A novel neural network model for solving a class of nonlinear semidefinite programming problems

In this paper, we describe a dynamic optimization technique for solving a class of nonlinear semidefinite programming based on Karush-Kuhn-Tucker optimality conditions. By employing Lyapunov function approach, it is investigated that the suggested neural network is stable in the sense of Lyapunov and globally convergent to an exact optimal solution of the original problem. The effectiveness of the proposed method is demonstrated by two numerical simulations.