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Analysis of the iteratively regularized Gauss-Newton method under a heuristic rule

Jin, Qinian; Wang, Wei


The iteratively regularized Gauss-Newton method is one of the most prominent regularization methods for solving nonlinear ill-posed inverse problems when the data is corrupted by noise. In order to produce a useful approximate solution, this iterative method should be terminated properly. The existing a priori and a posteriori stopping rules require accurate information on the noise level, which may not be available or reliable in practical applications. In this paper we propose a heuristic...[Show more]

CollectionsANU Research Publications
Date published: 2018
Type: Journal article
Source: Inverse Problems
DOI: 10.1088/1361-6420/aaa0fb


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