- Data-Driven Chance Constrained Programs over Wasserstein Balls Zhi Chen (zhi.chenimperial.ac.uk) Daniel Kuhn (daniel.kuhnepfl.ch) Wolfram Wiesemann (wwimperial.ac.uk) 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 $\infty$-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. Keywords: Distributionally robust optimization, ambiguous chance constraints, Wasserstein distance. Category 1: Robust Optimization Category 2: Stochastic Programming Category 3: Integer Programming ((Mixed) Integer Nonlinear Programming ) Citation: Download: [PDF]Entry Submitted: 06/22/2018Entry Accepted: 06/22/2018Entry Last Modified: 08/31/2018Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Optmization Society.