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Volume 29, Issue 4
HSS Method with a Complex Parameter for the Solution of Complex Linear System

Guiding Gu

J. Comp. Math., 29 (2011), pp. 441-457.

Published online: 2011-08

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  • Abstract

In this paper, a complex parameter is employed in the Hermitian and skew-Hermitian splitting (HSS) method (Bai, Golub and Ng: SIAM J. Matrix Anal. Appl., 24(2003), 603-626) for solving the complex linear system $Ax=f$. The convergence of the resulting method is proved when the spectrum of the matrix $A$ lie in the right upper (or lower) part of the complex plane. We also derive an upper bound of the spectral radius of the HSS iteration matrix, and an estimated optimal parameter $α$(denoted by $α_{est}$) of this upper bound is presented. Numerical experiments on two modified model problems show that the HSS method with $α_{est}$ has a smaller spectral radius than that with the real parameter which minimizes the corresponding upper bound. In particular, for the 'dominant' imaginary part of the matrix $A$, this improvement is considerable. We also test the GMRES method preconditioned by the HSS preconditioning matrix with our parameter $α_{est}$.

  • AMS Subject Headings

65F10, 65Y20.

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{JCM-29-441, author = {Guiding Gu}, title = {HSS Method with a Complex Parameter for the Solution of Complex Linear System}, journal = {Journal of Computational Mathematics}, year = {2011}, volume = {29}, number = {4}, pages = {441--457}, abstract = {

In this paper, a complex parameter is employed in the Hermitian and skew-Hermitian splitting (HSS) method (Bai, Golub and Ng: SIAM J. Matrix Anal. Appl., 24(2003), 603-626) for solving the complex linear system $Ax=f$. The convergence of the resulting method is proved when the spectrum of the matrix $A$ lie in the right upper (or lower) part of the complex plane. We also derive an upper bound of the spectral radius of the HSS iteration matrix, and an estimated optimal parameter $α$(denoted by $α_{est}$) of this upper bound is presented. Numerical experiments on two modified model problems show that the HSS method with $α_{est}$ has a smaller spectral radius than that with the real parameter which minimizes the corresponding upper bound. In particular, for the 'dominant' imaginary part of the matrix $A$, this improvement is considerable. We also test the GMRES method preconditioned by the HSS preconditioning matrix with our parameter $α_{est}$.

}, issn = {1991-7139}, doi = {https://doi.org/10.4208/jcm.1103-m3422}, url = {http://global-sci.org/intro/article_detail/jcm/8486.html} }
TY - JOUR T1 - HSS Method with a Complex Parameter for the Solution of Complex Linear System AU - Guiding Gu JO - Journal of Computational Mathematics VL - 4 SP - 441 EP - 457 PY - 2011 DA - 2011/08 SN - 29 DO - http://doi.org/10.4208/jcm.1103-m3422 UR - https://global-sci.org/intro/article_detail/jcm/8486.html KW - Hermitian matrix, Skew-Hermitian matrix, Splitting iteration method, Complex linear system, Complex parameter. AB -

In this paper, a complex parameter is employed in the Hermitian and skew-Hermitian splitting (HSS) method (Bai, Golub and Ng: SIAM J. Matrix Anal. Appl., 24(2003), 603-626) for solving the complex linear system $Ax=f$. The convergence of the resulting method is proved when the spectrum of the matrix $A$ lie in the right upper (or lower) part of the complex plane. We also derive an upper bound of the spectral radius of the HSS iteration matrix, and an estimated optimal parameter $α$(denoted by $α_{est}$) of this upper bound is presented. Numerical experiments on two modified model problems show that the HSS method with $α_{est}$ has a smaller spectral radius than that with the real parameter which minimizes the corresponding upper bound. In particular, for the 'dominant' imaginary part of the matrix $A$, this improvement is considerable. We also test the GMRES method preconditioned by the HSS preconditioning matrix with our parameter $α_{est}$.

Guiding Gu. (2011). HSS Method with a Complex Parameter for the Solution of Complex Linear System. Journal of Computational Mathematics. 29 (4). 441-457. doi:10.4208/jcm.1103-m3422
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