Solving Pseudomonotone Variational Inequalities and Pseudoconvex Optimization Problems Using the Projection Neural Network

In recent years, a recurrent neural network called projection neural network was proposed for solving monotone variational inequalities and related convex optimization problems. In this paper, we show that the projection neural network can also be used to solve pseudomonotone variational inequalitie...

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Veröffentlicht in:	IEEE transactions on neural networks Jg. 17; H. 6; S. 1487 - 1499
Hauptverfasser:	Hu, Xiaolin, Wang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.11.2006 Institute of Electrical and Electronics Engineers
Schlagworte:	Algorithms Applied sciences Artificial intelligence Artificial neural networks Asymptotic properties Asymptotic stability Circuits Componentwise pseudomonotone variational inequality Computer science; control theory; systems Connectionism. Neural networks Constraint optimization Constraints Convergence Exact sciences and technology global asymptotic stability Inequalities Information Storage and Retrieval - methods Iterative algorithms Neural networks Neural Networks (Computer) Optimization Pattern Recognition, Automated - methods Projection projection neural network pseudoconvex optimization pseudomonotone variational inequality Recurrent neural networks Signal processing algorithms Signal Processing, Computer-Assisted Stability Telecommunication traffic Monotonic function Recurrent neural nets Componentwise pseudomonotone variational inequality global asymptotic stability pseudoconvex optimization Lyapunov method Neural network Convex programming Constrained optimization projection neural network Asymptotic stability Variational inequality pseudomonotone variational inequality Monotonicity Lyapunov function Mathematical programming
ISSN:	1045-9227, 1941-0093
Online-Zugang:	Volltext
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