Distributionally Robust Optimization: A Review
It provides a comprehensive review for researchers in operations research and statistical learning, but it is incremental as it summarizes existing work without new results.
This paper surveys distributionally robust optimization (DRO), a modeling framework that addresses uncertainty in optimization by considering worst-case scenarios over a set of probability distributions, and reviews its connections to concepts like robust optimization and risk-aversion.
The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization.