Sinkhorn Distributionally Robust Optimization

Entropy-Regularized Wasserstein Distributionally Robust Optimization Uncertainty in data poses a central challenge in operations research. Distributionally robust optimization (DRO) offers a principled framework for addressing this challenge by producing solutions resilient to distributional variations. Among various DRO approaches, the Wasserstein DRO has received significant attention though its computational efficiency relies on stringent assumptions, and its worst case distributions are typically discrete. In “Sinkhorn Distributionally Robust Optimization,” Wang, Gao, and Xie leverage the Sinkhorn distance—an entropy-regularized variant of the Wasserstein distance—to more realistically model uncertainty, enhancing computational efficiency. The authors establish a strong duality reformulation and propose a first order stochastic mirror descent algorithm with provable complexity guarantees for general loss functions. Unlike Wasserstein DRO, Sinkhorn DRO yields continuous worst case distributions, offering a more flexible representation of practical uncertainties. Extensive experiments in the newsvendor problem, portfolio optimization, and adversarial classification demonstrate its superior performance in both out-of-sample performance and efficiency.