Title :
A new neural network for solving linear and quadratic programming problems
Author_Institution :
Dept. of Math., Nanjing Univ. of Posts & Telecommun., China
fDate :
11/1/1996 12:00:00 AM
Abstract :
A new neural network for solving linear and quadratic programming problems is presented and is shown to be globally convergent. The new neural network improves existing neural networks for solving these problems: it avoids the parameter turning problem, it is capable of achieving the exact solutions, and it uses only simple hardware in which no analog multipliers for variables are required. Furthermore, the network solves both the primal problems and their dual problems simultaneously
Keywords :
neural nets; duality; global convergence; linear programming; neural network; parameter turning; quadratic programming; Artificial neural networks; Computer networks; Hopfield neural networks; Linear programming; Neural network hardware; Neural networks; Quadratic programming; Stability; Traveling salesman problems; Turning;
Journal_Title :
Neural Networks, IEEE Transactions on
