New Discrete-Time Recurrent Neural Network Proposal for Quadratic Optimization With General Linear Constraints
María José Pérez-Ilzarbe · IEEE Transactions on Neural Networks and Learning Systems · 2012
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.