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 -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}
}
Comments
23 pages