A novel neural network for solving convex quadratic programming problems subject to equality and inequality constraints

This paper proposes a neural network model for solving convex quadratic programming (CQP) problems, whose equilibrium points coincide with Karush-Kuhn-Tucker (KKT) points of the CQP problem. Using the equality transformation and Fischer-Burmeister (FB) function, we construct the neural network model and present the KKT condition for the CQP problem. In contrast to two existing neural networks for solving such problems, the proposed neural network has fewer variables and neurons, which makes circuit realization easier. Moreover, the proposed neural network is asymptotically stable in the sense of Lyapunov such that it converges to an exact optimal solution of the CQP problem. Simulation results are provided to show the feasibility and efficiency of the proposed network.