Deflation by restriction for the inverse-free preconditioned Krylov subspace method

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2016, 6(1): 55-71. doi: 10.3934/naco.2016.6.55

Deflation by restriction for the inverse-free preconditioned Krylov subspace method

1.	Department of Mathematics, University of Kentucky, Lexington, KY 40506-0027, United States, United States

Received June 2015 Revised December 2015 Published January 2016

Abstract
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A deflation by restriction scheme is developed for the inverse-free preconditioned Krylov subspace method for computing a few extreme eigenvalues of the definite symmetric generalized eigenvalue problem $Ax = \lambda Bx$. The convergence theory for the inverse-free preconditioned Krylov subspace method is generalized to include this deflation scheme and numerical examples are presented to demonstrate the convergence properties of the algorithm with the deflation scheme.

Keywords: Generalized eigenvalue problem, Krylov subspace, deflation..

Mathematics Subject Classification: Primary: 65F1.

Citation: Qiao Liang, Qiang Ye. Deflation by restriction for the inverse-free preconditioned Krylov subspace method. Numerical Algebra, Control & Optimization, 2016, 6 (1) : 55-71. doi: 10.3934/naco.2016.6.55

References:

[1]	Z. Bai, J. Demmel, J. Dongarra, A. Ruhe and H. van der Vorst, Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide,, SIAM, (2000). doi: 10.1137/1.9780898719581. Google Scholar
[2]	L. Bergamaschi, G. Pini and F. Sartoretto, Approximate inverse preconditioning in the parallel solution of sparse eigenproblems,, Numer. Linear Alg. Appl., 7 (2000), 99. doi: 10.1002/(SICI)1099-1506(200004/05)7:3<99::AID-NLA188>3.3.CO;2-X. Google Scholar
[3]	J. Bramble, J. Pasciak and A. Knyazev, A subspace preconditioning algorithm for eigenvector/eigenvalue computation,, Adv. Comp. Math., 6 (1996), 150. doi: 10.1007/BF02127702. Google Scholar
[4]	D. Fokkema, G. Sleijpen and H. Van der Vorst, Jacobi-Davidson style QR and QZ algorithms for the reduction of matrix pencils,, SIAM J. Sci. Comput, 20 (1999), 94. doi: 10.1137/S1064827596300073. Google Scholar
[5]	G. H. Golub and Q. Ye, An inverse free preconditioned Krylov subspace method for symmetric generalized eigenvalue problems,, SIAM Journal on Scientific Computing, 24 (2002), 312. doi: 10.1137/S1064827500382579. Google Scholar
[6]	A. V. Knyazev, Preconditioned eigensolvers-an oxymoron,, Electronic Transactions on Numerical Analysis, 7 (1998), 104. Google Scholar
[7]	A. V. Knyazev, Toward the optimal preconditioned eigensolver: Locally optimal block preconditioned conjugate gradient,, SIAM Journal on Scientific Computing, 23 (2001), 517. doi: 10.1137/S1064827500366124. Google Scholar
[8]	Q. Liang and Q. Ye, Computing singular values of large matrices with an inverse-free preconditioned krylov subspace method,, Electronic Transactions on Numerical Analysis, 42 (2014), 197. Google Scholar
[9]	R. B. Lehoucq, Analysis And Implementation of An Implicitly Restarted Arnoldi Iteration,, Ph.D Thesis, (1995). Google Scholar
