Variance-Based Subgradient Extragradient Method for Stochastic Variational Inequality Problems

In this paper, we propose a variance-based subgradient extragradient algorithm with line search for stochastic variational inequality problems by aiming at robustness with respect to an unknown Lipschitz constant. This algorithm may be regarded as an integration of a subgradient extragradient algori...

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Veröffentlicht in:	Journal of scientific computing Jg. 89; H. 1; S. 4
Hauptverfasser:	Yang, Zhen-Ping, Zhang, Jin, Wang, Yuliang, Lin, Gui-Hua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2021 Springer Nature B.V
Schlagworte:	Algebra Algorithms Approximation Computational Mathematics and Numerical Analysis Convergence Iterative methods Mathematical analysis Mathematical and Computational Engineering Mathematical and Computational Physics Mathematics Mathematics and Statistics Numerical analysis Random variables Regularization methods Robustness (mathematics) Theoretical Stochastic variational inequality 65K15 Bounded proximal error bound 90C33 Variance reduction Convergence rate Stochastic approximation 90C15 Subgradient extragradient algorithm 62L20
ISSN:	0885-7474, 1573-7691
Online-Zugang:	Volltext
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