A Penalty Strategy Combined Varying-Parameter Recurrent Neural Network for Solving Time-Varying Multi-Type Constrained Quadratic Programming Problems

2020 ◽  
pp. 1-12
Author(s):  
Zhijun Zhang ◽  
Song Yang ◽  
Lunan Zheng
Keyword(s):  
Neural Network ◽  
Quadratic Programming ◽  
Recurrent Neural Network ◽  
Time Varying ◽  
Quadratic Programming Problems ◽  
Penalty Strategy

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

2011 ◽  
Vol 28 (04) ◽  
pp. 523-541 ◽  
Author(s):  
SOHRAB EFFATI ◽  
ABBAS GHOMASHI ◽  
MASUMEH ABBASI
Keyword(s):  
Neural Network ◽  
Quadratic Programming ◽  
Recurrent Neural Network ◽  
Complementarity Problems ◽  
Linear Complementarity Problems ◽  
Transient Behavior ◽  
Low Complexity ◽  
Linear Complementarity ◽  
Ncp Function ◽  
Quadratic Programming Problems

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.

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