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doi: 10.3934/naco.2019033

A preconditioned SSOR iteration method for solving complex symmetric system of linear equations

Faculty of Mathematical Sciences, University of Guilan, Rasht, Iran

*Corresponding author

Received  August 2018 Revised  April 2019 Published  May 2019

We present a preconditioned version of the symmetric successive overrelaxation (SSOR) iteration method for a class of complex symmetric linear systems. The convergence results of the proposed method are established and conditions under which the spectral radius of the iteration matrix of the method is smaller than that of the SSOR method are analyzed. Numerical experiments illustrate the theoretical results and depict the efficiency of the new iteration method.

Citation: Tahereh Salimi Siahkolaei, Davod Khojasteh Salkuyeh. A preconditioned SSOR iteration method for solving complex symmetric system of linear equations. Numerical Algebra, Control & Optimization, doi: 10.3934/naco.2019033
References:
[1]

O. Axelsson and A. Kucherov, Real valued iterative methods for solving complex symmetric linear systems, Numer. Linear Algebra Appl., 7 (2000), 197-218. doi: 10.1002/1099-1506(200005)7:4<197::AID-NLA194>3.0.CO;2-S. Google Scholar

[2]

Z.-Z. BaiM. Benzi and F. Chen, Modified HSS iteration methods for a class of complex symmetric linear systems, Computing, 87 (2010), 93-111. doi: 10.1007/s00607-010-0077-0. Google Scholar

[3]

Z.-Z. BaiM. Benzi and F. Chen, On preconditioned MHSS iteration methods for complex symmetric linear systems, Numer. Algor., 56 (2011), 297-317. doi: 10.1007/s11075-010-9441-6. Google Scholar

[4]

Z.-Z. Bai, Rotated block triangular preconditioning based on PMHSS, Sci. China Math., 56 (2013), 2523-2538. doi: 10.1007/s11425-013-4695-9. Google Scholar

[5]

Z.-Z. BaiG. H. Golub and M. K. Ng, Hermitian and skew-Hermitian splitting methods for non-Hermitian positive definite linear systems, SIAM. J. Matrix Anal. Appl., 24 (2003), 603-626. doi: 10.1137/S0895479801395458. Google Scholar

[6]

Z.-Z. BaiM. BenziF. Chen and Z.-Q. Wang, Preconditioned MHSS iteration methods for a class of block two-by-two linear systems with applications to distributed control problems, IMA J. Numer. Anal., 33 (2013), 343-369. doi: 10.1093/imanum/drs001. Google Scholar

[7]

Z.-Z. BaiB. N. Parlett and Z.-Q. Wang, On generalized successive overrelaxation methods for augmented linear systems, Numer. Math., 102 (2005), 1-38. doi: 10.1007/s00211-005-0643-0. Google Scholar

[8]

M. Benzi and D. Bertaccini, Block preconditioning of real-valued iterative algorithms for complex linear systems, IMA J. Numer. Anal., 28 (2008), 598-618. doi: 10.1093/imanum/drm039. Google Scholar

[9]

M. Benzi and G. Golub, A preconditioner for generalized saddle point problems, SIAM J. Matrix Anal. Appl., 26 (2004), 20-41. doi: 10.1137/S0895479802417106. Google Scholar

[10]

M. BenziG. H. Golub and J. Liesen, Numerical solution of saddle point problems, Acta Numerica, 14 (2005), 1-137. doi: 10.1017/S0962492904000212. Google Scholar

[11]

A. Bunse-Gerstner and R. Stover, On a conjugate gradient-type method for solving complex symmetric linear systems, Linear Algebra Appl., 287 (1999), 105-123. doi: 10.1016/S0024-3795(98)10091-5. Google Scholar

[12]

M. DehghanM. M. Dehghani and M. Hajarian, A generalized preconditioned MHSS method for a class of complex symmetric linear systems, J. Math. Model. Anal., 18 (2013), 561-576. doi: 10.3846/13926292.2013.839964. Google Scholar

