Title : ( A Novel Recurrent Neural Network for Solving Mlcps and its Application to Linear and Quadratic Programming )
Authors: Sohrab Effati , A. Ghomashi , M. Abbasi ,
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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.
@article{paperid:1025141,
author = {Effati, Sohrab and A. Ghomashi and M. Abbasi},
title = {A Novel Recurrent Neural Network for Solving Mlcps and its Application to Linear and Quadratic Programming},
journal = {Asia-Pacific Journal of Operational Research},
year = {2011},
volume = {28},
number = {4},
month = {November},
issn = {0217-5959},
pages = {523--541},
numpages = {18},
keywords = {NCP functions; dynamical system; linear programming; mixed linear
complementarity problem; quadratic programming; stability; global convergence.},
}
%0 Journal Article
%T A Novel Recurrent Neural Network for Solving Mlcps and its Application to Linear and Quadratic Programming
%A Effati, Sohrab
%A A. Ghomashi
%A M. Abbasi
%J Asia-Pacific Journal of Operational Research
%@ 0217-5959
%D 2011
