BiCGCR2: A new extension of conjugate residual method for solving non-Hermitian linear systems
From MaRDI portal
Publication:277203
DOI10.1016/j.cam.2016.03.032zbMath1336.65040OpenAlexW2335301309MaRDI QIDQ277203
Xian-Ming Gu, Bruno Carpentieri, Ting-Zhu Huang
Publication date: 4 May 2016
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.2016.03.032
Krylov subspace methodsBiCGbi-Lanczos procedureBiCRcoupled two-term recurrencesnon-Hermitian linear systems
Computational methods for sparse matrices (65F50) Iterative numerical methods for linear systems (65F10)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The CBiCG class of algorithms for complex symmetric linear systems with applications in several electromagnetic model problems
- A generalized product-type BiCOR method and its application in signal deconvolution
- A comparative study of iterative solutions to linear systems arising in quantum mechanics
- A new family of global methods for linear systems with multiple right-hand sides
- Relaxationsmethoden bester Strategie zur Lösung linearer Gleichungssysteme
- Lanczos-type variants of the COCR method for complex nonsymmetric linear systems
- A COCR method for solving complex symmetric linear systems
- An improved bi-conjugate residual algorithm suitable for distributed parallel computing
- A new quasi-minimal residual method based on a biconjugate \(A\)-orthonormalization procedure and coupled two-term recurrences
- BiCR variants of the hybrid BiCG methods for solving linear systems with nonsymmetric matrices
- An extension of the conjugate residual method to nonsymmetric linear systems
- QMR: A quasi-minimal residual method for non-Hermitian linear systems
- BiCGstab(\(l\)) and other hybrid Bi-CG methods
- BiCGstab(\(l\)) for linear equations involving unsymmetric matrices with complex spectrum
- Experimental study of ILU preconditioners for indefinite matrices
- Generalized conjugate gradient squared
- A hybrid variant of the BiCOR method for a nonsymmetric linear system with a complex spectrum
- A hybridized iterative algorithm of the BiCORSTAB and GPBiCOR methods for solving non-Hermitian linear systems
- BiCR-type methods for families of shifted linear systems
- Why it is Difficult to Solve Helmholtz Problems with Classical Iterative Methods
- The university of Florida sparse matrix collection
- Variational Iterative Methods for Nonsymmetric Systems of Linear Equations
- The BiCOR and CORS Iterative Algorithms for Solving Nonsymmetric Linear Systems
- A Taxonomy for Conjugate Gradient Methods
- Structured preconditioners for nonsingular matrices of block two-by-two structures
- Recent computational developments in Krylov subspace methods for linear systems
- Direct Methods for Sparse Linear Systems
- IDR(s): A Family of Simple and Fast Algorithms for Solving Large Nonsymmetric Systems of Linear Equations
- GMRES: A Generalized Minimal Residual Algorithm for Solving Nonsymmetric Linear Systems
- CGS, A Fast Lanczos-Type Solver for Nonsymmetric Linear systems
- Bi-CGSTAB: A Fast and Smoothly Converging Variant of Bi-CG for the Solution of Nonsymmetric Linear Systems
- Solution of Sparse Indefinite Systems of Linear Equations
- Variants of BICGSTAB for Matrices with Complex Spectrum
- A Quasi-Minimal Residual Variant of the Bi-CGSTAB Algorithm for Nonsymmetric Systems
- GPBi-CG: Generalized Product-type Methods Based on Bi-CG for Solving Nonsymmetric Linear Systems
- ML(k)BiCGSTAB: A BiCGSTAB Variant Based on Multiple Lanczos Starting Vectors
- An Implementation of the Look-Ahead Lanczos Algorithm for Non-Hermitian Matrices
- A Transpose-Free Quasi-Minimal Residual Algorithm for Non-Hermitian Linear Systems
- Generalized Conjugate A-Orthogonal Residual Squared Method for Complex Non-Hermitian Linear Systems
- Methods of conjugate gradients for solving linear systems
This page was built for publication: BiCGCR2: A new extension of conjugate residual method for solving non-Hermitian linear systems