[13]

V. EdalatpourD. Hezari and D. K. Salkuyeh, Two efficient inexact algorithms for a class of large sparse complex linear systems, Mediterr. J. Math., 13 (2016), 2301-2318. doi: 10.1007/s00009-015-0621-4. Google Scholar

[14]

G. H. Golub and C. F. Van Loan, Matrix Computations, 3rd ed., The Johns Hopkins University Press, Baltimore, MD, 1996. Google Scholar

[15]

D. HezariD. K. Salkuyeh and V. Edalatpour, A new iterative method for solving a class of complex symmetric system of linear equathions, Numer. Algor., 73 (2016), 927-955. doi: 10.1007/s11075-016-0123-x. Google Scholar

[16]

D. HezariV. Edalatpour and D. K. Salkuyeh, Preconditioned GSOR iterative method for a class of complex symmetric system of linear equations, Numer. Linear Algebera Appl., 22 (2015), 761-776. doi: 10.1002/nla.1987. Google Scholar

[17]

Z.-Z. Liang and G.-F. Zhang, On SSOR iteration method for a class of block two-by-wo linear systems, Numer. Algor., 71 (2016), 655-671. doi: 10.1007/s11075-015-0015-5. Google Scholar

[18]

D. K. SalkuyehD. Hezari and V. Edalatpour, Generalized successive overrelaxation iterative method for a class of complex symmetric linear system of equations, Int. J. Comput. Math., 92 (2015), 802-815. doi: 10.1080/00207160.2014.912753. Google Scholar

[19]

D. K. Salkuyeh and T. S. Siahkolaei, Two-parameter TSCSP method for solving complex symmetric system of linear equations, Calcolo, 55 (2018), 8. doi: 10.1007/s10092-018-0252-9. Google Scholar

[20]

G.-F. Zhang and Z. Zheng, A parameterized splitting iteration methods for complex symmetric linear systems, Jpn. J. Indust. Appl. Math., 31 (2014), 265-278. doi: 10.1007/s13160-014-0140-x. Google Scholar

show all references

References:
[1]

O. Axelsson and A. Kucherov, Real valued iterative methods for solving complex symmetric linear systems, Numer. Linear Algebra Appl., 7 (2000), 197-218. doi: 10.1002/1099-1506(200005)7:4<197::AID-NLA194>3.0.CO;2-S. Google Scholar

[2]

Z.-Z. BaiM. Benzi and F. Chen, Modified HSS iteration methods for a class of complex symmetric linear systems, Computing, 87 (2010), 93-111. doi: 10.1007/s00607-010-0077-0. Google Scholar

[3]

Z.-Z. BaiM. Benzi and F. Chen, On preconditioned MHSS iteration methods for complex symmetric linear systems, Numer. Algor., 56 (2011), 297-317. doi: 10.1007/s11075-010-9441-6. Google Scholar

[4]

Z.-Z. Bai, Rotated block triangular preconditioning based on PMHSS, Sci. China Math., 56 (2013), 2523-2538. doi: 10.1007/s11425-013-4695-9. Google Scholar

[5]

Z.-Z. BaiG. H. Golub and M. K. Ng, Hermitian and skew-Hermitian splitting methods for non-Hermitian positive definite linear systems, SIAM. J. Matrix Anal. Appl., 24 (2003), 603-626. doi: 10.1137/S0895479801395458. Google Scholar

[6]

Z.-Z. BaiM. BenziF. Chen and Z.-Q. Wang, Preconditioned MHSS iteration methods for a class of block two-by-two linear systems with applications to distributed control problems, IMA J. Numer. Anal., 33 (2013), 343-369. doi: 10.1093/imanum/drs001. Google Scholar

[7]

Z.-Z. BaiB. N. Parlett and Z.-Q. Wang, On generalized successive overrelaxation methods for augmented linear systems, Numer. Math., 102 (2005), 1-38. doi: 10.1007/s00211-005-0643-0. Google Scholar

