Convex Chance-Constrained Programs with Wasserstein Ambiguity
研究了在Wasserstein模糊性下机会约束可行域为凸的充分条件，将经典的对数凹分布下联合机会约束的凸性结论推广到分布鲁棒和分布乐观情形。
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.