A recurrent neural network for nonlinear convex programming

This paper presents a novel recurrent neural network for nonlinear convex programming. Under the condition that the objective function is convex and the constraint set is strictly convex or that the objective function is strictly convex and the constraint set is convex, the proposed neural network is proved to be stable in the sense of Lyapunov and globally convergent to an exact solution. Compared with the existing neural networks for solving such nonlinear optimization problems, the proposed neural network does not require an additional condition on the objective function and has a simple structure for implementation. Simulation results are given to illustrate further the global convergence and performance of the proposed neural network for constrained nonlinear optimization.