[8]

M. Benzi and D. Bertaccini, Block preconditioning of real-valued iterative algorithms for complex linear systems, IMA J. Numer. Anal., 28 (2008), 598-618. doi: 10.1093/imanum/drm039. Google Scholar

[9]

M. Benzi and G. Golub, A preconditioner for generalized saddle point problems, SIAM J. Matrix Anal. Appl., 26 (2004), 20-41. doi: 10.1137/S0895479802417106. Google Scholar

[10]

M. BenziG. H. Golub and J. Liesen, Numerical solution of saddle point problems, Acta Numerica, 14 (2005), 1-137. doi: 10.1017/S0962492904000212. Google Scholar

[11]

A. Bunse-Gerstner and R. Stover, On a conjugate gradient-type method for solving complex symmetric linear systems, Linear Algebra Appl., 287 (1999), 105-123. doi: 10.1016/S0024-3795(98)10091-5. Google Scholar

[12]

M. DehghanM. M. Dehghani and M. Hajarian, A generalized preconditioned MHSS method for a class of complex symmetric linear systems, J. Math. Model. Anal., 18 (2013), 561-576. doi: 10.3846/13926292.2013.839964. Google Scholar

[13]

V. EdalatpourD. Hezari and D. K. Salkuyeh, Two efficient inexact algorithms for a class of large sparse complex linear systems, Mediterr. J. Math., 13 (2016), 2301-2318. doi: 10.1007/s00009-015-0621-4. Google Scholar

[14]

G. H. Golub and C. F. Van Loan, Matrix Computations, 3rd ed., The Johns Hopkins University Press, Baltimore, MD, 1996. Google Scholar

[15]

D. HezariD. K. Salkuyeh and V. Edalatpour, A new iterative method for solving a class of complex symmetric system of linear equathions, Numer. Algor., 73 (2016), 927-955. doi: 10.1007/s11075-016-0123-x. Google Scholar

[16]

D. HezariV. Edalatpour and D. K. Salkuyeh, Preconditioned GSOR iterative method for a class of complex symmetric system of linear equations, Numer. Linear Algebera Appl., 22 (2015), 761-776. doi: 10.1002/nla.1987. Google Scholar

[17]

Z.-Z. Liang and G.-F. Zhang, On SSOR iteration method for a class of block two-by-wo linear systems, Numer. Algor., 71 (2016), 655-671. doi: 10.1007/s11075-015-0015-5. Google Scholar

[18]

D. K. SalkuyehD. Hezari and V. Edalatpour, Generalized successive overrelaxation iterative method for a class of complex symmetric linear system of equations, Int. J. Comput. Math., 92 (2015), 802-815. doi: 10.1080/00207160.2014.912753. Google Scholar

[19]

D. K. Salkuyeh and T. S. Siahkolaei, Two-parameter TSCSP method for solving complex symmetric system of linear equations, Calcolo, 55 (2018), 8. doi: 10.1007/s10092-018-0252-9. Google Scholar

[20]

G.-F. Zhang and Z. Zheng, A parameterized splitting iteration methods for complex symmetric linear systems, Jpn. J. Indust. Appl. Math., 31 (2014), 265-278. doi: 10.1007/s13160-014-0140-x. Google Scholar

