Convergence rate of Tsallis entropic regularized optimal transport
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909339282833408 |
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| author | Suguro, Takeshi Yachimura, Toshiaki |
| author_facet | Suguro, Takeshi Yachimura, Toshiaki |
| contents | In this paper, we study the Tsallis entropic regularized optimal transport in the continuous setting and establish fundamental results such as the $Γ$-convergence of the Tsallis regularized optimal transport to the Monge--Kantorovich problem as the regularization parameter tends to zero. In addition, using the quantization and shadow arguments developed by Eckstein--Nutz, we derive the convergence rate of the Tsallis entropic regularization and provide explicit constants. Furthermore, we compare these results with the well-known case of the Kullback--Leibler (KL) divergence regularization and show that the KL regularization achieves the fastest convergence rate in the Tsallis framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_06616 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Convergence rate of Tsallis entropic regularized optimal transport Suguro, Takeshi Yachimura, Toshiaki Optimization and Control Functional Analysis Probability Machine Learning In this paper, we study the Tsallis entropic regularized optimal transport in the continuous setting and establish fundamental results such as the $Γ$-convergence of the Tsallis regularized optimal transport to the Monge--Kantorovich problem as the regularization parameter tends to zero. In addition, using the quantization and shadow arguments developed by Eckstein--Nutz, we derive the convergence rate of the Tsallis entropic regularization and provide explicit constants. Furthermore, we compare these results with the well-known case of the Kullback--Leibler (KL) divergence regularization and show that the KL regularization achieves the fastest convergence rate in the Tsallis framework. |
| title | Convergence rate of Tsallis entropic regularized optimal transport |
| topic | Optimization and Control Functional Analysis Probability Machine Learning |
| url | https://arxiv.org/abs/2304.06616 |