Linear-quadratic programming and optimal control
SIAM Journal on Control and Optimization
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
On a Generalization of a Normal Map and Equation
SIAM Journal on Control and Optimization
A novel neural network for a class of convex quadratic minimax problems
Neural Computation
Computers & Mathematics with Applications
A dual neural network for kinematic control of redundant robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Recurrent Neural Network for Solving a Class of General Variational Inequalities
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
A neural network for a class of convex quadratic minimax problems with constraints
IEEE Transactions on Neural Networks
Stability analysis for stochastic Cohen-Grossberg neural networks with mixed time delays
IEEE Transactions on Neural Networks
Solving Quadratic Programming Problems by Delayed Projection Neural Network
IEEE Transactions on Neural Networks
Neural network for quadratic optimization with bound constraints
IEEE Transactions on Neural Networks
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
An application of a merit function for solving convex programming problems
Computers and Industrial Engineering
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In this paper, a quadratic minimax problem with linear constraints is studied. The mixed linear constraints and the degeneracy are the two significant characters of the problem considered in this paper. On the basis of the project properties and Lyapunov method, we get the complete convergence and the finite-time convergence of the proposed neural network in this paper. Moreover, we get that the nonsingular parts of the output trajectories respect to Q"1"1 and Q"2"2 are exponentially convergent. Particularly, we also give some analysis to the degenerate quadratic minimax problem without constraints. Furthermore, four illustrative examples are given to show the necessity of the matrix H in the network to solve this problem and the superiority of the network in this paper.