Resumen

ARENAS, FAVIÁN; PEREZ, ROSANA  y  VIVAS, HEVERT. A neural network model for nonlinear complementarity problems. Integración - UIS [online]. 2016, vol.34, n.2, pp.169-185. ISSN 0120-419X.  https://doi.org/10.18273/revint.v34n2-2016005.

Abstract. In this paper we present a neural network model for solving the nonlinear complementarity problem. This model is derived from an equivalent unconstrained minimization reformulation of the complementarity problem, which is based on a one-parametric class of nonlinear complementarity functions. We establish the existence and convergence of the trajectory of the neural network, and we study its Lyapunov stability, asymptoticstability as well as exponential stability. Numerical tests verify the obtained theoretical results.

Palabras clave : Neural network; nonlinear complementarity problem; stability; reformulation.

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