Chen, ZhiKuhn, DanielWiesemann, Wolfram2018-08-312018-08-312018-08-31202410.1287/opre.2022.2330https://infoscience.epfl.ch/handle/20.500.14299/1480751809.00210We 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 $\infty$-norm, the cone is the nonnegative orthant, and the chance constrained program can be reformulated as a mixed-integer linear program. Using our reformulation, we show that two popular approximation schemes based on the conditional-value-at-risk and the Bonferroni inequality can perform poorly in practice and that these two schemes are generally incomparable with each other.Distributionally robust optimizationAmbiguous chance constraintsWasserstein distancetext::journal::journal article::research article