A neural network for solving nonlinear convex programming with linear equality and bounded constraints

In this paper, to solve the nonlinear convex programming problems with linear equality and bounded constraints, a new neural network model is constructed. It is proved that if the initial point lies in the linear equality region, the state of the proposed neural network is convergent to an exact optimal solution of the optimization problem. Compared with the existed neural networks, the proposed in this paper has a low model complexity and avoid estimating the penalty parameters in advance. In the end, several numerical simulations illustrate the effectiveness of the proposed neural network.