A project neural network for solving degenerate convex quadratic program

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 QQ 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 RnRn 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.