that strong duality is contingent on a certain interchangeability principle. Moreover, they extend this duality result to encompass risk-averse optimization and globalized distributionally robust counterparts.