Result 1 to 20 from 29 total
Numerical stability of orthogonalization methods with a non-standard inner product. (English)
BIT 52, No. 4, 1035-1058 (2012).
1
Partitioned triangular tridiagonalization (English)
ACM Trans. Math. Softw. 37, No. 4, 38 (2011).
2
Adaptive version of simpler GMRES. (English)
Numer. Algorithms 53, No. 1, 93-112 (2010).
3
How to make simpler GMRES and GCR more stable. (English)
SIAM J. Matrix Anal. Appl. 30, No. 4, 1483-1499 (2008).
4
Maximum attainable accuracy of inexact saddle point solvers. (English)
SIAM J. Matrix Anal. Appl. 29, No. 4, 1297-1321 (2008).
5
Limiting accuracy of segregated solution methods for nonsymmetric saddle point problems. (English)
J. Comput. Appl. Math. 215, No. 1, 28-37 (2008).
6
How to make simpler GMRES and GCR more stable (English)
SIAM J. Matrix Analysis Applications 30, No. 4, 1483-1499 (2008).
7
Maximum attainable accuracy of inexact saddle point solvers (English)
SIAM J. Matrix Analysis Applications 29, No. 4, 1297-1321 (2007).
8
Dual variable methods for mixed-hybrid finite element approximation of the potential fluid flow problem in porous media. (English)
ETNA, Electron. Trans. Numer. Anal. 22, 17-40, electronic only (2006).
9
Modified Gram-Schmidt (MGS), least squares, and backward stability of MGS-GMRES. (English)
SIAM J. Matrix Anal. Appl. 28, No. 1, 264-284 (2006).
10
Modified gram-Schmidt (MGS), least squares, and backward stability of MGS-GMRES (English)
SIAM J. Matrix Analysis Applications 28, No. 1, 264-284 (2006).
11
The loss of orthogonality in the Gram-Schmidt orthogonalization process. (English)
Comput. Math. Appl. 50, No. 7, 1069-1075 (2005).
12
Rounding error analysis of the classical Gram-Schmidt orthogonalization process. (English)
Numer. Math. 101, No. 1, 87-100 (2005).
13
Krylov subspace methods for saddle point problems with indefinite preconditioning. (English)
SIAM J. Matrix Anal. Appl. 24, No.2, 368-391 (2002).
14
A framework for generalized conjugate gradient methods ‒ with special emphasis on contributions by Rüdiger Weiß. (English)
Appl. Numer. Math. 41, No.1, 7-22 (2002).
15
Least squares residuals and minimal residual methods. (English)
SIAM J. Sci. Comput. 23, No.5, 1503-1525 (2002).
16
Krylov subspace methods for saddle point problems with indefinite preconditioning (English)
SIAM J. Matrix Analysis Applications 24, No. 2, 368-391 (2002).
17
Primal vs. dual variable approach for mixed-hybrid finite element approximation of the potential fluid flow problem in porous media. (English)
Margenov, Svetozar (ed.) et al., Large-scale scientific computing. 3rd international conference, LSSC 2001, Sozopol, Bulgaria, June 6‒10, 2001. Revised papers. Berlin: Springer (ISBN 3-540-43043-1). Lect. Notes Comput. Sci. 2179, 417-424 (2001).
18
By how much can residual minimization accelerate the convergence of orthogonal residual methods? (English)
Numer. Algorithms 27, No.2, 189-213 (2001).
19
Residual smoothing techniques: Do they improve the limiting accuracy of iterative solvers? (English)
BIT 41, No.1, 86-114 (2001).
20
Result 1 to 20 from 29 total
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