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Proceedings Paper

A descent method for computing the Tikhonov regularized solution of linear inverse problems
Author(s): Fabiana Zama; Elena Loli Piccolomini; Germana Landi
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Paper Abstract

In this paper we describe an iterative algorithm, called Descent-TCG, based on truncated Conjugate Gradient iterations to compute Tikhonov regularized solutions of linear ill-posed problems. Suitable termination criteria are built-up to define an inner-outer iteration scheme for the computation of a regularized solution. Numerical experiments are performed to compare the algorithm with other well-established regularization methods. We observe that the best Descent-TCG results occur for highly noised data and we always get fairly reliable solutions, preventing the dangerous error growth often appearing in other well-established regularization methods. Finally, the Descent-TCG method is computationally advantageous especially for large size problems.

Paper Details

Date Published: 22 October 2004
PDF: 9 pages
Proc. SPIE 5562, Image Reconstruction from Incomplete Data III, (22 October 2004); doi: 10.1117/12.555819
Show Author Affiliations
Fabiana Zama, Univ. degli Studi di Bologna (Italy)
Elena Loli Piccolomini, Univ. degli Studi di Bologna (Italy)
Germana Landi, Univ. degli Studi di Bologna (Italy)

Published in SPIE Proceedings Vol. 5562:
Image Reconstruction from Incomplete Data III
Philip J. Bones; Michael A. Fiddy; Rick P. Millane, Editor(s)

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