Fast regularized structured total least squares algorithm for solving the basic deconvolution problem | Zendy

Mastronardi N. | Zendy; Lemmerling P. | Zendy; Van Huffel S. | Zendy

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Fast regularized structured total least squares algorithm for solving the basic deconvolution problem

Author(s) -

Mastronardi N.,

Lemmerling P.,

Van Huffel S.

Publication year - 2004

Publication title -

numerical linear algebra with applications

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.02

H-Index - 53

eISSN - 1099-1506

pISSN - 1070-5325

DOI - 10.1002/nla.413

Subject(s) - deconvolution , mathematics , algorithm , regularization (linguistics) , estimator , least squares function approximation , mathematical optimization , total least squares , non linear least squares , blind deconvolution , computer science , estimation theory , statistics , artificial intelligence , singular value decomposition

In this paper, we develop a fast regularized structured total least squares (RSTLS) algorithm for solving the basic deconvolution problem. The algorithm is based on a particular implementation of the generalized Schur algorithm. We apply the new algorithm on a deconvolution problem arising in a medical application in renography. By means of this example, we show that regularization in the structured total least squares framework yields more accurate results compared to other estimators. Copyright © 2004 John Wiley & Sons, Ltd.

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