Skip to main content
Log in

The block Lanczos algorithm for linear ill-posed problems

  • Published:
Calcolo Aims and scope Submit manuscript

Abstract

In the present paper, we propose a new method to inexpensively determine a suitable value of the regularization parameter and an associated approximate solution, when solving ill-conditioned linear system of equations with multiple right-hand sides contaminated by errors. The proposed method is based on the symmetric block Lanczos algorithm, in connection with block Gauss quadrature rules to inexpensively approximate matrix-valued function of the form \(W^Tf(A)W\), where \(W\in {\mathbb {R}}^{n\times k}\), \(k\ll n\), and \(A\in {\mathbb {R}}^{n\times n}\) is a symmetric matrix.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ahmad, G.F., Brooks, D.H., MacLeod, R.S.: An admissible solution approach to inverse electrocardiography. Ann. Biomed. Eng. 26, 278–292 (1998)

    Article  Google Scholar 

  2. Baglama, James: Dealing with linear dependence during the iterations of the restarted block Lanczos methods. Numer. Algorithm. 25, 23–36 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  3. Brianzi, P., Di Bendetto, F., Estatico, C.: Improvement of space-invariant image deblurring by preconditioned Landweber iterations. SIAM J. Sci. Comput. 30, 1430–1458 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  4. Björck, Å.A.: A bidiagonalization algorithm for solving large and sparse ill-posed systems of linear equations. BIT 18, 659–670 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bentbib, A.H., El Guide, M., Jbilou, K., Reichel, L.: A global Lanczos method for image restoration. J. Comput. Math. 300, 233–244 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bouhamidi, A., Jbilou, K., Reichel, L., Sadok, H.: An extrapolated TSVD method for linear discrete ill-posed problems with Kronecker structure. Linear Algebra Appl. 434(7), 1677–1688 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  7. Brezinski, C., Rodriguez, G., Seatzu, S.: Error estimates for the regularization of least squares problems. Numer. Algorithm. 51, 61–76 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  8. Calvetti, D., Golub, G.H., Reichel, L.: Estimation of the L-curve via Lanczos bidiagonalization. BIT 39, 603–619 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  9. Calvetti, D., Hansen, P.C., Reichel, L.: L-curve curvature bounds via Lanczos bidiagonalization. Electron. Trans. Numer. Anal. 14, 134–149 (2002)

    MathSciNet  MATH  Google Scholar 

  10. Calvetti, D., Lewis, B., Reichel, L., Sgallari, F.: Tikhonov regularization with nonnegativity constraint. Electron. Trans. Numer. Anal. 18, 153–173 (2004)

    MathSciNet  MATH  Google Scholar 

  11. Calvetti, D., Reichel, L.: Tikhonov regularization with a solution constraint. SIAM J. Sci. Comput 26, 224–239 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  12. Calvetti, D., Reichel, L.: Tikhonov regularization of large linear problems. BIT 43(2), 263–283 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  13. Engl, H.W., Hanke, M., Neubauer, A.: Regularization of Inverse Problems. Kluwer, Dordrecht (1996)

    Book  MATH  Google Scholar 

  14. Eldn, L.: Algorithms for the regularization of ill-conditioned least squares problems. BIT 17, 134–145 (1977)

    Article  MathSciNet  Google Scholar 

  15. Fenu, C., Martin, D., Reichel, L., Rodriguez, G.: Block Gauss and anti-Gauss quadrature with application to networks. SIAM J. Matrix Anal. Appl. 34(4), 1655–1684 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  16. Faber, T.L., Raghunath, N., Tudorascu, D., Votaw, J.R.: Motion correction of PET brain images through deconvolution: I. Theoretical development and analysis in software simulations. Phys. Med. Biol. 54(3), 797–811 (2009)

    Article  Google Scholar 

  17. Golub, G.H., Luk, F.T., Overton, M.L.: A block Lanczos method for computing the singular values and corresponding singular vectors of a matrix. ACM Trans. Math. Softw. 7, 149–169 (1981)

    Article  MATH  Google Scholar 

  18. el Guennouni, A., Jbilou, K., Sadok, H.: The block Lanczos method for linear systems with multiple right-hand sides. Appl. Numer. Math. 23(51), 243–256 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  19. el Guennouni, A., Jbilon, K., Sadok, H.: A block BiCGSTAB algorithm for multiple linear systems. Electron. Trans. Numer. Anal. 16, 129–142 (2003)