Table 1.  The optimal parameters for MHSS, GSOR, SSOR, ASSOR and PSSOR
$ m $
Method $ 16 $ $ 32 $ $ 64 $ $ 128 $ $ 256 $ $ 512 $
Example 1 PMHSS $ \alpha_{opt} $ 1.09 1.36 1.35 1.05 1.05 1.05
GSOR $ \alpha_{opt} $ 0.551 0.495 0.457 0.432 0.418 0.412
SSOR $ \omega_{opt} $ 0.33 0.29 0.26 0.24 0.24 0.23
ASSOR $ \omega_{opt} $ 0.80 0.77 0.75 0.74 0.72 0.72
PSSOR $ \alpha_{opt} $ 0.47 0.48 0.54 0.54 0.55 0.55
$ \omega_{opt} $ 0.83 0.83 0.82 0.82 0.82 0.82
Example 2 PMHSS $ \alpha _{opt} $ 1.43 1.53 1.38 1.26 1.24 1.24
GSOR $ \alpha_{opt} $ 0.189 0.190 0.190 0.190 0.190 0.190
SSOR $ \omega_{opt} $ 0.09 0.09 0.10 0.10 0.10 0.10
ASSOR $ \omega_{opt} $ 0.64 0.64 0.64 0.64 0.64 0.64
PSSOR $ \alpha_{opt} $ 0.08 0.09 0.09 0.09 0.09 0.09
$ \omega_{opt} $ 0.89 0.89 0.89 0.89 0.89 0.89
Example 3 PMHSS $ \alpha_{opt} $ 0.61 0.42 0.57 0.78 0.73 0.73
GSOR $ \alpha_{opt} $ 0.908 0.776 0.566 0.354 0.199 0.105
SSOR $ \omega_{opt} $ 0.69 0.52 0.34 0.19 0.10 0.05
ASSOR $ \omega_{opt} $ 0.62 0.62 0.62 0.61 0.61 0.61
PSSOR $ \alpha_{opt} $ 1.93 1.50 1.31 1.02 0.90 0.90
$ \omega_{opt} $ 0.82 0.74 0.68 0.62 0.61 0.61
$ m $
Method $ 16 $ $ 32 $ $ 64 $ $ 128 $ $ 256 $ $ 512 $
Example 1 PMHSS $ \alpha_{opt} $ 1.09 1.36 1.35 1.05 1.05 1.05
GSOR $ \alpha_{opt} $ 0.551 0.495 0.457 0.432 0.418 0.412
SSOR $ \omega_{opt} $ 0.33 0.29 0.26 0.24 0.24 0.23
ASSOR $ \omega_{opt} $ 0.80 0.77 0.75 0.74 0.72 0.72
PSSOR $ \alpha_{opt} $ 0.47 0.48 0.54 0.54 0.55 0.55
$ \omega_{opt} $ 0.83 0.83 0.82 0.82 0.82 0.82
Example 2 PMHSS $ \alpha _{opt} $ 1.43 1.53 1.38 1.26 1.24 1.24
GSOR $ \alpha_{opt} $ 0.189 0.190 0.190 0.190 0.190 0.190
SSOR $ \omega_{opt} $ 0.09 0.09 0.10 0.10 0.10 0.10
ASSOR $ \omega_{opt} $ 0.64 0.64 0.64 0.64 0.64 0.64
PSSOR $ \alpha_{opt} $ 0.08 0.09 0.09 0.09 0.09 0.09
$ \omega_{opt} $ 0.89 0.89 0.89 0.89 0.89 0.89
Example 3 PMHSS $ \alpha_{opt} $ 0.61 0.42 0.57 0.78 0.73 0.73
GSOR $ \alpha_{opt} $ 0.908 0.776 0.566 0.354 0.199 0.105
SSOR $ \omega_{opt} $ 0.69 0.52 0.34 0.19 0.10 0.05
ASSOR $ \omega_{opt} $ 0.62 0.62 0.62 0.61 0.61 0.61
PSSOR $ \alpha_{opt} $ 1.93 1.50 1.31 1.02 0.90 0.90
$ \omega_{opt} $ 0.82 0.74 0.68 0.62 0.61 0.61
Table 2.  Numerical results for Example 1
Method $ m=16 $ $ m=32 $ $ m=64 $ $ m=128 $ $ m=256 $ $ m=512 $
