Technical Note—Data-Driven Chance Constrained Programs over Wasserstein Balls
研究了在Wasserstein球（以经验分布为中心的分布集合）内，要求决策对每个分布都以高概率可行的数据驱动机会约束规划问题，并给出了精确的混合整数锥规划重写形式。
In the era of modern business analytics, data-driven optimization has emerged as a popular modeling paradigm to transform data into decisions. By constructing an ambiguity set of the potential data-generating distributions and subsequently hedging against all member distributions within this ambiguity set, data-driven optimization effectively combats the ambiguity with which real-life data sets are plagued. Chen et al. (2022) study data-driven, chance-constrained programs in which a decision has to be feasible with high probability under every distribution within a Wasserstein ball centered at the empirical distribution. The authors show that the problem admits an exact deterministic reformulation as a mixed-integer conic program and demonstrate (in numerical experiments) that the reformulation compares favorably to several state-of-the-art data-driven optimization schemes.