Sinkhorn Distributionally Robust Optimization

We study distributionally robust optimization with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization. We derive a convex programming dual reformulation for general nominal distributions, transport costs, and loss functions. To solve the dual reformulation, we develop a stochastic mirror descent algorithm with biased subgradient estimators and derive its computational complexity guarantees. Finally, we provide numerical examples using synthetic and real data to demonstrate its superior performance.

@article{wang2025_2109.11926,
  title={ Sinkhorn Distributionally Robust Optimization },
  author={ Jie Wang and Rui Gao and Yao Xie },
  journal={arXiv preprint arXiv:2109.11926},
  year={ 2025 }
}