Convergence rate of Tsallis entropic regularized optimal transport
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Abstract (arXiv)
In this paper, we study the Tsallis entropic regularized optimal transport in the continuous setting and establish fundamental results such as the 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.
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References · 10
- 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
- 2013A survey of the Schr"odinger problem and some of its connections with optimal transportcited by 292
- 2018An explicit analysis of the entropic penalty in linear programmingcited by 24
- 2021Quantitative Stability of Regularized Optimal Transport and Convergence of Sinkhorn's Algorithmcited by 22
- 2021Entropic Optimal Transport: Geometry and Large Deviationscited by 20
- 2016Tsallis Regularized Optimal Transport and Ecological Inferencecited by 15
- 2022Convergence Rates for Regularized Optimal Transport via Quantizationcited by 12
- 2020Optimal Transport losses and Sinkhorn algorithm with general convex regularizationcited by 10
- 2022Convergence rate of general entropic optimal transport costscited by 9
- 2021Relaxation of optimal transport problem via strictly convex functionscited by 1
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