Solving Multiextremal Problems by Using Recurrent Neural Networks.

In this paper, a neural network model for solving a class of multiextremal smooth nonconvex constrained optimization problems is proposed. Neural network is designed in such a way that its equilibrium points coincide with the local and global optimal solutions of the corresponding optimization problem. Based on the suitable underestimators for the Lagrangian of the problem, one give geometric criteria for an equilibrium point to be a global minimizer of multiextremal constrained optimization problem with or without bounds on the variables. Both necessary and sufficient global optimality conditions for a class of multiextremal constrained optimization problems are presented to determine a global optimal solution. By study of the resulting dynamic system, it is shown that under given assumptions, steady states of the dynamic system are stable and trajectories of the proposed model converge to the local and global optimal solutions of the problem. Numerical results are given and related graphs are depicted to illustrate the global convergence and performance of the solver for multiextremal constrained optimization problems.