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Volume 15, Issue 2
A Variant Modified Skew-Normal Splitting Iterative Method for Non-Hermitian Positive Definite Linear Systems

Rui Li, Jun-Feng Yin & Zhi-Lin Li

Numer. Math. Theor. Meth. Appl., 15 (2022), pp. 510-529.

Published online: 2022-03

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  • Abstract

We propose a variant modified skew-normal splitting iterative method to solve a class of large sparse non-Hermitian positive definite linear systems. Applying the preconditioning technique we also construct the preconditioned version of the proposed method. Theoretical analysis shows that the proposed method is unconditionally convergent even when the real part and the imaginary part of the coefficient matrix are non-symmetric. Meanwhile, when the real part and the imaginary part of the coefficient matrix are symmetric positive definite, we prove that the preconditioned variant modified skew-normal splitting iterative method will also unconditionally converge. Numerical experiments are presented to illustrate the efficiency of the proposed method and show better performance of it when compared with some other methods.

  • AMS Subject Headings

65F10, 65F15, 65T10

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{NMTMA-15-510, author = {Li , RuiYin , Jun-Feng and Li , Zhi-Lin}, title = {A Variant Modified Skew-Normal Splitting Iterative Method for Non-Hermitian Positive Definite Linear Systems}, journal = {Numerical Mathematics: Theory, Methods and Applications}, year = {2022}, volume = {15}, number = {2}, pages = {510--529}, abstract = {

We propose a variant modified skew-normal splitting iterative method to solve a class of large sparse non-Hermitian positive definite linear systems. Applying the preconditioning technique we also construct the preconditioned version of the proposed method. Theoretical analysis shows that the proposed method is unconditionally convergent even when the real part and the imaginary part of the coefficient matrix are non-symmetric. Meanwhile, when the real part and the imaginary part of the coefficient matrix are symmetric positive definite, we prove that the preconditioned variant modified skew-normal splitting iterative method will also unconditionally converge. Numerical experiments are presented to illustrate the efficiency of the proposed method and show better performance of it when compared with some other methods.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.OA-2021-0038}, url = {http://global-sci.org/intro/article_detail/nmtma/20362.html} }
TY - JOUR T1 - A Variant Modified Skew-Normal Splitting Iterative Method for Non-Hermitian Positive Definite Linear Systems AU - Li , Rui AU - Yin , Jun-Feng AU - Li , Zhi-Lin JO - Numerical Mathematics: Theory, Methods and Applications VL - 2 SP - 510 EP - 529 PY - 2022 DA - 2022/03 SN - 15 DO - http://doi.org/10.4208/nmtma.OA-2021-0038 UR - https://global-sci.org/intro/article_detail/nmtma/20362.html KW - Non-Hermitian matrix, skew-normal splitting, precondition, complex linear system. AB -

We propose a variant modified skew-normal splitting iterative method to solve a class of large sparse non-Hermitian positive definite linear systems. Applying the preconditioning technique we also construct the preconditioned version of the proposed method. Theoretical analysis shows that the proposed method is unconditionally convergent even when the real part and the imaginary part of the coefficient matrix are non-symmetric. Meanwhile, when the real part and the imaginary part of the coefficient matrix are symmetric positive definite, we prove that the preconditioned variant modified skew-normal splitting iterative method will also unconditionally converge. Numerical experiments are presented to illustrate the efficiency of the proposed method and show better performance of it when compared with some other methods.

Rui Li, Jun-Feng Yin & Zhi-Lin Li. (2022). A Variant Modified Skew-Normal Splitting Iterative Method for Non-Hermitian Positive Definite Linear Systems. Numerical Mathematics: Theory, Methods and Applications. 15 (2). 510-529. doi:10.4208/nmtma.OA-2021-0038
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