On the Convergence Rate of Sinkhorn’s Algorithm

We study Sinkhorn’s algorithm for solving the entropically regularized optimal transport problem. Its iterate [Formula: see text] is shown to satisfy [Formula: see text], where H denotes relative entropy and [Formula: see text] denotes the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with sub-Gaussian marginals. We also obtain the rate [Formula: see text] for the dual suboptimality and [Formula: see text] for the marginal entropies. More precisely, we derive nonasymptotic 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 [Formula: see text] as a function of the marginals quantified in relative entropy. Funding: P. Ghosal was supported by the National Science Foundation [Grant DMS-2153661]. M. Nutz was supported by the National Science Foundation [Grants DMS-1812661, DMS-2106056, and DMS-2407074].

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Publication Details

Journal
Mathematics of Operations Research
Published
2025-05-08
DOI
https://doi.org/10.1287/moor.2024.0427
Citations
5
Primary Topic
Mathematical Dynamics and Fractals
Type
article
Field-Weighted Citation Impact
18.46
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article

On the Convergence Rate of Sinkhorn’s Algorithm

Promit Ghosal, Marcel Nutz
5 citations
Mathematics of Operations Research
Mathematical Dynamics and Fractals
18.46
article

On the Convergence Rate of Sinkhorn’s Algorithm

Promit Ghosal, Marcel Nutz
article en
5 citations

Abstract

We study Sinkhorn’s algorithm for solving the entropically regularized optimal transport problem. Its iterate [Formula: see text] is shown to satisfy [Formula: see text], where H denotes relative entropy and [Formula: see text] denotes the optimal coupling. This holds for a large class of cost functions and marginals, including quadratic cost with sub-Gaussian marginals. We also obtain the rate [Formula: see text] for the dual suboptimality and [Formula: see text] for the marginal entropies. More precisely, we derive nonasymptotic 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 [Formula: see text] as a function of the marginals quantified in relative entropy. Funding: P. Ghosal was supported by the National Science Foundation [Grant DMS-2153661]. M. Nutz was supported by the National Science Foundation [Grants DMS-1812661, DMS-2106056, and DMS-2407074].

Mathematics of Operations Research
University of Chicago (US), Columbia University (US)
Openalex Percentile: Top 0%
Mathematical Dynamics and Fractals
18.46
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