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A stochastic proximal point algorithm: Convergence and application to convex optimization

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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.

Original languageEnglish
Title of host publication2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479919635
DOIs
Publication statusPublished - 1 Jan 2016
Event6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 - Cancun, Mexico
Duration: 13 Dec 201516 Dec 2015

Publication series

Name2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Volume2016-January

Conference

Conference6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
Country/TerritoryMexico
CityCancun
Period13/12/1516/12/15

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