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We have decided to discontinue the publication of preprints on our preprint server as of 1 March 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.

MiS Preprint
110/2020

A note on overrelaxation in the Sinkhorn algorithm

Tobias Lehmann, Max von Renesse, Alexander Sambale and André Uschmajew

Abstract

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.

Received:
Dec 23, 2020
Published:
Dec 25, 2020

Related publications

inJournal
2022 Journal Open Access
Tobias Lehmann, Max von Renesse, Alexander Sambale and André Uschmajew

A note on overrelaxation in the Sinkhorn algorithm

In: Optimization letters, 16 (2022) 8, pp. 2209-2220