Noise representation in residuals of LSQR, LSMR, and CRAIG regularization

DOI10.1016/j.laa.2017.07.031zbMath1391.15062arXiv1612.05551OpenAlexW2611081798MaRDI QIDQ2404979

Martin Plešinger, Marie Kubínová, Iveta Hnetynkova

Publication date: 21 September 2017

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1612.05551

zbMATH Keywords

regularizationill-posed problemsLSQRLSMRCRAIGGolub-Kahan iterative bidiagonalization

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22) Inverse problems in linear algebra (15A29) Iterative numerical methods for linear systems (65F10)

Related Items (6)

Regularization properties of Krylov iterative solvers CGME and LSMR for linear discrete ill-posed problems with an application to truncated randomized SVDs ⋮ Krylov methods for inverse problems: Surveying classical, and introducing new, algorithmic approaches ⋮ Regularization properties of LSQR for linear discrete ill-posed problems in the multiple singular value case and best, near best and general low rank approximations ⋮ Approximation accuracy of the Krylov subspaces for linear discrete ill-posed problems ⋮ The low rank approximations and Ritz values in LSQR for linear discrete ill-posed problem ⋮ Iterative hybrid regularization for extremely noisy full models in single particle analysis

Uses Software

• LSQR
• Regularization tools
• RestoreTools
• AIR tools
• LSMR
• CRAIG

Cites Work

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• The N‐Step Iteration Procedures

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