A CLASS OF MULTI-PARAMETER RELAXED PARALLEL MULTISPLITTING METHODS FOR LARGE SPARSE LINEAR COMPLEMENTARITY PROBLEMS

Abstract

We set up a class of multi-parameter relaxed parallel matrix multisplitting methods for solving the linear complementarity problems on the SIMD multiprocessor systems. This class of methods can not only includes all the existing relaxed methods for the linear complementarity problems, but also can yields a lot of novel ones in the sense of multisplitting. Thus, it is reasonably general. We set up the convergence theory of these relaxed methods under the condition that the system matrix is an H-matrix with positive diagonal elements.