Forward-reflected-backward method with variance reduction

We propose a variance reduced algorithm for solving monotone variational inequalities. Without assuming strong monotonicity, cocoercivity, or boundedness of the domain, we prove almost sure convergence of the iterates generated by the algorithm to a solution. In the monotone case, the ergodic averag...

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Bibliographic Details
Published in:	Computational optimization and applications Vol. 80; no. 2; pp. 321 - 346
Main Authors:	Alacaoglu, Ahmet, Malitsky, Yura, Cevher, Volkan
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.11.2021 Springer Nature B.V
Subjects:	Algorithms Convergence Convex and Discrete Geometry Inclusions Inequalities Management Science Mathematics Mathematics and Statistics Operations Research Operations Research/Decision Theory Optimization Statistics Finite-sum structure Monotone inclusions Stochastic variance reduction Variational inequalities Saddle point problems
ISSN:	0926-6003, 1573-2894, 1573-2894
Online Access:	Get full text
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