[10]	R. B. Lehoucq and D. C. Sorensen, Deflation techniques within an implicitly restarted Arnoldi iteration,, SIAM Journal on Matrix Analysis and Applications, 17 (1996), 789. doi: 10.1137/S0895479895281484. Google Scholar
[11]	R. B. Lehoucq, D. C. Sorensen, and C. Yang, ARPACK Users' Guides, Solution of Large Scale Eigenvalue Problems with Implicitly Restarted Arnoldi Method,, SIAM, (1998). doi: 10.1137/1.9780898719628. Google Scholar
[12]	R. Lehoucq and K. Meerbergen, The inexact rational Krylov sequence method,, SIAM J. Matrix Anal. Appl., 20 (1998), 131. doi: 10.1137/S0895479896311220. Google Scholar
[13]	K. Meerbergen and D. Roose, The restarted Arnoldi method applied to iterative linear solvers for the computation of rightmost eigenvalues,, SIAM J. Matrix Anal. Appl., 20 (1999), 1. doi: 10.1137/S0895479894274255. Google Scholar
[14]	J. H. Money and Q. Ye, Algorithm 845: EIGIFP: A MATLAB program for solving large symmetric generalized eigenvalue problems,, ACM Transactions on Mathematical Software, 31 (2005), 270. doi: 10.1145/1067967.1067973. Google Scholar
[15]	R. Morgan and D. Scott, Preconditioning the Lanczos algorithm for sparse symmetric eigenvalue problems,, SIAM J. Sci. Stat. Comput., 14 (1993), 585. doi: 10.1137/0914037. Google Scholar
[16]	Y. Notay, Combination of Jacobi-Davidson and conjugate gradients for the partial symmetric eigenproblem,, Numerical Linear Algebra and its Applications, 9 (2002), 21. doi: 10.1002/nla.246. Google Scholar
[17]	B. N. Parlett, The Symmetric Eigenvalue Problem,, Classics in Applied Mathematics, (1998). doi: 10.1137/1.9781611971163. Google Scholar
[18]	P. Quillen and Q. Ye, A block inverse-free preconditioned Krylov subspace method for symmetric generalized eigenvalue problems,, J. Comp. Appl. Math, 233 (2010), 1298. doi: 10.1016/j.cam.2008.10.071. Google Scholar
[19]	Y. Saad, Numerical methods for large eigenvalue problems,, Revised Edition, (2011). doi: 10.1137/1.9781611970739.ch1. Google Scholar
[20]	G. Sleijpen and H. van der Vorst, A Jacobi-Davidson iteration method for linear eigenvalue problems,, SIAM Journal on Matrix Analysis and Applications, 17 (1996), 401. doi: 10.1137/S0895479894270427. Google Scholar
[21]	A. Stathopoulos and J. R. McCombs, PRIMME: PReconditioned Iterative MultiMethod Eigensolver: Methods and software description,, ACM Transaction on Mathematical Software, 37 (2010), 1. Google Scholar
[22]	A. Stathopoulos, Y. Saad and C. Fisher, Robust preconditioning of large sparse symmetric eigenvalue problems,, J. Comp. Appl. Math., 64 (1995), 197. doi: 10.1016/0377-0427(95)00141-7. Google Scholar
[23]	H. van der Vorst, G. Sleijpen and M. van Gijzen, Efficient expansion of subspaces in the Jacobi-Davison method for standard and generalized eigenproblems,, Electronic Transactions on Numerical Analysis, 7 (1998), 75. Google Scholar
[24]	E. Vecharynski, C. Yang and F. Xue, Generalized preconditioned locally harmonic residual method for non-Hermitian eigenproblems,, Preprint., (). Google Scholar
[25]	J. H. Wilkinson, The Algebraic Eigenvalue Problem,, Oxford University Press, (1965). Google Scholar
[26]	C. Yang, Convergence analysis of an inexact truncated RQ iterations,, Elec. Trans. Numer. Anal., 7 (1998), 40. Google Scholar

