Parallel and distributed computation: numerical methods
Parallel and distributed computation: numerical methods
A simple and high performance neural network for quadratic programming problems
Applied Mathematics and Computation
A dual neural network for kinematic control of redundant robotmanipulators
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
Neural network for quadratic optimization with bound constraints
IEEE Transactions on Neural Networks
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
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 develops a project neural network for solving degenerate quadratic programming problems with general linear constraints. Compared with the existing neural networks for solving strict convex quadratic program, the proposed neural networks for solving degenerate convex quadratic program has a wider domain for implementation. In the theoretical aspects, the proposed neural network is shown to have complete convergence and finite-time convergence. Moreover, the nonsingular part of the output trajectory respect to Q has an exponentially convergent rate. Furthermore, through any equilibrium point of the proposed neural network, the information that whether the objective function can reach its minimum of R^n within the constraint conditions can be obtained easily. Illustrative examples further show the correctness of the results in this paper, and the good performance of the proposed neural network.