Generalized convergence rates results for linear inverse problems in hilbert spaces

Roman Andreev, Peter Elbau, Maarten V. De Hoop, Lingyun Qiu, Otmar Scherzer

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

In recent years, a series of convergence rates conditions for regularization methods has been developed. Mainly, the motivations for developing novel conditions came from the desire to carry over convergence rates results from the Hilbert space setting to generalized Tikhonov regularization in Banach spaces. For instance, variational source conditions have been developed, and they were expected to be equivalent to standard source conditions for linear inverse problems in a Hilbert space setting (see Schuster et al. [13]). We show that this expectation does not hold. However, in the standard Hilbert space setting these novel conditions are optimal, which we prove by using some deep results from Neubauer [11], and generalize existing convergence rates results. The key tool in our analysis is a homogeneous source condition, which we put into relation to the other existing source conditions from the literature. As a positive by-product, convergence rates results can be proven without spectral theory, which is the standard technique for proving convergence rates for linear inverse problems in Hilbert spaces (see Groetsch [7]).

Original languageEnglish (US)
Pages (from-to)549-566
Number of pages18
JournalNumerical Functional Analysis and Optimization
Volume36
Issue number5
DOIs
StatePublished - May 4 2015

Bibliographical note

Funding Information:
This work has been supported by the Austrian Science Fund (FWF) within the national research network Geometry + Simulation (project S11704, Variational Methods for Imaging on Manifolds). This research was supported in part by National Science Foundation grant CMG DMS-1025318, and in part by the members of the Geo-Mathematical Imaging Group at Purdue University.

Keywords

  • Convergence rates
  • Tikhonov regularization

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