DocumentCode
88683
Title
New Discrete-Time Recurrent Neural Network Proposal for Quadratic Optimization With General Linear Constraints
Author
Perez-Ilzarbe, M.J.
Author_Institution
Dept. de Autom. y Comput., Univ. Publica de Navarra, Pamplona, Spain
Volume
24
Issue
2
fYear
2013
fDate
Feb. 2013
Firstpage
322
Lastpage
328
Abstract
In this brief, the quadratic problem with general linear constraints is reformulated using the Wolfe dual theory, and a very simple discrete-time recurrent neural network is proved to be able to solve it. Conditions that guarantee global convergence of this network to the constrained minimum are developed. The computational complexity of the method is analyzed, and experimental work is presented that shows its high efficiency.
Keywords
computational complexity; convergence; mathematics computing; quadratic programming; recurrent neural nets; Wolfe dual theory; computational complexity; discrete-time recurrent neural network; general linear constraint; global convergence; quadratic optimization; Bismuth; Computational modeling; Convergence; Learning systems; Optimization; Trajectory; Vectors; Discrete time; global convergence; hybrid constraints; neural networks; quadratic optimization;
fLanguage
English
Journal_Title
Neural Networks and Learning Systems, IEEE Transactions on
Publisher
ieee
ISSN
2162-237X
Type
jour
DOI
10.1109/TNNLS.2012.2223484
Filename
6376234
Link To Document