Improved Complexity Analysis of the Sinkhorn and Greenkhorn Algorithms for Optimal Transport
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Abstract (arXiv)
The Sinkhorn algorithm is a widely used method for solving the optimal transport problem, and the Greenkhorn algorithm is one of its variants. While there are modified versions of these two algorithms whose computational complexities are O(n2|C|_∞2log n/varepsilon2) to achieve an varepsilon-accuracy, to the best of our knowledge, the existing complexities for the vanilla versions are O(n2|C|_∞3log n/varepsilon3). In this paper we fill this gap and show that the complexities of the vanilla Sinkhorn and Greenkhorn algorithms are indeed O(n2|C|_∞2log n/varepsilon2). The analysis relies on the equicontinuity of the dual variables for the discrete entropic regularized optimal transport problem, which is of independent interest.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2023.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 3.
6 papers.
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- 2023Lower Complexity Adaptation for Empirical Entropic Optimal Transportcited by 12
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- 2025Bias as a Virtue: Rethinking Generalization under Distribution Shifts31 May 2025
- 2025Hierarchical Refinement: Optimal Transport to Infinity and Beyond4 Mar 2025
- 2024Learning diffusion at lightspeed18 Jun 2024
- 2024A Universal Metric of Dataset Similarity for Cross-silo Federated Learning29 Apr 2024
- 2023Lower Complexity Adaptation for Empirical Entropic Optimal Transport23 Jun 2023