An Efficient Neurodynamic Scheme for Solving a Class of Nonconvex Nonlinear Optimization Problems

By p-power (or partial p-power) transformation, the Lagrangian function in non- convex optimization problem becomes locally convex. In this paper, we present a neural network based on an NCP function for solving the nonconvex optimization problem. An im- portant feature of this neural network is the one-to-one correspondence between its equilibria and KKT points of the nonconvex optimization problem. the proposed neural network is proved to be stable and convergent to an optimal solution of the original problem. Finally, an examples is provided to show the applicability of the proposed neural network.