A family of preconditioned iteratively regularized methods for nonlinear minimization

A. Smirnova, Rosemary Renaut

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

The preconditioned iteratively regularized Gauss-Newton algorithm for the minimization of general nonlinear functionals was introduced by Smirnova, Renaut and Khan (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.

Original languageEnglish (US)
Pages (from-to)405-418
Number of pages14
JournalJournal of Inverse and Ill-Posed Problems
Volume17
Issue number4
DOIs
StatePublished - Jun 1 2009

Keywords

  • Gauss-Newton method
  • Ill-posed problem
  • Regularization
  • Stopping rule

ASJC Scopus subject areas

  • Applied Mathematics

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