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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 and Mario Luiz Previatti de Souza
Published/Copyright: April 6, 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.

References

[1] A. B. Bakushinsky and M. Y. Kokurin, Iterative Methods for Approximate Solution of Inverse Problems, Math. Appl. (New York) 577, Springer, Dordrecht, 2004. 10.1007/978-1-4020-3122-9Search in Google Scholar

[2] K. Bredies and D. A. Lorenz, Regularization with non-convex separable constraints, Inverse Problems 25 (2009), no. 8, Article ID 085011. 10.1088/0266-5611/25/8/085011Search in Google Scholar

[3] C. Clason, B. Kaltenbacher and D. Wachsmuth, Functional error estimators for the adaptive discretization of inverse problems, Inverse Problems 32 (2016), no. 10, Article ID 104004. 10.1088/0266-5611/32/10/104004Search in Google Scholar

[4] C. Clason and A. Klassen, Quasi-solution of linear inverse problems in non-reflexive Banach spaces, J. Inverse Ill-Posed Probl. 26 (2018), no. 5, 689–702. 10.1515/jiip-2018-0026Search in Google Scholar

[5] I. N. Dombrovskaja and V. K. Ivanov, On the theory of certain linear equations in abstract spaces, Sibirsk. Mat. Ž. 6 (1965), 499–508. Search in Google Scholar

[6] H. W. Engl, M. Hanke and A. Neubauer, Regularization of Inverse Problems, Math. Appl. 375, Kluwer Academic, Dordrecht, 1996. 10.1007/978-94-009-1740-8Search in Google Scholar

[7] J. Flemming, Generized Tikhonov regularization: Basic theory and comprehensive results on convergence rates, PhD thesis, Technische Universität Chemnitz, 2011. Search in Google Scholar

[8] M. Grasmair, Generalized Bregman distances and convergence rates for non-convex regularization methods, Inverse Problems 26 (2010), no. 11, Article ID 115014. 10.1088/0266-5611/26/11/115014Search in Google Scholar

[9] C. W. Groetsch, Inverse Problems in the Mathematical Sciences, Vieweg Math. Sci. Eng., Friedrich Vieweg & Sohn, Braunschweig, 1993. 10.1007/978-3-322-99202-4Search in Google Scholar

[10] M. Hanke, A regularizing Levenberg–Marquardt scheme, with applications to inverse groundwater filtration problems, Inverse Problems 13 (1997), no. 1, 79–95. 10.1088/0266-5611/13/1/007Search in Google Scholar

[11] M. Hintermüller and R. H. W. Hoppe, Goal-oriented adaptivity in control constrained optimal control of partial differential equations, SIAM J. Control Optim. 47 (2008), no. 4, 1721–1743. 10.1137/070683891Search in Google Scholar

[12] M. Hintermüller, K. Ito and K. Kunisch, The primal-dual active set strategy as a semismooth Newton method, SIAM J. Optim. 13 (2002), no. 3, 865–888. 10.1137/S1052623401383558Search in Google Scholar

[13] B. Hofmann, B. Kaltenbacher, C. Pöschl and O. Scherzer, A convergence rates result for Tikhonov regularization in Banach spaces with non-smooth operators, Inverse Problems 23 (2007), no. 3, 987–1010. 10.1088/0266-5611/23/3/009Search in Google Scholar

[14] B. Hofmann and P. Mathé, Parameter choice in Banach space regularization under variational inequalities, Inverse Problems 28 (2012), no. 10, Article ID 104006. 10.1088/0266-5611/28/10/104006Search in Google Scholar

[15] T. Hohage and F. Weidling, Characterizations of variational source conditions, converse results, and maxisets of spectral regularization methods, SIAM J. Numer. Anal. 55 (2017), no. 2, 598–620. 10.1137/16M1067445Search in Google Scholar

[16] V. K. Ivanov, On linear problems which are not well-posed, Dokl. Akad. Nauk SSSR 145 (1962), 270–272. Search in Google Scholar

[17] V. K. Ivanov, On ill-posed problems, Mat. Sb. (N.S.) 61 (103) (1963), 211–223. Search in Google Scholar

[18] V. K. Ivanov, V. V. Vasin and V. P. Tanana, Theory of Linear Ill-posed Problems and its Applications, 2nd ed., Inverse Ill-posed Probl. Ser., VSP, Utrecht, 2002. 10.1515/9783110944822Search in Google Scholar

