A Regularized Total Least Squares Algorithm

Hongbin Guo () and Rosemary A. Renaut ()
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Hongbin Guo: Arizona State University, Department of Mathematics
Rosemary A. Renaut: Arizona State University, Department of Mathematics

A chapter in Total Least Squares and Errors-in-Variables Modeling, 2002, pp 57-66 from Springer

Abstract: Abstract Error-contaminated systems A x ≈ b, for which A is ill-conditioned, are considered. Such systems may be solved using Tikhonov-like regularized total least squares (R-TLS) methods. Golub et al, 1999, presented a direct algorithm for the solution of the Lagrange multiplier formulation for the R-TLS problem. Here we present a parameter independent algorithm for the approximate R-TLS solution. The algorithm, which utilizes the shifted inverse power method, relies only on a prescribed estimate for the regularization constraint condition and does not require the specification of other regularization parameters. An extension of the algorithm for nonsmooth solutions is also presented.

Keywords: total least squares; regularization. (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-017-3552-0_6

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DOI: 10.1007/978-94-017-3552-0_6

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