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Stochastic approximation approaches to the stochastic variational inequality problem

Stochastic approximation approaches to the stochastic variational inequality problem
Stochastic approximation approaches to the stochastic variational inequality problem
Stochastic approximation methods have been extensively studied in the literature for solving systems of stochastic equations and stochastic optimization problems where function values and first order derivatives are not observable but can be approximated through simulation. In this paper, we investigate stochastic approximation methods for solving stochastic variational inequality problems (SVIP) where the underlying functions are the expected value of stochastic functions. Two types of methods are proposed: stochastic approximation methods based on projections and stochastic approximation methods based on reformulations of SVIP. Global convergence results of the proposed methods are obtained under appropriate conditions
0018-9286
1462-1475
Jiang, Huoyuan
46b5a1d3-7063-4aec-8b71-9868c87a82f7
Xu, Huifu
d3200e0b-ad1d-4cf7-81aa-48f07fb1f8f5
Jiang, Huoyuan
46b5a1d3-7063-4aec-8b71-9868c87a82f7
Xu, Huifu
d3200e0b-ad1d-4cf7-81aa-48f07fb1f8f5

Jiang, Huoyuan and Xu, Huifu (2008) Stochastic approximation approaches to the stochastic variational inequality problem. IEEE Transactions on Automatic Control, 53 (6), 1462-1475. (doi:10.1109/TAC.2008.925853).

Record type: Article

Abstract

Stochastic approximation methods have been extensively studied in the literature for solving systems of stochastic equations and stochastic optimization problems where function values and first order derivatives are not observable but can be approximated through simulation. In this paper, we investigate stochastic approximation methods for solving stochastic variational inequality problems (SVIP) where the underlying functions are the expected value of stochastic functions. Two types of methods are proposed: stochastic approximation methods based on projections and stochastic approximation methods based on reformulations of SVIP. Global convergence results of the proposed methods are obtained under appropriate conditions

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Published date: July 2008
Organisations: Operational Research

Identifiers

Local EPrints ID: 79538
URI: http://eprints.soton.ac.uk/id/eprint/79538
ISSN: 0018-9286
PURE UUID: 5a382132-77a2-4f52-8887-6c149266cf3f
ORCID for Huifu Xu: ORCID iD orcid.org/0000-0001-8307-2920

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Date deposited: 17 Mar 2010
Last modified: 26 Nov 2019 01:48

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