Skip to main content
Log in

L-Curve and Curvature Bounds for Tikhonov Regularization

  • Published:
Numerical Algorithms Aims and scope Submit manuscript

Abstract

The L-curve is a popular aid for determining a suitable value of the regularization parameter when solving linear discrete ill-posed problems by Tikhonov regularization. However, the computational effort required to determine the L-curve and its curvature can be prohibitive for large-scale problems. Recently, inexpensively computable approximations of the L-curve and its curvature, referred to as the L-ribbon and the curvature-ribbon, respectively, were proposed for the case when the regularization operator is the identity matrix. This note discusses the computation and performance of the L- and curvature-ribbons when the regularization operator is an invertible 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.

Similar content being viewed by others

References

  1. M.L. Baart, The use of auto-correlation for pseudo-rank determination in noisy ill-conditioned leastsquares problems, IMA J. Numer. Anal. 2 (1982) 241–247.

    Google Scholar 

  2. Å. Björck and L. Eldén, Methods in numerical linear algebra for ill-posed problems, Report, Department of Mathematics, Linköping University, Linköping, Sweden (1979).

    Google Scholar 

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

    Google Scholar 

  4. D. Calvetti, P.C. Hansen and L. Reichel, L-curve curvature bounds via Lanczos bidiagonalization, Elec. Trans. Numer. Anal. 14 (2002) 20–35. Available at the Web site http://etna.mcs.kent.edu.

    Google Scholar 

  5. D. Calvetti, S. Morigi, L. Reichel and F. Sgallari, Tikhonov regularization and the L-curve for large, discrete ill-posed problems, J. Comput. Appl. Math. 123 (2000) 423–446.

    Google Scholar 

  6. D. Calvetti, S. Morigi, L. Reichel and F. Sgallari, An L-ribbon for large underdetermined linear discrete ill-posed problems, Numer. Algorithms 25 (2000) 89–107.

    Google Scholar 

  7. L.M. Delves and J.L. Mohamed, Computational Methods for Integral Equations (Cambridge Univ. Press, Cambridge, 1985).

    Google Scholar 

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

    Google Scholar 

  9. P.C. Hansen, Regularization tools: A Matlab package for analysis and solution of discrete illposed problems, Numer. Algorithms 6 (1994) 1–35. Software is available in Netlib at the Web site http://www.netlib.org.

    Google Scholar 

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

    Google Scholar 

  11. P.C. Hansen, The L-curve and its use in the numerical treatment of inverse problems, in: Computational Inverse Problems in Electrocardiology, ed. P. Johnston, Advances in Computational Bioengineering, Vol. 4 (WIT Press, Southampton, 2000) pp. 119–142.

    Google Scholar 

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

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Calvetti, D., Reichel, L. & Shuibi, A. L-Curve and Curvature Bounds for Tikhonov Regularization. Numerical Algorithms 35, 301–314 (2004). https://doi.org/10.1023/B:NUMA.0000021764.16526.47

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:NUMA.0000021764.16526.47

Navigation