@Article{NMTMA-13-4,
author = {Zhang, Guofeng and Yong, Qian and Zhaozheng, Liang and Zhang, Guofeng and Zhaozheng, Liang},
title = {Regularized DPSS Preconditioners for Singular Saddle Point Problems},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2020},
volume = {13},
number = {4},
pages = {986--1006},
abstract = {<p style="text-align: justify;">Recently, Cao proposed a regularized deteriorated positive and skew-Hermitian splitting (RDPSS) preconditioner for the non-Hermitian nonsingular saddle point problem. In this paper, 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 unconditionally. 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.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.OA-2019-0123},
url = {https://global-sci.com/article/90377/regularized-dpss-preconditioners-for-singular-saddle-point-problems}
}