Mathematical Programming: Series A and B
On a Generalization of a Normal Map and Equation
SIAM Journal on Control and Optimization
A Recurrent Neural Network for Solving a Class of General Variational Inequalities
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Estimate of exponential convergence rate and exponential stability for neural networks
IEEE Transactions on Neural Networks
A neural network model for monotone linear asymmetric variational inequalities
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A novel neural network for variational inequalities with linear and nonlinear constraints
IEEE Transactions on Neural Networks
Solving linear programming problems with neural networks: a comparative study
IEEE Transactions on Neural Networks
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Recurrent neural networks have become a prominent tool for optimizations including linear or nonlinear variational inequalities and programming, due to its regular mathematical properties and well-defined parallel structure. This brief presents a general discrete-time recurrent network for linear variational inequalIties and related optimization problems with hybrid constraints. In contrary to the existing discrete-time networks, this geIlleral model can operate not only on bound constraints, but also on hybrid constraints comprised of inequality, equality and bound constraints. The model has dynamical properties of global convergence, asymptotical and exponential convergences under some weaker conditions. Numerical examples demonstrate its efficacy and performance.