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
1462-1475
Jiang, Huoyuan
46b5a1d3-7063-4aec-8b71-9868c87a82f7
Xu, Huifu
d3200e0b-ad1d-4cf7-81aa-48f07fb1f8f5
July 2008
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), .
(doi:10.1109/TAC.2008.925853).
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
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Local EPrints ID: 79538
URI: http://eprints.soton.ac.uk/id/eprint/79538
ISSN: 0018-9286
PURE UUID: 5a382132-77a2-4f52-8887-6c149266cf3f
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Date deposited: 17 Mar 2010
Last modified: 14 Mar 2024 02:47
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Author:
Huoyuan Jiang
Author:
Huifu Xu
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