AN IDEAL PENALTY FUNCTION FOR CONSTRAINED OPTIMIZATION11This paper has been published in the Journal of the Institute of Mathematics and Its Applications. It is being published here at the invitation of the editors and with the permission of the Institute of Mathematics and Its Applications. There are some small differences which are explained in the ‘Note in Proof.’

1975 ◽  
pp. 121-163 ◽  
Author(s):  
R. Fletcher
Keyword(s):  
Penalty Function

Rule-Based LTV and Penalty Function for Concentration Risk

2012 ◽  
Author(s):  
Yongwoong Lee ◽  
Yiran Zhang ◽  
Ser-Huang Poon
Keyword(s):  
Penalty Function ◽  
Rule Based

IMPROVED NEURAL NETWORKS FOR LINEAR AND NONLINEAR PROGRAMMING

1991 ◽  
Vol 02 (04) ◽  
pp. 331-339 ◽  
Author(s):  
Jiahan Chen ◽  
Michael A. Shanblatt ◽  
Chia-Yiu Maa
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Nonlinear Programming ◽  
Penalty Function ◽  
New Combination ◽  
Optimal Point ◽  
Linear And Nonlinear Programming ◽  
Linear And Nonlinear ◽  
Simulation Results

A method for improving the performance of artificial neural networks for linear and nonlinear programming is presented. By analyzing the behavior of the conventional penalty function, the reason for the inherent degenerating accuracy is discovered. Based on this, a new combination penalty function is proposed which can ensure that the equilibrium point is acceptably close to the optimal point. A known neural network model has been modified by using the new penalty function and the corresponding circuit scheme is given. Simulation results show that the relative error for linear and nonlinear programming is substantially reduced by the new method.

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