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• W2345322282 abstract "Maximal monotone operators are set-valued mappings which extend (but are not limited to) the notion of subdifferential of a convex function. The proximal point algorithm is a method for finding a zero of a maximal monotone operator. The algorithm consists in fixed point iterations of a mapping called the resolvent which depends on the maximal monotone operator of interest. The paper investigates a stochastic version of the algorithm where the resolvent used at iteration k is associated to one realization of a random maximal monotone operator. We establish the almost sure ergodic convergence of the iterates to a zero of the expectation (in the Aumann sense) of the latter random operator. Application to constrained stochastic optimization is considered." @default.
• W2345322282 created "2016-06-24" @default.
• W2345322282 creator A5024802752 @default.
• W2345322282 date "2015-12-01" @default.
• W2345322282 modified "2023-09-30" @default.
• W2345322282 title "A stochastic proximal point algorithm: convergence and application to convex optimization" @default.
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• W2345322282 doi "https://doi.org/10.1109/camsap.2015.7465295" @default.
• W2345322282 hasPublicationYear "2015" @default.
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