SIRT- and CG-type methods for the iterative solution of sparse linear least-squares problems

DOI10.1016/0024-3795(90)90215-XzbMath0702.65042OpenAlexW2087111693MaRDI QIDQ915373

A. van der Sluis, Henk A. van der Vorst

Publication date: 1990

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

Full work available at URL: https://doi.org/10.1016/0024-3795(90)90215-x

zbMATH Keywords

convergenceregularizationconjugate gradient methodtomographySIRT methodsleast-squares problemsCG-type methodssparse linear least-squares

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Iterative numerical methods for linear systems (65F10)

Related Items

Accuracy and effectiveness of preconditioned conjugate gradient algorithms for large and ill-conditioned problems, Bounds on the quality of reconstructed images in binary tomography, AIR tools II: algebraic iterative reconstruction methods, improved implementation, AIR tools -- a MATLAB package of algebraic iterative reconstruction methods, SIRT- and CG-type methods for the iterative solution of sparse linear least-squares problems, Choosing the relaxation parameter in sequential block-iterativemethods for linear systems, Accelerated Landweber iterations for the solution of ill-posed equations, Bounds on the largest singular value of a matrix and the convergence of simultaneous and block‐iterative algorithms for sparse linear systems, Image restoration by cosine transform-based iterative regularization, EM Algorithms, Schultz matrix iteration based method for stable solution of discrete ill-posed problems, Approximation accuracy of the Krylov subspaces for linear discrete ill-posed problems, Further results on the convergence behavior of conjugate-gradients and Ritz values, The discrete Picard condition for discrete ill-posed problems, The conjugate gradient regularization method in computed tomography problems, Regularization tools: A Matlab package for analysis and solution of discrete ill-posed problems, Analysis of approximate inverses in tomography. II: Iterative inverses

Uses Software

• LSQR
• CRAIG

Cites Work

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• Conjugate Gradient-Like Algorithms for Solving Nonsymmetric Linear Systems
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• LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
• Accelerated projection methods for computing pseudoinverse solutions of systems of linear equations