Linear-quadratic programming and optimal control
SIAM Journal on Control and Optimization
Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
Solving nonlinear complementarity problems with neural networks: a reformulation method approach
Journal of Computational and Applied Mathematics
Convex Optimization
A novel neural network for a class of convex quadratic minimax problems
Neural Computation
Numerical Mathematics (Texts in Applied Mathematics)
Numerical Mathematics (Texts in Applied Mathematics)
A new neural network for solving nonlinear projection equations
Neural Networks
Efficient recurrent neural network model for the solution of general nonlinear optimization problems
Optimization Methods & Software
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
A high-performance feedback neural network for solving convex nonlinear programming problems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A novel neural network for nonlinear convex programming
IEEE Transactions on Neural Networks
A neural network for a class of convex quadratic minimax problems with constraints
IEEE Transactions on Neural Networks
Neural network for quadratic optimization with bound constraints
IEEE Transactions on Neural Networks
A capable neural network model for solving the maximum flow problem
Journal of Computational and Applied Mathematics
Solving general convex nonlinear optimization problems by an efficient neurodynamic model
Engineering Applications of Artificial Intelligence
An application of a merit function for solving convex programming problems
Computers and Industrial Engineering
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This paper presents a new neural network model for solving degenerate quadratic minimax (DQM) problems. On the basis of the saddle point theorem, optimization theory, convex analysis theory, Lyapunov stability theory and LaSalle invariance principle, the equilibrium point of the proposed network is proved to be equivalent to the optimal solution of the DQM problems. It is also shown that the proposed network model is stable in the sense of Lyapunov and it is globally convergent to an exact optimal solution of the original problem. Several illustrative examples are provided to show the feasibility and the efficiency of the proposed method in this paper.