arXiv Analytics

Sign in

arXiv:2212.06000 [math.OC]AbstractReferencesReviewsResources

On the Convergence Rate of Sinkhorn's Algorithm

Promit Ghosal, Marcel Nutz

Published 2022-12-12Version 1

We study Sinkhorn's algorithm for solving the entropically regularized optimal transport problem. Its iterate $\pi_{t}$ is shown to satisfy $H(\pi_{t}|\pi_{*})+H(\pi_{*}|\pi_{t})=O(t^{-1})$ where $H$ denotes relative entropy and $\pi_{*}$ the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with subgaussian marginals. We also obtain the rate $O(t^{-1})$ for the dual suboptimality and $O(t^{-2})$ for the marginal entropies. More precisely, we derive non-asymptotic bounds, and in contrast to previous results on linear convergence that are limited to bounded costs, our estimates do not deteriorate exponentially with the regularization parameter. We also obtain a stability result for $\pi_{*}$ as a function of the marginals, quantified in relative entropy.

Related articles: Most relevant | Search more
arXiv:2002.06315 [math.OC] (Published 2020-02-15)
Bregman Augmented Lagrangian and Its Acceleration
arXiv:1911.05979 [math.OC] (Published 2019-11-14)
Towards an $O(\frac{1}{t})$ convergence rate for distributed dual averaging
arXiv:1204.0301 [math.OC] (Published 2012-04-02)
Tree Codes Improve Convergence Rate of Consensus Over Erasure Channels