Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets

Distributionally robust optimization has grown into one of the most popular approaches in addressing optimization under uncertainty. As its key ingredient, the ambiguity set specifies several types of distributional information about the ambiguous true distribution. In “Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets,” Z. Chen, M. Sim, and H. Xu contribute to the existing ambiguity sets in the literature, by proposing a new class of ambiguity sets that encompasses a potentially infinite number of expectation constraints. This class has the benefits of, among other things, a tighter approximation of stochastic independence that is ubiquitous in characterizing uncertainty. The authors propose an algorithm involving a greedy improvement procedure to solve the corresponding distributionally robust optimization problem. In their computational study, the authors are able to obtain significantly improved solutions using this algorithm.