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Neural Computing and Applications

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01-08-2013 | Original Article

A new result for projection neural networks to solve linear variational inequalities and related optimization problems

Authors: Bonan Huang, Huaguang Zhang, Dawei Gong, Zhanshan Wang

Published in: Neural Computing and Applications | Issue 2/2013

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Abstract

In recent years, a projection neural network was proposed for solving linear variational inequality (LVI) problems and related optimization problems, which required the monotonicity of LVI to guarantee its convergence to the optimal solution. In this paper, we present a new result on the global exponential convergence of the projection neural network. Unlike existing convergence results for the projection neural network, our main result does not assume the monotonicity of LVI problems. Therefore, the projection neural network can be further guaranteed to solve a class of non-monotone LVI and non-convex optimization problems. Numerical examples illustrate the effectiveness of the obtained result.

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Title: A new result for projection neural networks to solve linear variational inequalities and related optimization problems
Authors: Bonan Huang
Huaguang Zhang
Dawei Gong
Zhanshan Wang
Publication date: 01-08-2013
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 2/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0918-1

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