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A modified discrepancy principle to attain optimal convergence rates under unknown noise

  • We consider a linear ill-posed equation in the Hilbert space setting. Multiple independent unbiased measurements of the right-hand side are available. A natural approach is to take the average of the measurements as an approximation of the right-hand side and to estimate the data error as the inverse of the square root of the number of measurements. We calculate the optimal convergence rate (as the number of measurements tends to infinity) under classical source conditions and introduce a modified discrepancy principle, which asymptotically attains this rate.

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Author:Tim Nikolas JahnORCiDGND
URN:urn:nbn:de:hebis:30:3-631838
DOI:https://doi.org/10.1088/1361-6420/ac1775
ISSN:1361-6420
Parent Title (English):Inverse problems
Publisher:Inst.
Place of publication:Bristol [u.a.]
Document Type:Article
Language:English
Date of Publication (online):2021/08/11
Date of first Publication:2021/08/11
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2024/05/08
Tag:convergence; discrepancy principle; optimality; spectral cut-off; statistical inverse problems
Volume:37
Issue:9, art. 095008
Article Number:095008
Page Number:23
First Page:1
Last Page:23
Institutes:Informatik und Mathematik
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0