Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Linear programming and network flows (2nd ed.)
Linear programming and network flows (2nd ed.)
Mathematical Programming: Series A and B
Foundations of robotics: analysis and control
Foundations of robotics: analysis and control
Mathematical Programming: Series A and B
Modified Projection-Type Methods for Monotone Variational Inequalities
SIAM Journal on Control and Optimization
A class of combined iterative methods for solving variational inequalities
Journal of Optimization Theory and Applications
On the stability of globally projected dynamical systems
Journal of Optimization Theory and Applications
Improvements of some projection methods for monotone nonlinear variational inequalities
Journal of Optimization Theory and Applications
Numerical Mathematics (Texts in Applied Mathematics)
Numerical Mathematics (Texts in Applied Mathematics)
A new neural network for solving nonlinear projection equations
Neural Networks
The Projection Neural Network for Solving Convex Nonlinear Programming
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Efficient recurrent neural network model for the solution of general nonlinear optimization problems
Optimization Methods & Software
A dynamical model for solving degenerate quadratic minimax problems with constraints
Journal of Computational and Applied Mathematics
A capable neural network model for solving the maximum flow problem
Journal of Computational and Applied Mathematics
A one-layer recurrent neural network for support vector machine learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Improved neural network for SVM learning
IEEE Transactions on Neural Networks
A high-performance feedback neural network for solving convex nonlinear programming problems
IEEE Transactions on Neural Networks
A novel neural network for nonlinear convex programming
IEEE Transactions on Neural Networks
A recurrent neural network for solving nonlinear convex programs subject to linear constraints
IEEE Transactions on Neural Networks
A novel neural network for variational inequalities with linear and nonlinear constraints
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
On solving constrained optimization problems with neural networks: a penalty method approach
IEEE Transactions on Neural Networks
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In this paper, a neural network model is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle invariance principle to solve general convex nonlinear programming (GCNLP) problems. Based on the Saddle point theorem, the equilibrium point of the proposed neural network is proved to be equivalent to the optimal solution of the GCNLP problem. By employing Lyapunov function approach, 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 original problem. The simulation results also show that the proposed neural network is feasible and efficient.