Data-Driven Chance Constrained Programs over Wasserstein Balls

• 成果类型: Article
• 署名作者: Chen, Zhi; Kuhn, Daniel; Wiesemann, Wolfram
• 署名单位: City University of Hong Kong; Imperial College London
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.2330
• 发表日期: 2024
• 关键词: distributionally robust optimization

摘要

We provide an exact deterministic reformulation for data-driven, chanceconstrained programs over Wasserstein balls. For individual chance constraints as well as joint chance constraints with right-hand-side uncertainty, our reformulation amounts to a mixed-integer conic program. In the special case of a Wasserstein ball with the 1-norm or the ???-norm, the cone is the nonnegative orthant, and the chance-constrained program can be reformulated as a mixed-integer linear program. Our reformulation compares favorably to several state-of-the-art data-driven optimization schemes in our numerical experiments.