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Numerical Algorithms

, Volume 46, Issue 2, pp 189–194 | Cite as

Regularization Tools version 4.0 for Matlab 7.3

  • Per Christian HansenEmail author
Original Paper

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.

Keywords

Regularization Discrete ill-posed problems Matlab 

Mathematics Subject Classification (2000)

65F22 

References

  1. 1.
    Calvetti, D., Lewis, B., Reichel, L.: GMRES-type methods for inconsistent systems. Lin. Alg. Appl. 316, 157–169 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Hanke, M.: Conjugate Gradient Methods for Ill-Posed Problems. Longman Scientific and Technical, Essex (1995)zbMATHGoogle Scholar
  3. 3.
    Hansen, P.C.: Regularization tools: a Matlab package for analysis and solution of discrete ill-posed problems. Numer. Algorithms 6, 1–35(1994)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Hansen, P.C.: Regularization Tools version 3.0 for Matlab 5.2. Numer. Algorithms 20, 195–196 (1999)zbMATHCrossRefGoogle Scholar
  5. 5.
    Hansen, P.C.: Deconvolution and regularization with Toeplitz matrices. Numer. Algorithms 29, 323–378 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Hansen, P.C., Jensen, T.K.: Smoothing-norm preconditioning for regularizing minimum-norm methods. SIAM J. Matrix Anal. Appl. 29, 1–14 (2006)CrossRefMathSciNetGoogle Scholar
  7. 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)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 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)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Jacobsen, M., Hansen, P.C., Saunders, M.A.: Subspace preconditioned LSQR for discrete ill-posed problems. BIT 43, 975–989 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Jensen, T.K., Hansen, P.C.: Iterative regularization with minimum-residual methods. BIT 47, 103–120 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Natterer, F., Wübbeling, F.: Mathematical Methods in Image Reconstruction. SIAM, Philadelphia (2001)zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2007

Authors and Affiliations

  1. 1.Informatics and Mathematical ModellingTechnical University of DenmarkLyngbyDenmark

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