Chaotic iterative methods for the linear complementarity problems

A class of parallel multisplitting chaotic relaxation methods is established for the large sparse linear complementarity problems, and the global and monotone convergence is proved for the H-matrix and the L-matrix classes, respectively. Moreover, comparison theorem is given, which describes the influences of the parameters and the multiple splittings upon the monotone convergence rates of the new methods.