A LQP Based Interior Prediction-Correction Method for Nonlinear Complementarity Problems

Abstract

To solve nonlinear complementarity problems (NCP), at each iteration, the classical proximal point algorithm solves a well-conditioned sub-NCP while the Logarithmic-Quadratic Proximal (LQP) method solves a system of nonlinear equations ($LQP$ $system$). This paper presents a practical LQP method-based prediction-correction method for NCP. The predictor is obtained via solving the $LQP$ $system$ approximately under significantly relaxed restriction, and the new iterate (the corrector) is computed directly by an explicit formula derived from the original LQP method. The implementations are very easy to be carried out. Global convergence of the method is proved under the same mild assumptions as the original LQP method. Finally, numerical results for traffic equilibrium problems are provided to verify that the method is effective for some practical problems.