On the real convergence rate of the conjugate gradient method

Linear Algebra and Its Applications

Volumn 154-156, Issue C, 1991, Pages 535-549

  Strakos Z ^a  

^a INSTITUTE OF MATHEMATICS   (Czech Republic)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0000685145     PISSN: 00243795     EISSN: None     Source Type: Journal    
DOI: 10.1016/0024-3795(91)90393-B     Document Type: Article

References (27)

1. On the rate of convergence of the preconditioned conjugate gradient method
2. On the eigenvalue distribution of a class of preconditioning methods
3. Round-off Error Analysis of Descent Methods for Solving Linear Equations
4. Numerical stability of descent methods for solving linear equations
5. Comparison of splittings used with the conjugate gradient algorithm
6. Methods of conjugate gradients for solving linear systems
7. Influence of the eigenvalue spectrum on the convergence rate of the conjugate gradient method
8. Behavior of slightly perturbed Lanczos and conjugate gradient recurrences
9. Solution of sparse indefinite systems of linear equations
10. Accuracy and effectiveness of the Lanczos algorithm for the symmetric eigenproblem
11. LSQR An algorithm for sparse linear equations and sparse least squares
12. A new look at the Lanczos algorithm for solving symmetric systems of linear equations
13. On the method of conjugate gradients for solution of large sparse systems of linear equations
14. The Lanczos Algorithm for Solving Symmetric Linear Systems
15. The Lanczos algorithm with partial reorthogonalization
16. Analysis of the symmetric Lanczos algorithm with reorthogonalization methods
17. A remark on the rate of convergence of the conjugate gradient method for the solution of linear systems (in Czech)
18. On the rate of convergence of conjugate gradient method for solution of linear systems
19. The Conjugate Gradient Method for the Solution of Linear Systems (in Czech)
20. Preconditioning by Incomplete Decompositions
21. Roundoff-error analysis of a new class of conjugate-gradient algorithms