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On a heuristic stopping rule for the regularization of inverse problems by the augmented Lagrangian method

Jin, Qinian


In this paper we propose a heuristic stopping rule of Hanke–Raus type for the regularization of linear ill-posed inverse problems by the augmented Lagrangian method. This stopping rule requires no information on the noise level. Under certain source conditions, we derive a posteriori error estimates in term of Bregman distance. By imposing certain conditions on the noise data, we establish convergence results. Numerical results are presented to illustrate the performance

CollectionsANU Research Publications
Date published: 2017
Type: Journal article
Source: Numerische Mathematik
DOI: 10.1007/s00211-016-0860-8


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