Distributionally robust optimization: A review on theory and applications

Abstract

In this paper, we survey the primary research on the theory and applications of distributionally robust optimization (DRO). We start with review-ing the modeling power and computational attractiveness of DRO approaches, induced by the ambiguity sets structure and tractable robust counterpart refor-mulations. Next, we summarize the efficient solution methods, out-of-sample performance guarantee, and convergence analysis. Then, we illustrate some applications of DRO in machine learning and operations research, and finally, we discuss the future research directions.