Abstract: This article presents a neural network model for the optimization of arbitrary convex functions with linear constraints. The equilibrium point of the energy function constructed is the optima] solution of the original problem. The problems, which would arise in conventional neural network optimization methods, are overcome. The neural model is globally stable and can converge to the optimal point. The computer simulation results verify the effectiveness of the method.