Linear-quadratic programming and optimal control
SIAM Journal on Control and Optimization
Mathematical Programming: Series A and B
SIAM Journal on Optimization
A general methodology for designing globally convergent optimization neural networks
IEEE Transactions on Neural Networks
Exponential stability of globally projected dynamic systems
IEEE Transactions on Neural Networks
A novel neural network for nonlinear convex programming
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 Systems, Man, and Cybernetics, Part B: Cybernetics
A new one-layer neural network for linear and quadratic programming
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this paper, a new neural network for a class of variational inequalities with linear and nonlinear constraints is proposed by converting it into an extended variational inequality. The proposed neural network with the asymmetric mapping is proved to be stable in the sense of Lyapunov and converge to a solution of the original problem within a finite time under a weaker co-coercivity condition by using a convex energy function. Meanwhile, the finite-time convergence for the proposed network with the gradient mapping is also shown under some mild conditions. Compared with the existing neural networks, the new model is suitable to parallel implementation with lower complexity, and can be applied to solve some nonmonotone problems. The validity and transient behavior of the proposed neural network are demonstrated by numerical examples.