A Sub-Additive DC Approach to the Complementarity Problem

In this article, we propose a new merit function based on sub-additive functions for solving a nonlinear complementarity problem (NCP). This leads to consider an optimization problem that is equivalent to the NCP. In the case of a concave NCP this optimization problem is a Difference of Convex (DC) program and we can therefore use DC Algorithm to locally solve it. We prove that in the case of a concave monotone NCP, it is sufficient to compute a stationary point of the optimization problem to obtain a solution of the complementarity problem. In the case of a general NCP, assuming that a DC decomposition of the complementarity problem is known, we propose a penalization technique to reformulate the optimization problem as a DC program and prove that local minima of this penalized problem are solutions of the NCP. Numerical results on linear complementarity problems and absolute value equations show that our method is promising.