Distributionally Robust Stochastic Programming
研究在给定参考概率测度下，不确定性集由与之接近的测度构成的分布鲁棒随机规划，讨论最坏情况泛函的律不变性及两种基本构造。
In this paper we study distributionally robust stochastic programming in a setting where there is a specified reference probability measure and the uncertainty set of probability measures consists of measures in some sense close to the reference measure. We discuss law invariance of the associated worst case functional and consider two basic constructions of such uncertainty sets. Finally we illustrate some implications of the property of law invariance.