Technical Note—Data-Driven Chance Constrained Programs over Wasserstein Balls

Zhi Chen (), Daniel Kuhn () and Wolfram Wiesemann ()
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Zhi Chen: Department of Management Sciences, College of Business, City University of Hong Kong, Kowloon Tong, Hong Kong
Daniel Kuhn: Risk Analytics and Optimization Chair, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
Wolfram Wiesemann: Imperial College Business School, Imperial College London, South Kensington Campus, SW7 2AZ, United Kingdom

Operations Research, 2024, vol. 72, issue 1, 410-424

Abstract: We provide an exact deterministic reformulation for data-driven, chance-constrained 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.

Keywords: Optimization; distributionally robust optimization; ambiguous chance constraints; Wasserstein distance (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:72:y:2024:i:1:p:410-424

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