Skip to main content
SpringerLink
Log in

Regularization Tools version 4.0 for Matlab 7.3

  • Original Paper
  • Published:
Numerical Algorithms Aims and scope Submit manuscript

Abstract

This communication describes version 4.0 of Regularization Tools, a Matlab package for analysis and solution of discrete ill-posed problems. The new version allows for under-determined problems, and it is expanded with several new iterative methods, as well as new test problems and new parameter-choice methods.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Calvetti, D., Lewis, B., Reichel, L.: GMRES-type methods for inconsistent systems. Lin. Alg. Appl. 316, 157–169 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  2. Hanke, M.: Conjugate Gradient Methods for Ill-Posed Problems. Longman Scientific and Technical, Essex (1995)

    MATH  Google Scholar 

  3. Hansen, P.C.: Regularization tools: a Matlab package for analysis and solution of discrete ill-posed problems. Numer. Algorithms 6, 1–35(1994)

    Article  MATH  MathSciNet  Google Scholar 

  4. Hansen, P.C.: Regularization Tools version 3.0 for Matlab 5.2. Numer. Algorithms 20, 195–196 (1999)

    Article  MATH  Google Scholar 

  5. Hansen, P.C.: Deconvolution and regularization with Toeplitz matrices. Numer. Algorithms 29, 323–378 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  6. Hansen, P.C., Jensen, T.K.: Smoothing-norm preconditioning for regularizing minimum-norm methods. SIAM J. Matrix Anal. Appl. 29, 1–14 (2006)

    Article  MathSciNet  Google Scholar 

  7. Hansen, P.C., Jensen, T.K., Rodriguez, G.: An adaptive pruning algorithm for the discrete L-curve criterion. J. Comput. Appl. Math. 198, 483–492 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  8. Hansen, P.C., Kilmer, M., Kjeldsen, R.H.: Exploiting residual information in the parameter choice for discrete ill-posed problems. BIT 46, 41–59 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  9. Jacobsen, M., Hansen, P.C., Saunders, M.A.: Subspace preconditioned LSQR for discrete ill-posed problems. BIT 43, 975–989 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  10. Jensen, T.K., Hansen, P.C.: Iterative regularization with minimum-residual methods. BIT 47, 103–120 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  11. Natterer, F., Wübbeling, F.: Mathematical Methods in Image Reconstruction. SIAM, Philadelphia (2001)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Informatics and Mathematical Modelling, Technical University of Denmark, Building 321, 2800, Lyngby, Denmark

    Per Christian Hansen

Authors
  1. Per Christian Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Per Christian Hansen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hansen, P.C. Regularization Tools version 4.0 for Matlab 7.3. Numer Algor 46, 189–194 (2007). https://doi.org/10.1007/s11075-007-9136-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11075-007-9136-9

Keywords

Mathematics Subject Classification (2000)

Search

Navigation