An artificial neural network for solving quadratic zero-one programming problems

Abstract

This paper proposes a neurodynamic approach for solving the quadratic zero-one programming problem with linear constraints. Based on the basic idea of the Scholtes’ relaxation scheme, the original quadratic zero-one programming problem can be approximated by a parameterized nonlinear program. Then, an artificial neural network is proposed to solve the related parameterized nonlinear programming. It is certified that the presented artificial neural network is stable in the sense of Lyapunov. Some numerical experiments are introduced to illustrate our results in the end.