Preconditioned conjugate gradient method for rank deficient least-squares problems

DOI10.1080/00207169908804872zbMath0948.65041OpenAlexW2108012307MaRDI QIDQ4944666

C. H. Santos, J. Y. Yuan

Publication date: 13 November 2000

Published in: International Journal of Computer Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207169908804872

zbMATH Keywords

comparison of methodspreconditioningerror boundsiterative methodconjugate gradient methodsuccessive overrelaxationlarge sparse problemsrank deficient least squares problems

Mathematics Subject Classification ID

Computational methods for sparse matrices (65F50) Numerical solutions to overdetermined systems, pseudoinverses (65F20) Iterative numerical methods for linear systems (65F10) Numerical computation of matrix norms, conditioning, scaling (65F35)

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Optimal parameters of GSOR-like methods for solving the augmented linear systems ⋮ Symmetric SOR method for augmented systems ⋮ A class of triangular splitting methods for saddle point problems ⋮ A modified symmetric successive overrelaxation method for augmented systems ⋮ Evaluation of ST preconditioners for saddle point problems ⋮ The direct-projection methods ⋮ Generalization: One technique of computational and applied mathematical methodology ⋮ On generalized symmetric SOR method for augmented systems ⋮ Symmetric-triangular decomposition and its applications. II: Preconditioners for indefinite systems ⋮ Block SOR methods for rank-deficient least-squares problems

Cites Work

• Successive overrelaxation methods for solving the rank deficient linear least squares problem
• On the rate of convergence of the preconditioned conjugate gradient method
• Convergence of a direct-iterative method for large-scale least-squares problems
• Block SOR methods for rank-deficient least-squares problems
• An implicit deflation method for ill-conditioned Sylvester and Lyapunov equations