A twin error gauge for Kaczmarz's iterations∗

B.S. van Lith, Per Christian Hansen, Michiel E. Hochstenbach

Research output: Contribution to journalJournal articlepeer-review

26 Downloads (Pure)

Abstract

We propose two new algebraic reconstruction techniques based on Kaczmarz’s method that produce a regularized solution to noisy tomography problems. Tomography problems exhibit semi-convergence when iterative methods are employed, and the aim is therefore to stop near the semi-convergence point. Our approach is based on an error gauge that is constructed by pairing standard down-sweep Kaczmarz’s method with its up-sweep version; we stop the iterations when this error gauge is minimal. The reconstructions of the new methods differ from standard Kaczmarz iterates in that our final result is the average of the stopped up- and down-sweeps. Even when Kaczmarz’s method is supplied with an oracle that provides the exact error–and is therefore able to stop at the best possible iterate – our methods have a lower two-norm error in the vast majority of our test cases. In terms of computational cost, our methods are a little cheaper than standard Kaczmarz equipped with a statistical stopping rule.
Original languageEnglish
JournalSIAM Journal on Scientific Computing
Pages (from-to)S173-S199
ISSN1064-8275
DOIs
Publication statusPublished - 2021

Keywords

  • Computed tomography
  • ART
  • Kaczmarz
  • Stopping rules
  • Error estimation
  • Semiconvergence

Fingerprint

Dive into the research topics of 'A twin error gauge for Kaczmarz's iterations∗'. Together they form a unique fingerprint.

Cite this