Startseite Mathematik The Ivanov regularized Gauss–Newton method in Banach space with an a posteriori choice of the regularization radius
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The Ivanov regularized Gauss–Newton method in Banach space with an a posteriori choice of the regularization radius

  • Barbara Kaltenbacher ORCID logo EMAIL logo , Andrej Klassen und Mario Luiz Previatti de Souza
Veröffentlicht/Copyright: 6. April 2019

Abstract

In this paper, we consider the iteratively regularized Gauss–Newton method, where regularization is achieved by Ivanov regularization, i.e., by imposing a priori constraints on the solution. We propose an a posteriori choice of the regularization radius, based on an inexact Newton/discrepancy principle approach, prove convergence and convergence rates under a variational source condition as the noise level tends to zero and provide an analysis of the discretization error. Our results are valid in general, possibly nonreflexive Banach spaces, including, e.g., L as a preimage space. The theoretical findings are illustrated by numerical experiments.

MSC 2010: 65F22; 65N20

Funding source: Austrian Science Fund

Award Identifier / Grant number: I2271

Award Identifier / Grant number: P30054

Award Identifier / Grant number: Cl 487/1-1

Funding statement: Supported by the FWF under grants I2271 and P30054 and by the DFG under grant Cl 487/1-1, as well as partially by the Karl Popper Kolleg “Modeling-Simulation-Optimization”, funded by the Alpen-Adria-Universität Klagenfurt and by the Carinthian Economic Promotion Fund (KWF).

Acknowledgements

The authors wish to thank Christian Clason, University of Duisburg-Essen for valuable discussions and support with the implementation. Moreover, the authors wish to thank all reviewers for their very careful reading of the manuscript and their detailed reports with valuable comments and suggestions that have led to an improved version of the paper.

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Received: 2018-10-02
Revised: 2019-01-01
Accepted: 2019-02-18
Published Online: 2019-04-06
Published in Print: 2019-08-01

© 2019 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 10.12.2025 von https://www.degruyterbrill.com/document/doi/10.1515/jiip-2018-0093/pdf
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