A note on overrelaxation in the Sinkhorn algorithm
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Abstract (arXiv)
We derive an a priori parameter range for overrelaxation of the Sinkhorn algorithm, which guarantees global convergence and a strictly faster asymptotic local convergence. Guided by the spectral analysis of the linearized problem we pursue a zero cost procedure to choose a near optimal relaxation parameter.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Dec 2020.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 1; 2026: 1.
7 papers.
- 2023Scalable Optimal Transport Methods in Machine Learning: A Contemporary Surveycited by 27
- 2022Rethinking Initialization of the Sinkhorn Algorithmcited by 14
- 2025On Fitting Flow Models with Large Sinkhorn Couplingscited by 11
- 2024Progressive Entropic Optimal Transport Solverscited by 10
- 2023Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methodscited by 2
- 2023MFMAN-YOLO: A Method for Detecting Pole-like Obstacles in Complex Environmentcited by 0
- 2026FlashSinkhorn 2: Block-Sparse Entropic Optimal Transportcited by 0
- 2026FlashSinkhorn 2: Block-Sparse Entropic Optimal Transport1 Oct 2026
- 2025On Fitting Flow Models with Large Sinkhorn Couplings5 Jun 2025
- 2024Progressive Entropic Optimal Transport Solvers7 Jun 2024
- 2023MFMAN-YOLO: A Method for Detecting Pole-like Obstacles in Complex Environment24 Jul 2023
- 2023Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey8 May 2023
- 2023Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods30 Jan 2023
- 2022Rethinking Initialization of the Sinkhorn Algorithm15 Jun 2022