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Restarted Full Orthogonalization Method with Deflation for Shifted Linear Systems

Published online by Cambridge University Press:  28 May 2015

Jun-Feng Yin*
Affiliation:
Department of Mathematics, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
Guo-Jian Yin*
Affiliation:
Department of Mathematics, Tongji University, 1239 Siping Road, Shanghai 200092, P. R. China
*
Corresponding author.Email address:yinjf@tongji.edu.cn
Corresponding author.Email address:gjyin@math.cuhk.edu.hk
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Abstract

In this paper, we study shifted restated full orthogonalization method with deflation for simultaneously solving a number of shifted systems of linear equations. Theoretical analysis shows that with the deflation technique, the new residual of shifted restarted FOM is still collinear with each other. Hence, the new approach can solve the shifted systems simultaneously based on the same Krylov subspace. Numerical experiments show that the deflation technique can significantly improve the convergence performance of shifted restarted FOM.

Type
Research Article
Copyright
Copyright © Global Science Press Limited 2014

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References

[1]Arnoldi, W. E., The principle of minimized iteration in the solution of the matrix eigenvalue problem, Quart. Appl. Math., 9(1951), pp. 17–29.CrossRefGoogle Scholar
[2]Duff, I. S., Grimes, R. G. and Lewis, J. G., Sparse matrix test problem, ACM Trans. Math. Software, 15(1989), pp. 1–14.CrossRefGoogle Scholar
[3]Saad, Y., Numerical methods for large eigenvalue problems, Halsted Press, New York, 1992.Google Scholar
[4]Chapman, A. and Saad, Y., Defalted and augmented Krylov subspace techniques, Numer. Linear Algebra. Appl., 4(1997), pp. 43–66.3.0.CO;2-Z>CrossRefGoogle Scholar
[5]Simoncini, V., Restarted full orthogonalization method for shifted linear systems, BIT Numer. Math, 43(2003), pp. 459–466.CrossRefGoogle Scholar
[6]Simoncini, V., On the convergence of restarted Krylov subspace methods, SIAM J. Matrix Anal. Appl., 22(2000), pp. 430–452.CrossRefGoogle Scholar
[7]Simoncini, V. and Perotti, F., On the numerical solution of$$ λ2A + λB + C = b and application to structural dynamics, SIAM J. Sci. Comput., 23(2002), pp. 1876–1898.CrossRefGoogle Scholar
[8]Frommer, A. and Glässner, U., Restarted GMRES for shifted linear systems, SIAM J. Sci. Comput., 19(1998), pp. 15–26.CrossRefGoogle Scholar
[9]Morgan, R. B., GMRES with deflated restarting, SIAM J. Sci. Comput., 24(2002), pp. 20–37.CrossRefGoogle Scholar
[10]Morgan, R. B., A restarted GMRES method augmented with eigenvectors, SIAM J. Matrix Anal. Appl., 16(1995), pp. 1154–1171.CrossRefGoogle Scholar
[11]Morgan, R. B., Implicitly restarted GMRES and Arnoldi methods for nonsymmetric systems of equations, SIAM J. Matrix Anal. Appl., 21(2000), pp. 1112–1135.CrossRefGoogle Scholar
[12]Meerbergen, K., The solution of parametrized symmetric linear systems, SIAM J. Matrix Anal. Appl., 24(2003), pp. 1038–1059.CrossRefGoogle Scholar
[13]Jing, Yan-Fei and Huang, Ting-Zhu, Restarted weighted full orthogonalization method for shifted linear systems, Comput. Math. Appl., 57(2009), pp. 1583–1591.CrossRefGoogle Scholar
[14]Wu, K. and Simon, H., Thick-resart Lanczos method for symmetric eigenvalue problems, SIAM J. Matrix Anal. Appl., 22(2000), pp. 602–616.CrossRefGoogle Scholar