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

作者：Effati Sohrab^*; Ghomashi Abbas; Abbasi Masumeh

来源：Asia Pacific Journal of Operational Research, 2011, 28(4): 523-541.

DOI：10.1142/S0217595911003223

• NCP functions
• dynamical system
• linear programming
• mixed linear complementarity problem
• quadratic programming
• stability
• global convergence

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.

• 出版日期2011-8