A variant of IDRstab with reliable update strategies for solving sparse linear systems
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Publication:2252832
DOI10.1016/j.cam.2013.08.028zbMath1291.65093OpenAlexW2074426261MaRDI QIDQ2252832
Kuniyoshi Abe, Kensuke Aihara, Emiko Ishiwata
Publication date: 23 July 2014
Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cam.2013.08.028
Computational methods for sparse matrices (65F50) Iterative numerical methods for linear systems (65F10)
Related Items (5)
Variants of the groupwise update strategy for short-recurrence Krylov subspace methods ⋮ A quasi-minimal residual variant of IDRstab using the residual smoothing technique ⋮ Reliability Investigation of BiCGStab and IDR Solvers for the Advection-Diffusion-Reaction Equation ⋮ Mstab: Stabilized Induced Dimension Reduction for Krylov Subspace Recycling ⋮ Development of the Block BiCGSTAB($\ell$) method for solving linear systems with multiple right hand sides
Uses Software
Cites Work
- IDR: a new generation of Krylov subspace methods?
- Bi-CGSTAB as an induced dimension reduction method
- GBi-CGSTAB(\(s,L\)): IDR(\(s\)) with higher-order stabilization polynomials
- BiCGstab(\(l\)) and other hybrid Bi-CG methods
- BiCGstab(\(l\)) for linear equations involving unsymmetric matrices with complex spectrum
- Maintaining convergence properties of BiCGStab methods in finite precision arithmetic
- Reliable updated residuals in hybrid Bi-CG methods
- An alternative implementation of the IDRstab method saving vector updates
- Exploiting BiCGstab($\ell$) Strategies to Induce Dimension Reduction
- IDR(s): A Family of Simple and Fast Algorithms for Solving Large Nonsymmetric Systems of Linear Equations
- Bi-CGSTAB: A Fast and Smoothly Converging Variant of Bi-CG for the Solution of Nonsymmetric Linear Systems
- Lanczos Methods for the Solution of Nonsymmetric Systems of Linear Equations
- Estimating the Attainable Accuracy of Recursively Computed Residual Methods
- Inexact Krylov Subspace Methods for Linear Systems
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