On the recurrent neural networks for solving general quadratic programming problems

Quadratic programming problems are a widespread class of nonlinear programming problems with many practical applications. The case of inequality constraints have been considered in a previous author´s paper. In this contribution, an extension of these results for the case of inequality and equality constraints is presented. Based on an equivalent formulation of the Kuhn-Tucker conditions, a new neural network for solving general quadratic programming problems, for the case of both inequality and equality constraints, is proposed. Two theorems for global stability and convergence of this network are given as well. The presented network has lower complexity for implementations and the examples confirm its effectiveness. Simulation results based on SIMULINK® models are given and compared.