Sinkhorn Distributionally Robust Optimization
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866915213336379392 |
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| author | Wang, Jie Gao, Rui Xie, Yao |
| author_facet | Wang, Jie Gao, Rui Xie, Yao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2109_11926 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Sinkhorn Distributionally Robust Optimization Wang, Jie Gao, Rui Xie, Yao Optimization and Control Machine Learning 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. |
| title | Sinkhorn Distributionally Robust Optimization |
| topic | Optimization and Control Machine Learning |
| url | https://arxiv.org/abs/2109.11926 |