Minimum residual methods for augmented systems

DOI10.1007/BF02510258zbMath0914.65026OpenAlexW2022852340MaRDI QIDQ1272879

Bernd Fischer, David J. Silvester, Andrew J. Wathen, Alison Ramage

Publication date: 21 June 1999

Published in: BIT (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02510258

zbMATH Keywords

convergencepreconditioningconjugate gradient methoditerative methodsGMRESKrylov subspace methodaugmented systemslarge systemsMINRESLSQR algorithmminimum residual methodsindefinite matrix problemssystem of normal equations

Mathematics Subject Classification ID

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

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Uses Software

• LSQR
• CRAIG

Cites Work

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• QMR: A quasi-minimal residual method for non-Hermitian linear systems
• Solution of sparse rectangular systems using LSQR and Craig
• Necessary and Sufficient Conditions for the Existence of a Conjugate Gradient Method
• GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems
• LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
• Mixed and Hybrid Finite Element Methods
• How Fast are Nonsymmetric Matrix Iterations?
• Solution of Sparse Indefinite Systems of Linear Equations
• Fast Iterative Solution of Stabilised Stokes Systems Part II: Using General Block Preconditioners
• Fast Nonsymmetric Iterations and Preconditioning for Navier–Stokes Equations
• Iterative refinement of linear least squares solutions I
• The principle of minimized iterations in the solution of the matrix eigenvalue problem

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