Parallel random block-coordinate forward–backward algorithm: a unified convergence analysis

We study the block-coordinate forward–backward algorithm in which the blocks are updated in a random and possibly parallel manner, according to arbitrary probabilities. The algorithm allows different stepsizes along the block-coordinates to fully exploit the smoothness properties of the objective fu...

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Veröffentlicht in:Mathematical programming Jg. 193; H. 1; S. 225 - 269
Hauptverfasser: Salzo, Saverio, Villa, Silvia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2022
Springer
Springer Nature B.V
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ISSN:0025-5610, 1436-4646
Online-Zugang:Volltext
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Zusammenfassung:We study the block-coordinate forward–backward algorithm in which the blocks are updated in a random and possibly parallel manner, according to arbitrary probabilities. The algorithm allows different stepsizes along the block-coordinates to fully exploit the smoothness properties of the objective function. In the convex case and in an infinite dimensional setting, we establish almost sure weak convergence of the iterates and the asymptotic rate o (1/ n ) for the mean of the function values. We derive linear rates under strong convexity and error bound conditions. Our analysis is based on an abstract convergence principle for stochastic descent algorithms which allows to extend and simplify existing results.
Bibliographie:ObjectType-Article-1
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ISSN:0025-5610
1436-4646
DOI:10.1007/s10107-020-01602-1