Convergence of Regularized Time-Stepping Methods for Differential Variational Inequalities

Abstract

This paper provides convergence analysis of regularized time-stepping methods for the differential variational inequality (DVI), which consists of a system of ordinary differential equations and a parametric variational inequality (PVI) as the constraint. The PVI often has multiple solutions at each step of a time-stepping method, and it is hard to choose an appropriate solution for guaranteeing the convergence. In [L. Han, A. Tiwari, M. K. Camlibel and J.-S. Pang, SIAM J. Numer. Anal., 47 (2009) pp. 3768--3796], the authors proposed to use “least-norm solutions” of parametric linear complementarity problems at each step of the time-stepping method for the monotone linear complementarity system and showed the novelty and advantages of the use of the least-norm solutions. However, in numerical implementation, when the PVI is not monotone and its solution set is not convex, finding a least-norm solution is difficult. This paper extends the Tikhonov regularization approximation to the P$_0$-function DVI, which ensures that the PVI has a unique solution at each step of the regularized time-stepping method. We show the convergence of the regularized time-stepping method to a weak solution of the DVI and present numerical examples to illustrate the convergence theorems.