English

Convergence rate of Tsallis entropic regularized optimal transport

Optimization and Control 2024-10-10 v2 Functional Analysis Probability Machine Learning

Abstract

In this paper, we study the Tsallis entropic regularized optimal transport in the continuous setting and establish fundamental results such as the Γ\Gamma-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.

Keywords

Cite

@article{arxiv.2304.06616,
  title  = {Convergence rate of Tsallis entropic regularized optimal transport},
  author = {Takeshi Suguro and Toshiaki Yachimura},
  journal= {arXiv preprint arXiv:2304.06616},
  year   = {2024}
}

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23 pages