Wasserstein distributionally robust optimization yields data-driven decisions that are computable as convex programs and have finite-sample out-of-sample guarantees, and this tutorial unifies the theory with machine learning applications.
Data-Driven Chance Constrained Programs over Wasserstein Balls
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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. Our reformulation compares favourably to several state-of-the-art data-driven optimization schemes in our numerical experiments.
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Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning
Wasserstein distributionally robust optimization yields data-driven decisions that are computable as convex programs and have finite-sample out-of-sample guarantees, and this tutorial unifies the theory with machine learning applications.