A dynamic system model for solving convex nonlinear optimization problems

This paper proposes a feedback neural network model for solving convex nonlinear programming (CNLP) problems. Under the condition that the objective function is convex and all constraint functions are strictly convex or that the objective function is strictly convex and the constraint function is convex, the proposed neural network is proved to be stable in the sense of Lyapunov and globally convergent to an exact optimal solution of the original problem. The validity and transient behavior of the neural network are demonstrated by using some examples.

► A neural network for solving convex nonlinear programming problems is proposed.
► The essence of neural network for optimization is to establish a dynamic system.
► The proposed dynamical system is proved to be stable in the sense of Lyapunov.
► It is shown that the model is globally convergent to an exact optimal solution of the original problem.
► The validity and transient behavior of the model are demonstrated by using some examples.