Inverse-free Dual Neural Networks for Online Solution of Strictly Convex Quadratic Programming

2007 ◽  
Author(s):  
Yunong Zhang ◽  
Zhonghua Li ◽  
Hong-Zhou Tan
Keyword(s):  
Neural Networks ◽  
Quadratic Programming ◽  
Convex Quadratic Programming ◽  
Strictly Convex

NOVEL EXPONENTIAL STABILITY CONDITIONS FOR A CLASS OF INTERVAL PROJECTION NEURAL NETWORKS

2009 ◽  
Vol 02 (03) ◽  
pp. 287-297 ◽  
Author(s):  
ZIXIN LIU ◽  
SHU LÜ ◽  
SHOUMING ZHONG
Keyword(s):  
Neural Networks ◽  
Quadratic Programming ◽  
Exponential Stability ◽  
Equilibrium Point ◽  
Convex Quadratic Programming ◽  
Stability Conditions ◽  
Lyapunov Functionals ◽  
Numerical Example ◽  
Grönwall Inequality ◽  
Quadratic Programming Problems

In this paper, a class of interval projection neural networks for solving quadratic programming problems are investigated. By using Gronwall inequality and constructing appropriate Lyapunov functionals, several novel conditions are derived to guarantee the exponential stability of the equilibrium point. Compared with previous results, the conclusions obtained here are suitable not only to convex quadratic programming problems but also to degenerate quadratic programming problems, and the conditions are more weaker than the earlier results reported in the literature. In addition, one numerical example is discussed to illustrate the validity of the main results.

scholarly journals A characterization of the weighted version of McEliece–Rodemich–Rumsey–Schrijver number based on convex quadratic programming

2015 ◽  
Vol 07 (04) ◽  
pp. 1550050
Author(s):  
Carlos J. Luz
Keyword(s):  
Quadratic Programming ◽  
Discrete Math ◽  
Convex Quadratic Programming ◽  
Stability Number ◽  
Weighted Version ◽  
Similar Characterization ◽  
Number Formula ◽  
Weighted Stability ◽  
Theta Number

For any graph [Formula: see text] Luz and Schrijver [A convex quadratic characterization of the Lovász theta number, SIAM J. Discrete Math. 19(2) (2005) 382–387] introduced a characterization of the Lovász number [Formula: see text] based on convex quadratic programming. A similar characterization is now established for the weighted version of the number [Formula: see text] independently introduced by McEliece, Rodemich, and Rumsey [The Lovász bound and some generalizations, J. Combin. Inform. Syst. Sci. 3 (1978) 134–152] and Schrijver [A Comparison of the Delsarte and Lovász bounds, IEEE Trans. Inform. Theory 25(4) (1979) 425–429]. Also, a class of graphs for which the weighted version of [Formula: see text] coincides with the weighted stability number is characterized.

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