[19] Q. Jin and H. Yang, Levenberg–Marquardt method in Banach spaces with general convex regularization terms, Numer. Math. 133 (2016), no. 4, 655–684. 10.1007/s00211-015-0764-zSearch in Google Scholar

[20] B. Kaltenbacher, P. Hungerländer and F. Rendl, Regularization of inverse problems via box constrained minimization, preprint (2018), https://arxiv.org/abs/1807.11316. Search in Google Scholar

[21] B. Kaltenbacher and M. L. Previatti de Souza, Convergence and adaptive discretization of the IRGNM Tikhonov and the IRGNM Ivanov method under a tangential cone condition in Banach space, Numer. Math. 140 (2018), no. 2, 449–478. 10.1007/s00211-018-0971-5Search in Google Scholar PubMed PubMed Central

[22] B. Kaltenbacher, A. Kirchner and S. Veljović, Goal oriented adaptivity in the IRGNM for parameter identification in PDEs: I. reduced formulation, Inverse Problems 30 (2014), no. 4, Article ID 0450011. 10.1088/0266-5611/30/4/045001Search in Google Scholar

[23] B. Kaltenbacher and A. Klassen, On convergence and convergence rates for Ivanov and Morozov regularization and application to some parameter identification problems in elliptic PDEs, Inverse Problems 34 (2018), no. 5, Article ID 055008. 10.1088/1361-6420/aab739Search in Google Scholar

[24] B. Kaltenbacher, A. Neubauer and O. Scherzer, Iterative Regularization Methods for Nonlinear Ill-posed Problems, Radon Ser. Comput. Appl. Math. 6, Walter de Gruyter, Berlin, 2008. 10.1515/9783110208276Search in Google Scholar

[25] A. Kirsch, An Introduction to the Mathematical Theory of Inverse Problems, Appl. Math. Sci. 120, Springer, New York, 1996. 10.1007/978-1-4612-5338-9Search in Google Scholar

[26] D. Lorenz and N. Worliczek, Necessary conditions for variational regularization schemes, Inverse Problems 29 (2013), no. 7, Article ID 075016. 10.1088/0266-5611/29/7/075016Search in Google Scholar

[27] A. K. Louis, Inverse und schlecht gestellte Probleme, Teubner Studienbücher Math., B. G. Teubner, Stuttgart, 1989. 10.1007/978-3-322-84808-6Search in Google Scholar

[28] V. A. Morozov, Regularization Methods for Ill-posed Problems, CRC Press, Boca, 1993. Search in Google Scholar

[29] A. Neubauer and R. Ramlau, On convergence rates for quasi-solutions of ill-posed problems, Electron. Trans. Numer. Anal. 41 (2014), 81–92. Search in Google Scholar

[30] A. Rieder, On convergence rates of inexact Newton regularizations, Numer. Math. 88 (2001), no. 2, 347–365. 10.1007/PL00005448Search in Google Scholar

[31] T. Schuster, B. Kaltenbacher, B. Hofmann and K. S. Kazimierski, Regularization Methods in Banach Spaces, Radon Ser. Comput. Appl. Math. 10, Walter de Gruyter, Berlin, 2012. 10.1515/9783110255720Search in Google Scholar

[32] T. I. Seidman and C. R. Vogel, Well-posedness and convergence of some regularisation methods for nonlinear ill posed problems, Inverse Problems 5 (1989), no. 2, 227–238. 10.1088/0266-5611/5/2/008Search in Google Scholar

[33] A. N. Tikhonov and V. A. Arsenin, Methods for Solving Ill-posed Problems, Nauka, Moscow, 1979. Search in Google Scholar

[34] M. Ulbrich, Semismooth Newton methods for variational inequalities and constrained optimization problems in function spaces, MOS-SIAM Ser. Optim. 11, Society for Industrial and Applied Mathematics, Philadelphia, 2011. 10.1137/1.9781611970692Search in Google Scholar

[35] V. V. Vasin and A. L. Ageev, Ill-posed Problems with A Priori Information, Inverse Ill-posed Probl. Ser., VSP, Utrecht, 1995. 10.1515/9783110900118Search in Google Scholar

[36] G. M. Vaĭnikko and A. Y. Veretennikov, Iteration Procedures in Ill-posed Problems, (in Russian), “Nauka”, Moscow, 1986. Search in Google Scholar

[37] B. Vexler and W. Wollner, Adaptive finite elements for elliptic optimization problems with control constraints, SIAM J. Control Optim. 47 (2008), no. 1, 509–534. 10.1137/070683416Search in Google Scholar

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

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