The University of Southampton
University of Southampton Institutional Repository

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

This record has no associated files available for download.

More information

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

Catalogue record

Date deposited: 17 Mar 2010
Last modified: 14 Mar 2024 02:47

Export record

Altmetrics

Contributors

Author: Huoyuan Jiang
Author: Huifu Xu ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×