A discrete-time neural network for solving nonlinear convex problems with hybrid constraints

This paper investigates a discrete-time neural network model for solving nonlinear convex programming problems with hybrid constraints. The neural network finds the solution of both primal and dual problems and converges to the corresponding exact solution globally. We prove here that the proposed neural network is globally exponentially stable. Furthermore, we extend the proposed neural network for solving a class of monotone variational inequality problems with hybrid constraints. Compared with other existing neural networks for solving such problems, the proposed neural network has a low complexity for implementation without a penalty parameter and converge an exact solution to convex problem with hybrid constraints. Some numerical simulations for justifying the theoretical analysis are also given. The numerical simulations are shown that in the new model note only the cost of the hardware implementation is not relatively expensive, but also accuracy of the solution is greatly good.