Accuracy of preconditioned CG-type methods for least squares problems.
DOI10.1016/S0898-1221(03)80009-3zbMath1056.65035MaRDI QIDQ1416375
Publication date: 14 December 2003
Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)
numerical examplespreconditioningincomplete orthogonal factorizationconjugate gradient-type methodFinite precision arithmeticincomplete Gram-Schmidt factorizationIncomplete modified Gram-Schmidt orthogonalizationLeast square problemsRounding error analysis
Numerical solutions to overdetermined systems, pseudoinverses (65F20) Numerical computation of matrix norms, conditioning, scaling (65F35) Orthogonalization in numerical linear algebra (65F25)
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- Row-ordering schemes for sparse Givens transformations. I. Bipartite graph model
- Iterative Lösung und Fehlerabschätzung in der Ausgleichsrechnung
- Incomplete block matrix factorization preconditioning methods. The ultimate answer?
- Convergence of a direct-iterative method for large-scale least-squares problems
- Householder reflections versus Givens rotations in sparse orthogonal decomposition
- Solution of sparse linear least squares problems using Givens rotations
- Comparison of two pivotal strategies in sparse plane rotations
- Orthogonal Reduction of Sparse Matrices to Upper Triangular Form Using Householder Transformations
- Practical Use of Polynomial Preconditionings for the Conjugate Gradient Method
- Predicting fill for sparse orthogonal factorization
- LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
- CIMGS: An Incomplete Orthogonal FactorizationPreconditioner
- Estimating the Attainable Accuracy of Recursively Computed Residual Methods
- