Linear programming and network flows (2nd ed.)
Linear programming and network flows (2nd ed.)
A simple and high performance neural network for quadratic programming problems
Applied Mathematics and Computation
Convex Optimization
A double scaling algorithm for the constrained maximum flow problem
Computers and Operations Research
A dynamical model for solving degenerate quadratic minimax problems with constraints
Journal of Computational and Applied Mathematics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Linear and quadratic programming neural network analysis
IEEE Transactions on Neural Networks
Solving Quadratic Programming Problems by Delayed Projection Neural Network
IEEE Transactions on Neural Networks
Solving general convex nonlinear optimization problems by an efficient neurodynamic model
Engineering Applications of Artificial Intelligence
An application of a merit function for solving convex programming problems
Computers and Industrial Engineering
Hi-index | 7.29 |
This paper presents an optimization technique for solving a maximum flow problem arising in widespread applications in a variety of settings. On the basis of the Karush-Kuhn-Tucker (KKT) optimality conditions, a neural network model is constructed. The equilibrium point of the proposed neural network is then proved to be equivalent to the optimal solution of the original problem. It is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the maximum flow problem. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.