Regularization Tools Version 4.1 (for MATLAB Version 7.3)
A MATLAB package for analysis and solution of discrete ill-posed problems,
Prof. Per Christian Hansen,
DTU Compute, Technical University of Denmark.
The software is available from:
- Mathwork's MATLAB Central at
please note and respect the BSD License associated with this software.
The software package Regularization Tools, Version 4.1 (for MATLAB
Version 7.3), consists of a collection of documented
functions for analysis and solution of discrete ill-posed problems.
By means of this package, the user can experiment with different
regularization strategies, compare them, and draw conclusions that would
otherwise require a major programming effort.
In addition to the analysis and solution routines, the package also
includes 12 test problems.
The package and the underlying theory is published in:
The most recent version of the package is described in:
- P. C. Hansen, Regularization Tools: A Matlab package for analysis and
solution of discrete ill-posed problems, Numerical Algorithms, 6
(1994), pp. 1-35.
- P. C. Hansen, Regularization Tools Version 4.0 for Matlab 7.3,
Numerical Algorithms, 46 (2007), pp. 189-194.
The accompanying manual, which also includes a description of the
underlying algorithms, as well as a tutorial, is electronically available:
Additional MATLAB software
The function TVreg.m computes a 1D Total
Variation regularized solution.
The function preprocL.m can be used to preprocess an
arbitrary L matrix such that it conforms with the requirements in
Regularization Tools; requires that the
UTV Tools package
The 212-times-100 helioseismology problem used in several of my
papers is available either as an m-file
helio.m or as a mat-file
helio.mat (note: some browsers try to change
the file extension when saving this mat-file).
The functions mblur.m and oblur.m
compute block Toeplitz matrices representing motion blur and out-of-focus
The function pptsvd.m computes piecewise
polynomial regularized solutions by means of the PP-TSVD algorithm.
Note that the computing time can be very large for large problems.