Skip to main content
Log in

Analysis of the convergence of the minimal and the orthogonal residual methods

  • Published:
Numerical Algorithms Aims and scope Submit manuscript

Abstract

We consider two Krylov subspace methods for solving linear systems, which are the minimal residual method and the orthogonal residual method. These two methods are studied without referring to any particular implementations. By using the Petrov–Galerkin condition, we describe the residual norms of these two methods in terms of Krylov vectors, and the relationship between there two norms. We define the Ritz singular values, and prove that the convergence of these two methods is governed by the convergence of the Ritz singular values.

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. P.N. Brown, A theoretical comparison of the Arnoldi and the GMRES algorithms, SIAM J. Sci. Statist. Comput. 12 (1991) 58–78.

    Article  Google Scholar 

  2. J. Cullum and A. Greenbaum, Relations between Galerkin and norm-minimizing iterative methods for solving linear systems, SIAM J. Matrix Anal. Appl. 17 (1996) 223–248.

    Article  Google Scholar 

  3. S.C. Eisenstat, H.C. Elman and M.H. Schultz, Variational iterative methods for nonsymmetric systems of linear equations, SIAM J. Numer. Anal. 20 (1983) 345–357.

    Article  Google Scholar 

  4. H.C. Elman, Iterative methods for large sparse nonsymmetric systems of linear equations, Ph.D. thesis, Computer Science Department, Yale University, New Haven, CT (1982).

  5. F.R. Gantmacher, The Theory of Matrices, Vol. 1 (Chelsea, New York, 1959).

    Google Scholar 

  6. A. Greenbaum and L.N. Trefethen, GMRES/CR and ARNOLDI/LANCZOS as matrix approximation problems, SIAM J. Sci. Statist. Comput. 15 (1994) 357–368.

    Google Scholar 

  7. M.R. Hestenes and E. Stiefel, Methods of conjugate gradients for solving linear systems, J. Res. Nat. Bur. Standards 49 (1952) 409–436.

    Google Scholar 

  8. G. Golub and C.F. van Loan, Matrix Computations, 2nd ed. (Johns Hopkins Univ. Press, Baltimore, MD, 1989).

    Google Scholar 

  9. Y. Huang and H.A. van der Vorst, Some observations on the convergence behaviour of GMRES, Delft University of Technology, Report 89–09 (1989).

  10. T. Huckle, The Arnoldi method for normal matrices, SIAM J. Matrix Anal. Appl. 15 (1994) 479–489.

    Article  Google Scholar 

  11. K. Jbilou and H. Sadok, Analysis of some vector extrapolation methods for solving systems of linear equations, Numer. Math. 70 (1995) 73–89.

    Article  Google Scholar 

  12. N.M. Nachtigal, S.C. Reddy and L.N. Trefethen, How fast are nonsymmetric matrix iterations, SIAM J. Matrix Anal. Appl. 13 (1992) 778–795.

    Article  Google Scholar 

  13. C.C. Paige, B.N. Parlett and H.A. van der Vorst, Approximate solutions and eigenvalue bounds from Krylov subspaces, Numer. Linear Algebra Appl. 2 (1995) 115–134.

    Article  Google Scholar 

  14. C.C. Paige and M.A. Saunders, Solution of sparse indefinite systems of linear equations, SIAM J. Numer. Anal. 12 (1975) 617–629.

    Article  Google Scholar 

  15. Y. Saad, Variations on Arnoldi's method for computing eigenelements of large unsymmetric, Linear Algebra Appl. 34 (1980) 269–295.

    Article  Google Scholar 

  16. Y. Saad, Krylov subspace methods for solving large unsymmetric linear systems, Math. Comp. 37 (1981) 105–126.

    Google Scholar 

  17. Y. Saad and M.H. Schultz, Conjugate gradient-like algorithms for solving nonsymmetric linear systems, Math. Comp. 44 (1985) 417–424.

    Google Scholar 

  18. Y. Saad and M.H. Schultz, GMRES: a generalized minimal residual algorithm for solving non-symmetric linear systems, SIAM J. Sci. Statist. Comput. 7 (1986) 856–869.

    Article  Google Scholar 

  19. G.W. Stewart and J.G. Sun, Matrix Perturbation Theory (Academic Press, New York, 1990).

    Google Scholar 

  20. R.C. Thompson, Principal submatrices IX: interlacing inequalities for singular values of submatrices, Linear Algebra Appl. 5 (1972) 1–12.

    Article  Google Scholar 

  21. A. van der Sluis and H.A. van der Vorst, The rate of convergence of conjugate gradients, Numer. Math. 48 (1986) 543–560.

    Article  Google Scholar 

  22. H.A. van der Vorst and C. Vuik, The rate of convergence of the GMRES method, J. Comput. Appl. Math. 48 (1993) 327–341.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to H. Sadok.

Additional information

Communicated by C. Brezinski

AMS subject classification

65F10

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sadok, H. Analysis of the convergence of the minimal and the orthogonal residual methods. Numer Algor 40, 201–216 (2005). https://doi.org/10.1007/s11075-005-1533-3

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11075-005-1533-3

Keywords

Navigation