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References:

[1]	Z. Bai, J. Demmel, J. Dongarra, A. Ruhe and H. van der Vorst, Templates for the Solution of Algebraic Eigenvalue Problems: A Practical Guide,, SIAM, (2000). doi: 10.1137/1.9780898719581. Google Scholar
[2]	L. Bergamaschi, G. Pini and F. Sartoretto, Approximate inverse preconditioning in the parallel solution of sparse eigenproblems,, Numer. Linear Alg. Appl., 7 (2000), 99. doi: 10.1002/(SICI)1099-1506(200004/05)7:3<99::AID-NLA188>3.3.CO;2-X. Google Scholar
[3]	J. Bramble, J. Pasciak and A. Knyazev, A subspace preconditioning algorithm for eigenvector/eigenvalue computation,, Adv. Comp. Math., 6 (1996), 150. doi: 10.1007/BF02127702. Google Scholar
[4]	D. Fokkema, G. Sleijpen and H. Van der Vorst, Jacobi-Davidson style QR and QZ algorithms for the reduction of matrix pencils,, SIAM J. Sci. Comput, 20 (1999), 94. doi: 10.1137/S1064827596300073. Google Scholar
[5]	G. H. Golub and Q. Ye, An inverse free preconditioned Krylov subspace method for symmetric generalized eigenvalue problems,, SIAM Journal on Scientific Computing, 24 (2002), 312. doi: 10.1137/S1064827500382579. Google Scholar
[6]	A. V. Knyazev, Preconditioned eigensolvers-an oxymoron,, Electronic Transactions on Numerical Analysis, 7 (1998), 104. Google Scholar
[7]	A. V. Knyazev, Toward the optimal preconditioned eigensolver: Locally optimal block preconditioned conjugate gradient,, SIAM Journal on Scientific Computing, 23 (2001), 517. doi: 10.1137/S1064827500366124. Google Scholar
[8]	Q. Liang and Q. Ye, Computing singular values of large matrices with an inverse-free preconditioned krylov subspace method,, Electronic Transactions on Numerical Analysis, 42 (2014), 197. Google Scholar
[9]	R. B. Lehoucq, Analysis And Implementation of An Implicitly Restarted Arnoldi Iteration,, Ph.D Thesis, (1995). Google Scholar
[10]	R. B. Lehoucq and D. C. Sorensen, Deflation techniques within an implicitly restarted Arnoldi iteration,, SIAM Journal on Matrix Analysis and Applications, 17 (1996), 789. doi: 10.1137/S0895479895281484. Google Scholar
[11]	R. B. Lehoucq, D. C. Sorensen, and C. Yang, ARPACK Users' Guides, Solution of Large Scale Eigenvalue Problems with Implicitly Restarted Arnoldi Method,, SIAM, (1998). doi: 10.1137/1.9780898719628. Google Scholar
[12]	R. Lehoucq and K. Meerbergen, The inexact rational Krylov sequence method,, SIAM J. Matrix Anal. Appl., 20 (1998), 131. doi: 10.1137/S0895479896311220. Google Scholar
[13]	K. Meerbergen and D. Roose, The restarted Arnoldi method applied to iterative linear solvers for the computation of rightmost eigenvalues,, SIAM J. Matrix Anal. Appl., 20 (1999), 1. doi: 10.1137/S0895479894274255. Google Scholar
[14]	J. H. Money and Q. Ye, Algorithm 845: EIGIFP: A MATLAB program for solving large symmetric generalized eigenvalue problems,, ACM Transactions on Mathematical Software, 31 (2005), 270. doi: 10.1145/1067967.1067973. Google Scholar
[15]	R. Morgan and D. Scott, Preconditioning the Lanczos algorithm for sparse symmetric eigenvalue problems,, SIAM J. Sci. Stat. Comput., 14 (1993), 585. doi: 10.1137/0914037. Google Scholar
[16]	Y. Notay, Combination of Jacobi-Davidson and conjugate gradients for the partial symmetric eigenproblem,, Numerical Linear Algebra and its Applications, 9 (2002), 21. doi: 10.1002/nla.246. Google Scholar
[17]	B. N. Parlett, The Symmetric Eigenvalue Problem,, Classics in Applied Mathematics, (1998). doi: 10.1137/1.9781611971163. Google Scholar
[18]	P. Quillen and Q. Ye, A block inverse-free preconditioned Krylov subspace method for symmetric generalized eigenvalue problems,, J. Comp. Appl. Math, 233 (2010), 1298. doi: 10.1016/j.cam.2008.10.071. Google Scholar
[19]	Y. Saad, Numerical methods for large eigenvalue problems,, Revised Edition, (2011). doi: 10.1137/1.9781611970739.ch1. Google Scholar
[20]	G. Sleijpen and H. van der Vorst, A Jacobi-Davidson iteration method for linear eigenvalue problems,, SIAM Journal on Matrix Analysis and Applications, 17 (1996), 401. doi: 10.1137/S0895479894270427. Google Scholar
[21]	A. Stathopoulos and J. R. McCombs, PRIMME: PReconditioned Iterative MultiMethod Eigensolver: Methods and software description,, ACM Transaction on Mathematical Software, 37 (2010), 1. Google Scholar
[22]	A. Stathopoulos, Y. Saad and C. Fisher, Robust preconditioning of large sparse symmetric eigenvalue problems,, J. Comp. Appl. Math., 64 (1995), 197. doi: 10.1016/0377-0427(95)00141-7. Google Scholar
[23]	H. van der Vorst, G. Sleijpen and M. van Gijzen, Efficient expansion of subspaces in the Jacobi-Davison method for standard and generalized eigenproblems,, Electronic Transactions on Numerical Analysis, 7 (1998), 75. Google Scholar
[24]	E. Vecharynski, C. Yang and F. Xue, Generalized preconditioned locally harmonic residual method for non-Hermitian eigenproblems,, Preprint., (). Google Scholar
[25]	J. H. Wilkinson, The Algebraic Eigenvalue Problem,, Oxford University Press, (1965). Google Scholar
[26]	C. Yang, Convergence analysis of an inexact truncated RQ iterations,, Elec. Trans. Numer. Anal., 7 (1998), 40. Google Scholar

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