Solving nonlinear complementarity problems with neural networks: a reformulation method approach
Solving nonlinear complementarity problems with neural networks: a reformulation method approach
In this paper, we present a neural network approach for solving nonlinear complementarity problems. The neural network model is derived from an unconstrained minimization reformulation of the complementarity problem. The existence and the convergence of the trajectory of the neural network are addressed in detail. In addition, we also explore the stability properties, such as the stability in the sense of Lyapunov, the asymptotic stability and the exponential stability, for the neural network model. The theory developed here is also valid for neural network models derived from a number of reformulation methods for nonlinear complementarity problems. Simulation results are also reported.
neural network, nonlinear complementarity problem, stability, reformulation
343-359
Liao, Li-Zhi
c79b29f2-ea1e-4e0e-badc-756956646ee1
Qi, Houduo
e9789eb9-c2bc-4b63-9acb-c7e753cc9a85
Qi, Liqun
69936be7-f1aa-4c1f-b403-5bd5f3ba7d4c
2001
Liao, Li-Zhi
c79b29f2-ea1e-4e0e-badc-756956646ee1
Qi, Houduo
e9789eb9-c2bc-4b63-9acb-c7e753cc9a85
Qi, Liqun
69936be7-f1aa-4c1f-b403-5bd5f3ba7d4c
Liao, Li-Zhi, Qi, Houduo and Qi, Liqun
(2001)
Solving nonlinear complementarity problems with neural networks: a reformulation method approach.
Journal of Computational and Applied Mathematics, 131 (1-2), .
(doi:10.1016/S0377-0427(00)00262-4).
Abstract
In this paper, we present a neural network approach for solving nonlinear complementarity problems. The neural network model is derived from an unconstrained minimization reformulation of the complementarity problem. The existence and the convergence of the trajectory of the neural network are addressed in detail. In addition, we also explore the stability properties, such as the stability in the sense of Lyapunov, the asymptotic stability and the exponential stability, for the neural network model. The theory developed here is also valid for neural network models derived from a number of reformulation methods for nonlinear complementarity problems. Simulation results are also reported.
This record has no associated files available for download.
More information
Published date: 2001
Keywords:
neural network, nonlinear complementarity problem, stability, reformulation
Organisations:
Operational Research
Identifiers
Local EPrints ID: 29637
URI: http://eprints.soton.ac.uk/id/eprint/29637
ISSN: 0377-0427
PURE UUID: b6ab1f00-b529-4c99-8c38-e3646cf1974a
Catalogue record
Date deposited: 11 May 2006
Last modified: 16 Mar 2024 03:41
Export record
Altmetrics
Contributors
Author:
Li-Zhi Liao
Author:
Liqun Qi
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