    MathSciNet  MATH  Google Scholar 

  20. Golub, G.H., Meurant, G.: Matrices, moments and quadrature. In: Griffiths, D.F., Watson, G.A. (eds.) Numerical Analysis 1993, pp. 105–156. Longman, Essex (1994)

    Google Scholar 

  21. Golub, G.H., Meurant, G.: Matrices, Moments and Quadrature with Applications. Princeton University Press, Princeton (2010)

  22. Groetsch, C.W.: The Theory of Tikhonov Regularization for Fredholm Integral Equations of the First Kind. Pitman, Boston (1984)

    MATH  Google Scholar 

  23. Golub, G.H., von Matt, U.: Quadratically constrained least squares and quadratic problems. Numer. Math. 59, 561–580 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  24. Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (1996)

    MATH  Google Scholar 

  25. Hanke, M.: On Lanczos based methods for the regularization of discrete ill-posed problems. BIT 41(5), 1008–1018 (2001)

    Article  MathSciNet  Google Scholar 

  26. Hansen, P.C.: Regularization tools version 4.0 for MATLAB 7.3. Numer. Algorithm. 46, 189–194 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  27. Hansen, P.C.: Rank-Deficient and Discrete Ill-Posed Problems. SIAM, Philadelphia (1998)

    Book  Google Scholar 

  28. Hansen, P.C., Nagy, J.G., O’Leary, D.P.: Deblurring Images: Matrices, Spectra, and Filtering. SIAM, Philadelphia (2006)

    Book  MATH  Google Scholar 

  29. Jbilou, K., Sadok, H., Tinzefte, A.: Oblique projection methods for linear systems with multiple right-hand sides. Electron. Trans. Numer. Anal. 20, 119–138 (2005)

    MathSciNet  MATH  Google Scholar 

  30. Kindermann, S.: Convergence analysis of minimization-based noise level-free parameter choice rules for linear ill-posed problems. Electron. Trans. Numer. Anal. 38, 233–257 (2011)

    MathSciNet  MATH  Google Scholar 

  31. Kamm, J., Nagy, J.G.: Kronecker product and SVD approximations in image restoration. Linear Algebra Appl. 284, 177–192 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  32. Kamm, J., Nagy, J.G.: Kronecker product approximations for restoration image with reflexive boundary conditions. SIAM J. Matrix Anal. Appl. 25, 829–841 (2004)

    MATH  Google Scholar 

  33. Laurie, D.P.: Anti-Gaussian quadrature formulas. Math. Comp. 65, 739–747 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  34. McNown, S.R., Hunt, B.R.: Approximate shift-invariance by warping shift-variant systems. In: SPIE’s 1994 International Symposium on Optics, Imaging, and Instrumentation, pp. 156–167. International Society for Optics and Photonics (1994)

  35. Nagy, J.G., Ng, M.K., Perrone, L.: Kronecker product approximation for image restoration with reflexive boundary conditions. SIAM J. Matrix Anal. Appl. 25, 829–841 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  36. Paige, C.C., Saunders, M.A.: LSQR: An algorithm for sparse linear equations and sparse least squares. ACM Trans. Math. Soft. 8, 43–71 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  37. Reichel, L., Rodriguez, G.: Old and new parameter choice rules for discrete ill-posed problems. Numer. Algorithm. 63, 65–87 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  38. Rojas, M., Sorensen, D.C.: A trust-region approach to regularization of large-scale discrete forms of ill-posed problems. SIAM J. Sci. Comput. 23, 1842–1860 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  39. Rojas, M., Steihaug, T.: An interior-point trust-region-based method for large-scale non-negative regularization. Inverse Probl. 18, 1291–1307 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  40. Toutounian, F., Karimi, S.: Global least squares method (Gl-LSQR) for solving general linear systems with several right-hand sides. Appl. Math. Comput. 178, 452–460 (2006)

    MathSciNet  MATH  Google Scholar 

  41. Van Loan, C.F., Pitsianis, N.P.: Approximation with Kronecker products. In: Moonen, M.S., Golub, G.H. (eds.) Linear Algebra for Large Scale and Real Time Applications, pp. 293–314. Kluwer, Dordrecht (1993)

    Chapter  Google Scholar 

Download references

Acknowledgments

We would like to thank the referee for the pertinent and useful comments towards the improvement of our manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Jbilou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bentbib, A.H., Guide, M.E. & Jbilou, K. The block Lanczos algorithm for linear ill-posed problems. Calcolo 54, 711–732 (2017). https://doi.org/10.1007/s10092-016-0206-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10092-016-0206-z

Keywords

Mathematics Subject Classification

Navigation