Sharper Exponential Convergence Rates for Sinkhorn's Algorithm in Continuous Settings

Abstract

We study the convergence rate of Sinkhorn's algorithm for solving entropy-regularized optimal transport problems when at least one of the probability measures, $μ$, admits a density over $\mathbb{R}^d$. For a semi-concave cost function bounded by $c_{\infty}$ and a regularization parameter $λ> 0$, we obtain exponential convergence guarantees on the dual sub-optimality gap with contraction rate polynomial in $λ/c_{\infty}$. This represents an exponential improvement over the known contraction rate $1 - Θ(\exp(-c_{\infty}/λ))$ achievable via Hilbert's projective metric. Specifically, we prove a contraction rate value of $1-Θ(λ^2/c_\infty^2)$ when $μ$ has a bounded log-density. In some cases, such as when $μ$ is log-concave and the cost function is $c(x,y)=-\langle x, y \rangle$, this rate improves to $1-Θ(λ/c_\infty)$. The latter rate matches the one that we derive for the transport between isotropic Gaussian measures, indicating tightness in the dependency in $λ/c_\infty$. Our results are fully non-asymptotic and explicit in all the parameters of the problem.