On a variable smoothing procedure for Krylov subspace methods

DOI10.1016/S0024-3795(97)00037-2zbMath0886.65031MaRDI QIDQ1375088

Mohammed Heyouni, Hassane Sadok

Publication date: 6 January 1998

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

zbMATH Keywords

convergencesmoothingGalerkin-Krylov subspace methodminimal residual seminorm methods

Mathematics Subject Classification ID

Iterative numerical methods for linear systems (65F10)

Related Items (12)

On the convergence of Q-OR and Q-MR Krylov methods for solving nonsymmetric linear systems ⋮ Heavy ball restarted CMRH methods for linear systems ⋮ Restarted Hessenberg method for solving shifted nonsymmetric linear systems ⋮ A unified approach to Krylov subspace methods for solving linear systems ⋮ On global Hessenberg based methods for solving Sylvester matrix equations ⋮ An optimal Q-OR Krylov subspace method for solving linear systems ⋮ Flexible global generalized Hessenberg methods for linear systems with multiple right-hand sides ⋮ A new implementation of the CMRH method for solving dense linear systems ⋮ Algorithms for the CMRH method for dense linear systems ⋮ Smoothing iterative block methods for linear systems with multiple right-hand sides ⋮ Matrix Krylov subspace methods for linear systems with multiple right-hand sides ⋮ A Hessenberg-type algorithm for computing PageRank problems

Uses Software

• FIDISOL
• CMRH

Cites Work

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• On certain methods for expanding the characteristic polynomial
• Variations on Arnoldi's method for computing eigenelements of large unsymmetric matrices
• QMR: A quasi-minimal residual method for non-Hermitian linear systems
• CMRH: A new method for solving nonsymmetric linear systems based on the Hessenberg reduction algorithm
• Practical Use of Some Krylov Subspace Methods for Solving Indefinite and Nonsymmetric Linear Systems
• GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems
• Residual Smoothing Techniques for Iterative Methods
• Some Applications of the Pseudoinverse of a Matrix
• A Theoretical Comparison of the Arnoldi and GMRES Algorithms
• The principle of minimized iterations in the solution of the matrix eigenvalue problem

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