Preconditioned GMRES methods for least squares problems

DOI10.1007/BF03167519zbMath1154.65022MaRDI QIDQ946857

Tokushi Ito, Ken Hayami

Publication date: 25 September 2008

Published in: Japan Journal of Industrial and Applied Mathematics (Search for Journal in Brave)

zbMATH Keywords

algorithmsnumerical examplesnumerical experimentspreconditioningGMRESsingular systemsleast squares problemgeneralized minimal residual methodKrylov subspacelarge sparse matrixill-conditioned systemsincomplete QR decomposition

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)

Related Items (1)

KRYLOV SUBSPACE METHODS WITH DEFLATION AND BALANCING PRECONDITIONERS FOR LEAST SQUARES PROBLEMS

Cites Work

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• A necessary and sufficient convergence condition of orthomin(k) methods for least squares problem with weight
• Preconditioning techniques for nonsymmetric and indefinite linear systems
• GMRES-type methods for inconsistent systems
• GMRES On (Nearly) Singular Systems
• GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems
• A Robust Preconditioner with Low Memory Requirements for Large Sparse Least Squares Problems
• Incomplete Methods for Solving $A^T Ax = b$
• Methods of conjugate gradients for solving linear systems

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