A posteriori error bounds for the linearly-constrained varitional inequality problem
Mathematics of Operations Research
Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Lagrange neural networks for linear programming
Journal of Parallel and Distributed Computing - Special issue on neural computing on massively parallel processing
A deterministic annealing neural network for convex programming
Neural Networks
Nonlinear Control Systems
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
A new neural network for solving linear programming problems and its application
IEEE Transactions on Neural Networks
A high-performance neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
A new neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
Neural network for solving extended linear programming problems
IEEE Transactions on Neural Networks
A general methodology for designing globally convergent optimization neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Neural network for solving linear programming problems with bounded variables
IEEE Transactions on Neural Networks
Stabilizing Lagrange-Type Nonlinear Programming Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
On a Stabilization Problem of Nonlinear Programming Neural Networks
Neural Processing Letters
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By modifying the multipliers associated with inequality constraints, we can directly solve convex programming problem without nonnegative constraints of the multipliers associated with inequality constraints, hence it is no longer necessary to convert the inequality constraints into the equality constraints by using the ‘slack variables’. With this technique, the neural network to solve convex programming problem is constructed, and its stability is analyzed rigorously. Simulation shows that this method is feasible.