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A data-driven iteratively regularized Landweber iteration
- Publication Year :
- 2018
-
Abstract
- We derive and analyse a new variant of the iteratively regularized Landweber iteration, for solving linear and nonlinear ill-posed inverse problems. The method takes into account training data, which are used to estimate the interior of a black box, which is used to define the iteration process. We prove convergence and stability for the scheme in infinite dimensional Hilbert spaces. These theoretical results are complemented by several numerical experiments for solving linear inverse problems for the Radon transform and a nonlinear inverse problem for Schlieren tomography.
- Subjects :
- Mathematics - Numerical Analysis
65J20 (65J10, 65J15, 65J22)
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1812.00272
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1080/01630563.2020.1740734