A note on overrelaxation in the Sinkhorn algorithm

A note on overrelaxation in the Sinkhorn algorithm

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 pa...

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Hauptverfasser: Lehmann, Tobias, von Renesse, Max-K, Sambale, Alexander, Uschmajew, André
Format: Artikel
Sprache:eng
Schlagworte:
Computer Science - Numerical Analysis
Mathematics - Numerical Analysis
Mathematics - Optimization and Control
Mathematics - Statistics Theory
Statistics - Theory
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Zusammenfassung: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.
DOI:10.48550/arxiv.2012.12562