Noise representation in residuals of LSQR, LSMR, and CRAIG regularization-Reference-Cited by-同舟云学术

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

Published:2017-11 Issue: Volume:533 Page:357-379
ISSN:0024-3795
Container-title:Linear Algebra and its Applications
language:en
Short-container-title:Linear Algebra and its Applications

Author:

Hnětynková Iveta,Kubínová Marie^ORCID,Plešinger Martin

Funder

Univerzita Karlova v Praze

Grantová Agentura České Republiky

Publisher

Elsevier BV

Subject

Discrete Mathematics and Combinatorics,Geometry and Topology,Numerical Analysis,Algebra and Number Theory

Reference36 articles.

1. Discrete Inverse Problems;Hansen,2010

2. Rank-Deficient and Discrete Ill-Posed Problems;Hansen,1998

3. LSQR: an algorithm for sparse linear equations and sparse least squares;Paige;ACM Trans. Math. Software,1982

4. A bidiagonalization algorithm for solving large and sparse ill-posed systems of linear equations;Björck;BIT,1988

5. Computing projections with LSQR;Saunders;BIT,1997

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2. Inversion Algorithm of Insulator Surface Charge Density Based on Hybrid LSMR Algorithm for Shift-Variant System;IEEE Transactions on Dielectrics and Electrical Insulation;2024-06

3. Computational Methods for Large-Scale Inverse Problems: A Survey on Hybrid Projection Methods;SIAM Review;2024-05

4. Algebraic properties of projected problems in LSQR;PAMM;2023-09-13

5. Iterative hybrid regularization for extremely noisy full models in single particle analysis;Linear Algebra and its Applications;2023-01

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