Numer. Math. Theor. Meth. Appl., 10 (2017), pp. 167-185.
Published online: 2017-10
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We generalize the accelerated Hermitian and skew-Hermitian splitting(AHSS) iteration methods for large sparse saddle-point problems. These methods involve four iteration parameters whose special choices can recover the preconditioned HSS and accelerated HSS iteration methods. Also a new efficient case is introduced and we theoretically prove that this new method converges to the unique solution of the saddle-point problem. Numerical experiments are used to further examine the effectiveness and robustness of iterations.
}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.2017.m1524}, url = {http://global-sci.org/intro/article_detail/nmtma/12341.html} }We generalize the accelerated Hermitian and skew-Hermitian splitting(AHSS) iteration methods for large sparse saddle-point problems. These methods involve four iteration parameters whose special choices can recover the preconditioned HSS and accelerated HSS iteration methods. Also a new efficient case is introduced and we theoretically prove that this new method converges to the unique solution of the saddle-point problem. Numerical experiments are used to further examine the effectiveness and robustness of iterations.