Convex Chance-Constrained Programs with Wasserstein Ambiguity

Chance constraints, under either known or ambiguous distributions, yield nonconvex feasible regions in general. This paper identifies sufficient conditions that lead to convex feasible regions of chance constraints with Wasserstein ambiguity. Notably, it generalizes the seminal work of other authors, which established the convexity of joint chance constraints under log-concave probability distributions, to the distributionally robust and distributionally optimistic settings.