Volume 42, pp. 13-40, 2014.

Large-scale dual regularized total least squares

Jörg Lampe and Heinrich Voss

Abstract

The total least squares (TLS) method is a successful approach for linear problems when not only the right-hand side but also the system matrix is contaminated by some noise. For ill-posed TLS problems, regularization is necessary to stabilize the computed solution. In this paper we present a new approach for computing an approximate solution of the dual regularized large-scale total least squares problem. An iterative method is proposed which solves a convergent sequence of projected linear systems and thereby builds up a highly suitable search space. The focus is on an efficient implementation with particular emphasis on the reuse of information.

Full Text (PDF) [489 KB], BibTeX

Key words

total least squares, regularization, ill-posedness, generalized eigenproblem

AMS subject classifications

65F15, 65F22, 65F30

Links to the cited ETNA articles

[15]	Vol. 31 (2008), pp. 12-24 Jörg Lampe and Heinrich Voss: A fast algorithm for solving regularized total least squares problems

ETNA articles which cite this article

Vol. 55 (2022), pp. 1-75 Jörg Lampe and Heinrich Voss: A survey on variational characterizations for nonlinear eigenvalue problems

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