Abstract: A nongradient neural network is presented for solving a kind of the general linear complementarity problem.With the Lyapunov theorem and LaSalle invariant set principle,the network is proved to be Lyapunov stable and globally convergent to an exact solution of the problem when the matrix M is positive semidefinite.The network can be used to solve the linear complementarity prlblem,and it is simpler than the existing network.Moreover,it includes the network of quadratic optimization problem.Feasi...

Key words: general linear complementarity problem； neural network； stability； convergence；