Investigation of Preconditioning Techniques for the Iteratively Regularized Gauss–Newton Method for Exponentially Ill-Posed Problems

DOI10.1137/090749967zbMath1220.65061OpenAlexW1982107329MaRDI QIDQ3006130

Stefan Langer

Publication date: 10 June 2011

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/090749967

zbMATH Keywords

Lanczos methodpreconditioningconjugate gradient methodnonlinear inverse problemsinverse scatteringregularized Newton methodsill-conditioned nonlinear equations

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22) Numerical computation of solutions to systems of equations (65H10) Preconditioners for iterative methods (65F08)

Related Items (3)

Convergence analysis of a proximal Gauss-Newton method ⋮ A direct sampling-based deep learning approach for inverse medium scattering problems ⋮ Theoretical and Numerical Study of Iteratively Truncated Newton’s Algorithm

This page was built for publication: Investigation of Preconditioning Techniques for the Iteratively Regularized Gauss–Newton Method for Exponentially Ill-Posed Problems