Skip to main content
Log in

VPAStab: Stabilised Vector-Padé Approximation with Application to Linear Systems

  • Published:
Numerical Algorithms Aims and scope Submit manuscript

Abstract

An algorithm called VPAStab is given for the acceleration of convergence of a sequence of vectors. It combines a method of vector-Padé approximation with a successful technique for stabilisation. More generally, this algorithm is designed to find the fixed point of the generating function of the given sequence of vectors, analogously to the way in which ordinary Padé approximants can accelerate the convergence of a given scalar sequence. VPAStab is justified in the context of its application to the solution of a large sparse system of linear equations. The possible breakdowns of the algorithm are listed. Numerical experiments indicate that these breakdowns can be classified either as pivot-type (type L) or as ghost-type (type D).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. G.A. Baker Jr. and P.R. Graves-Morris, Padé Approximants (Cambridge Univ. Press, Cambridge, 1997).

    Google Scholar 

  2. C. Brezinski and M. Redivo-Zaglia, Breakdowns in the computation of orthogonal polynomials, in: Nonlinear Numerical Methods and Rational Approximation II, ed. A. Cuyt (Kluwer, Dordrecht, 1994) pp. 49–59.

    Google Scholar 

  3. C. Brezinski and M. Redivo-Zaglia, Look-ahead in BiCGStab and other product methods for linear systems, BIT 35 (1995) 169–201.

    Google Scholar 

  4. C. Brezinski and H. Sadok, Some vector sequence transformations with applications to systems of equations, Numer. Algorithms 3 (1992) 75–80.

    Google Scholar 

  5. C. Brezinski and H. Sadok, Lanczos-type algorithms for solving systems of linear equations, Appl. Numer. Math. 11 (1993) 443–473.

    Google Scholar 

  6. R. Fletcher, Conjugate gradient methods for indefinite systems, in: Numerical Analysis, Dundee, 1975, ed. G.A. Watson, Lecture Notes in Mathematics, Vol. 506 (Springer, Berlin, 1976) pp. 73–89.

    Google Scholar 

  7. R.W. Freund, M.H. Gutknecht and N. Nachtigal, An implementation of look-ahead Lanczos algorithm for non-Hermitian matrices, SIAM J. Sci. Comput. 14 (1993) 137–158.

    Google Scholar 

  8. G.H. Golub and H.A. van der Vorst, Closer to the solution: Iterative linear solvers, in: The State of the Art in Numerical Analysis, eds. I.S. Duff and G.A. Watson (Clarendon Press, Oxford, 1997) pp. 63–92.

    Google Scholar 

  9. W.B. Gragg, Matrix interpretations and applications of the continued fraction algorithm, Rocky Mountain J. Math. 4 (1974) 213–225.

    Google Scholar 

  10. P.R. Graves-Morris, Reliability of Lanczos-type product methods from perturbation theory, Reliable Comput. 6 (2000) 411–428.

    Google Scholar 

  11. P.R. Graves-Morris, The breakdowns of BiCGStab, Numer. Algorithms (2002) in press.

  12. P.R. Graves-Morris and A. Salam, Avoiding breakdown in van der Vorst's method, Numer. Algorithms 21 (1999) 205–223.

    Google Scholar 

  13. A. Greenbaum, Estimating the attainable accuracy of recursively computed residual methods, SIAM J. Matrix Anal. Appl. 18 (1997) 535–551.

    Google Scholar 

  14. M.H. Gutknecht, Variants of BiCGStab for matrices with complex spectrum, SIAM J. Sci. Comput. 14 (1993) 1020–1033.

    Google Scholar 

  15. M.H. Gutknecht, Lanczos-type solvers for non-symmetric linear systems of equations, Acta Numer. 6 (1997) 271–397.

    Google Scholar 

  16. M.H. Gutknecht and K.J. Ressel, Look-ahead procedures for Lanczos-type product methods based on three-term Lanczos recurrences, SIAM J. Matrix Anal. Appl. 21 (2000) 1051–1078.

    Google Scholar 

  17. M.H. Gutknecht and Z. Strakoš, Accuracy of two three-term and three two-term recurrences for Krylov space solvers, SIAM J. Matrix Anal. Appl. 22 (2000) 213–229.

    Google Scholar 

  18. W.D. Joubert, Lanczos methods for the solution of non-symmetric systems of linear equations, SIAM J. Matrix Anal. Appl. 13 (1992) 926–943.

    Google Scholar 

  19. C. Lanczos, Solution of systems of linear equations by minimised iterations, J. Res. Nat. Bureau Standarnds 49 (1952) 33–53.

    Google Scholar 

  20. MATLAB 6.0 (MathWorks, Natick, MA, USA).

  21. Y. Saad, Preconditioning techniques for non-symmetric and indefinite linear systems, J. Comput. Appl. Math. 24 (1988) 89–105.

    Google Scholar 

  22. G.L.G. Sleijpen and D.R. Fokkema, BiCGstab(l) for linear equations involving unsymmetric matrices with complex spectrum, Electronic Trans. Numer. Anal. 1 (1993) 11–32.

    Google Scholar 

  23. G.L.G. Sleijpen and H.A. van der Vorst, Maintaining convergence properties of BiCGStab methods in finite precision arithmetic, Numer. Algorithms 10 (1995) 203–223.

    Google Scholar 

  24. G.L.G. Sleijpen and H.A. van der Vorst, Reliable updated residuals in hybrid Bi-CG methods, Computing 56 (1996) 141–163.

    Google Scholar 

  25. H.A. van der Vorst, Bi-CGStab: A fast and smoothly convergent variant of Bi-CG for the solution of non-symmetric linear systems, SIAM J. Sci. Statist. Comput. 13 (1992) 631-644.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Graves-Morris, P. VPAStab: Stabilised Vector-Padé Approximation with Application to Linear Systems. Numerical Algorithms 33, 293–304 (2003). https://doi.org/10.1023/A:1025532525878

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1025532525878

Navigation