For the solution of linear systems of equations with unsymmetric coefficient matrix, Freund and Nachtigal (SIAM J. Sci. Comput. 15 (1994), 313–337) proposed a Kryloy subspace method called Quasi-Minimal Residual method (QMR). The two main ingredients of QMR are the unsymmetric Lanczos algorithm and the quasi-minimal residual approach that minimizes a factor of the residual vector rather than the residual itself. The Lanczos algorithm spans a Krylov subspace by generating two sequences of biorthogonal vectors called Lanczos vectors. Due to the orthogonalization and scaling of the Lanczos vectors, algorithms that make use of the Lanczos process contain inner products leading to global communication and synchronization on parallel processors. ...
In this report a parallel implementation of the QMR algorithm with look-ahead for solving large spar...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1991.Includes bibliogr...
Among the iterative methods for solving large linear systems with a sparse (or, possibly, structured...
For the solutions of linear systems of equations with unsymmetric coefficient matrices, we has propo...
A new version of the unsymmetric Lanczos algorithm without look-ahead is described combining element...
A new version of the unsymmetric Lanczos algorithm without look-ahead is described combining element...
. Recently, the authors have proposed a new Krylov subspace iteration, the quasi-minimal residual al...
For the solution of large sparse systems of linear equations with general non-Hermitian coefficient ...
The authors have proposed a new Krylov subspace iteration, the quasi-minimal residual algorithm (QMR...
For the solutions of unsymmetric linear systems of equations, we have proposed an improved version o...
The Lanczos algorithm is among the most frequently used iterative techniques for computing a few dom...
Despite its usefulness in solving eigenvalue problems and linear systems of equations, the nonsymmet...
Introduction One of the fundamental task of numerical computing is the ability to solve linear syst...
Today the most popular iterative methods for solving nonsymmetric linear systems are Krylov methods....
Many applications require the solution of multiple linear systems that have the same coefficient mat...
In this report a parallel implementation of the QMR algorithm with look-ahead for solving large spar...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1991.Includes bibliogr...
Among the iterative methods for solving large linear systems with a sparse (or, possibly, structured...
For the solutions of linear systems of equations with unsymmetric coefficient matrices, we has propo...
A new version of the unsymmetric Lanczos algorithm without look-ahead is described combining element...
A new version of the unsymmetric Lanczos algorithm without look-ahead is described combining element...
. Recently, the authors have proposed a new Krylov subspace iteration, the quasi-minimal residual al...
For the solution of large sparse systems of linear equations with general non-Hermitian coefficient ...
The authors have proposed a new Krylov subspace iteration, the quasi-minimal residual algorithm (QMR...
For the solutions of unsymmetric linear systems of equations, we have proposed an improved version o...
The Lanczos algorithm is among the most frequently used iterative techniques for computing a few dom...
Despite its usefulness in solving eigenvalue problems and linear systems of equations, the nonsymmet...
Introduction One of the fundamental task of numerical computing is the ability to solve linear syst...
Today the most popular iterative methods for solving nonsymmetric linear systems are Krylov methods....
Many applications require the solution of multiple linear systems that have the same coefficient mat...
In this report a parallel implementation of the QMR algorithm with look-ahead for solving large spar...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1991.Includes bibliogr...
Among the iterative methods for solving large linear systems with a sparse (or, possibly, structured...