Stochastic Approximations and Perturbations in Forward-Backward Splitting for Monotone Operators

Abstract

We investigate the asymptotic behavior of a stochastic version of the forward-backward splitting algorithm for finding a zero of the sum of a maximally monotone set-valued operator and a cocoercive operator in Hilbert spaces. Our general setting features stochastic approximations of the cocoercive operator and stochastic perturbations in the evaluation of the resolvents of the set-valued operator. In addition, relaxations and not necessarily vanishing proximal parameters are allowed. Weak and strong almost sure convergence properties of the iterates is established under mild conditions on the underlying stochastic processes. Leveraging these results, we also establish the almost sure convergence of the iterates of a stochastic variant of a primal-dual proximal splitting method for composite minimization problems.

References in corpus (2)

• A Stochastic forward-backward splitting method for solving monotone inclusions in Hilbert spaces
• A stochastic inertial forward-backward splitting algorithm for multivariate monotone inclusions

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