A NOVEL RECURRENT NEURAL NETWORK FOR SOLVING MLCPsAND ITS APPLICATION TO LINEAR AND QUADRATIC PROGRAMMING PROBLEMS

Sohrab Effati (), Abbas Ghomashi () and Masumeh Abbasi ()
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Sohrab Effati: Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
Abbas Ghomashi: Department of Applied Mathematics, Islamic Azad University, Kermanshah Branch, Kermanshah, Iran
Masumeh Abbasi: Department of Applied Mathematics, Islamic Azad University, Kermanshah Branch, Kermanshah, Iran

Asia-Pacific Journal of Operational Research (APJOR), 2011, vol. 28, issue 04, 523-541

Abstract: In this paper, we present a recurrent neural network for solving mixed linear complementarity problems (MLCPs) with positive semi-definite matrices. The proposed neural network is derived based on an NCP function and has a low complexity respect to the other existing models. In theoretical and numerical aspects, global convergence of the proposed neural network is proved. As an application, we show that the proposed neural network can be used to solve linear and convex quadratic programming problems. The validity and transient behavior of the proposed neural network are demonstrated by using five numerical examples.

Keywords: NCP functions; dynamical system; linear programming; mixed linear complementarity problem; quadratic programming; stability; global convergence (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:apjorx:v:28:y:2011:i:04:n:s0217595911003223

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DOI: 10.1142/S0217595911003223

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