Matrix analysis
A deterministic annealing neural network for convex programming
Neural Networks
Primal-dual interior-point methods
Primal-dual interior-point methods
A neural network model for non-smooth optimization over a compact convex subset
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A new neural network for solving linear programming problems and its application
IEEE Transactions on Neural Networks
Primal and dual assignment networks
IEEE Transactions on Neural Networks
Analysis and design of primal-dual assignment networks
IEEE Transactions on Neural Networks
A general methodology for designing globally convergent optimization neural networks
IEEE Transactions on Neural Networks
A recurrent neural network for solving nonlinear convex programs subject to linear constraints
IEEE Transactions on Neural Networks
A Simplified Dual Neural Network for Quadratic Programming With Its KWTA Application
IEEE Transactions on Neural Networks
Solving Quadratic Programming Problems by Delayed Projection Neural Network
IEEE Transactions on Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A new one-layer neural network for linear and quadratic programming
IEEE Transactions on Neural Networks
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
A new neural network for solving nonlinear programming problems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part I
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Hi-index | 0.00 |
A one-layer recurrent neural network with a discontinuous activation function is proposed for linear programming. The number of neurons in the neural network is equal to that of decision variables in the linear programming problem. It is proven that the neural network with a sufficiently high gain is globally convergent to the optimal solution. Its application to linear assignment is discussed to demonstrate the utility of the neural network. Several simulation examples are given to show the effectiveness and characteristics of the neural network.