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The regularizing effect of the Golub-Kahan iterative bidiagonalization and revealing the noise level in the data

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Abstract

Regularization techniques based on the Golub-Kahan iterative bidiagonalization belong among popular approaches for solving large ill-posed problems. First, the original problem is projected onto a lower dimensional subspace using the bidiagonalization algorithm, which by itself represents a form of regularization by projection. The projected problem, however, inherits a part of the ill-posedness of the original problem, and therefore some form of inner regularization must be applied. Stopping criteria for the whole process are then based on the regularization of the projected (small) problem.

In this paper we consider an ill-posed problem with a noisy right-hand side (observation vector), where the noise level is unknown. We show how the information from the Golub-Kahan iterative bidiagonalization can be used for estimating the noise level. Such information can be useful for constructing efficient stopping criteria in solving ill-posed problems.

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Authors and Affiliations

  1. Institute of Computer Science, Academy of Sciences, Pod Vodárenskou věží 2, Prague 8, Czech Republic

    Iveta Hnětynková, Martin Plešinger & Zdeněk Strakoš

  2. Faculty of Mathematics and Physics, Charles University in Prague, Sokolovská 83, Prague 8, Czech Republic

    Iveta Hnětynková & Zdeněk Strakoš

  3. Faculty of Mechatronics, Technical University of Liberec, Studentská 2/1402, Liberec, Czech Republic

    Martin Plešinger

Authors
  1. Iveta Hnětynková
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  2. Martin Plešinger
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  3. Zdeněk Strakoš
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Correspondence to Iveta Hnětynková.

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Communicated by Lars Eldén.

The work of the first author was supported by the research project MSM0021620839 financed by MŠMT. The work of the second and the third author was supported by the GAAS grant IAA100300802, and by the Institutional Research Plan AV0Z10300504.

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Hnětynková, I., Plešinger, M. & Strakoš, Z. The regularizing effect of the Golub-Kahan iterative bidiagonalization and revealing the noise level in the data. Bit Numer Math 49, 669–696 (2009). https://doi.org/10.1007/s10543-009-0239-7

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