A neural network model for solving convex quadratic programming problems with some applications

This paper presents a capable neural network for solving strictly convex quadratic programming (SCQP) problems with general linear constraints. The proposed neural network model is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. A block diagram of the proposed model is also given. Several applicable examples further show the correctness of the results and the good performance of the model.