Department of Mathematics
A new one-layer neural network for linear and quadratic programming
In this paper, we present a new neural network for solving linear and quadratic programming problems in real time by introducing some new vectors. The proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem when the objective function is convex on the set defined by equality constraints. Compared with existing one-layer neural networks for quadratic programming problems, the proposed neural network has the least neurons and requires weak stability conditions. The validity and transient behavior of the proposed neural network are demonstrated by some simulation results. © 2006 IEEE.
Convergence, Linear and quadratic programming, Neural network, Stability
Source Publication Title
IEEE Transactions on Neural Networks
Institute of Electrical and Electronics Engineers
Link to Publisher's Edition
Gao, Xingbao, and Li-Zhi Liao. "A new one-layer neural network for linear and quadratic programming." IEEE Transactions on Neural Networks 21.6 (2010): 918-929.