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A family of preconditioned iteratively regularized methods for nonlinear minimization
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A. Smirnova
Published/Copyright:
June 16, 2009
Abstract
The preconditioned iteratively regularized Gauss–Newton algorithm for the minimization of general nonlinear functionals was introduced by Smirnova, Renaut and Khan (Inverse Problems 23: 1547–1563, 2007). In this paper, we establish theoretical convergence results for an extended stabilized family of Generalized Preconditioned Iterative methods which includes ℳ-times iterated Tikhonov regularization with line search. Numerical schemes illustrating the theoretical results are also presented.
Received: 2008-11-05
Published Online: 2009-06-16
Published in Print: 2009-June
© de Gruyter 2009
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Articles in the same Issue
- External sources of resonance type in X-ray tomography
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