PMHSS IT 21 21 21 21 21 20
CPU 0.02 0.03 0.08 0.36 1.94 1.48
GSOR IT 20 22 24 26 27 27
CPU 0.02 0.02 0.06 0.39 2.05 11.27
SSOR IT 19 21 23 26 26 27
CPU 0.02 0.03 0.09 0.55 3.02 16.89
ASSOR IT 5 5 6 6 6 6
CPU 0.01 0.02 0.09 0.16 0.91 4.82
PSSOR IT 4 4 4 4 4 4
CPU 0.01 0.02 0.03 0.13 0.63 3.31
Method $ m=16 $ $ m=32 $ $ m=64 $ $ m=128 $ $ m=256 $ $ m=512 $
PMHSS IT 21 21 21 21 21 20
CPU 0.02 0.03 0.08 0.36 1.94 1.48
GSOR IT 20 22 24 26 27 27
CPU 0.02 0.02 0.06 0.39 2.05 11.27
SSOR IT 19 21 23 26 26 27
CPU 0.02 0.03 0.09 0.55 3.02 16.89
ASSOR IT 5 5 6 6 6 6
CPU 0.01 0.02 0.09 0.16 0.91 4.82
PSSOR IT 4 4 4 4 4 4
CPU 0.01 0.02 0.03 0.13 0.63 3.31
Table 3.  Numerical results for Example 2
Method $ m=16 $ $ m=32 $ $ m=64 $ $ m=128 $ $ m=256 $ $ m=512 $
PMHSS IT 34 37 38 38 38 38
CPU 0.02 0.04 0.09 0.60 3.21 26.73
GSOR IT 80 76 72 69 68 68
CPU 0.03 0.04 0.16 1.04 4.85 27.01
SSOR IT 74 74 66 66 66 66
CPU 0.03 0.06 0.19 1.33 7.61 41.39
ASSOR IT 7 7 7 7 7 7
CPU 0.01 0.01 0.03 0.18 0.96 5.09
PSSOR IT 3 3 3 3 3 3
CPU 0.02 0.02 0.03 0.11 0.51 2.64
Method $ m=16 $ $ m=32 $ $ m=64 $ $ m=128 $ $ m=256 $ $ m=512 $
PMHSS IT 34 37 38 38 38 38
CPU 0.02 0.04 0.09 0.60 3.21 26.73
GSOR IT 80 76 72 69 68 68
CPU 0.03 0.04 0.16 1.04 4.85 27.01
SSOR IT 74 74 66 66 66 66
CPU 0.03 0.06 0.19 1.33 7.61 41.39
ASSOR IT 7 7 7 7 7 7
CPU 0.01 0.01 0.03 0.18 0.96 5.09
PSSOR IT 3 3 3 3 3 3
CPU 0.02 0.02 0.03 0.11 0.51 2.64
Table 4.  Numerical results for Example 3
Method $ m=16 $ $ m=32 $ $ m=64 $ $ m=128 $ $ m=256 $ $ m=512 $
PMHSS IT 30 30 30 30 30 32
CPU 0.02 0.04 0.16 1.09 6.33 34.32
GSOR IT 7 11 20 44 71 131
CPU 0.02 0.02 0.08 0.97 8.19 90.83
SSOR IT 6 10 17 33 66 135
CPU 0.02 0.02 0.10 1.12 11.86 140.55
ASSOR IT 8 8 8 8 8 8
CPU 0.01 0.01 0.05 0.30 1.62 9.34
PSSOR IT 4 5 6 7 7 7
CPU 0.02 0.02 0.05 0.27 1.48 8.46
Method $ m=16 $ $ m=32 $ $ m=64 $ $ m=128 $ $ m=256 $ $ m=512 $
PMHSS IT 30 30 30 30 30 32
CPU 0.02 0.04 0.16 1.09 6.33 34.32
GSOR IT 7 11 20 44 71 131
CPU 0.02 0.02 0.08 0.97 8.19 90.83
SSOR IT 6 10 17 33 66 135
CPU 0.02 0.02 0.10 1.12 11.86 140.55
ASSOR IT 8 8 8 8 8 8
CPU 0.01 0.01 0.05 0.30 1.62 9.34
PSSOR IT 4 5 6 7 7 7
CPU 0.02 0.02 0.05 0.27 1.48 8.46
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