A Class Of Regularized DPSS Preconditioners For Solving Saddle Point Systems

In many computation science and engineering applications,we need to solve a class of large linear system,which are of the saddle point structure.Recently,Cao proposed a regularized deteriorated positive and skew-Hermitian splitting(RDPSS)preconditioner for the non-Hermitian nonsingular saddle point problem.In this pa-per,we consider applying RDPSS preconditioner to solve the singular saddle point problem.Moreover,we propose a two-parameter accelerated variant of the RDPSS(ARDPSS)preconditioner to further improve its efficiency.Theoretical analysis proves that the RDPSS and ARDPSS methods are semi-convergent uncondition-ally.Some spectral properties of the corresponding preconditioned matrices are analyzed.Numerical experiments indicate that better performance can be achieved when applying the ARDPSS preconditioner to accelerate the GMRES method for solving the singular saddle point problem.