In this paper, we consider Tsallis entropic regularized optimal transport and discuss the convergence rate as the regularization parameter $\varepsilon$ goes to $0$. In particular, we establish the convergence rate of the Tsallis entropic regularized optimal transport using the quantization and shadow arguments developed by Eckstein--Nutz. We compare this to the convergence rate of the entropic regularized optimal transport with Kullback--Leibler (KL) divergence and show that KL is the fastest convergence rate in terms of Tsallis relative entropy.
翻译:在本文中,我们考虑Tsallis熵正则化最优传输,并讨论当正则化参数 $\varepsilon$ 趋近于 $0$ 时的收敛速率。特别地,我们使用Eckstein-Nutz发展的量子化和阴影方法来建立Tsallis熵正则化最优传输的收敛速率。我们将其与Kullback-Leibler(KL)距离正则化最优传输的收敛速率进行比较,并展示在Tsallis相对熵的意义下KL具备最快的收敛速率。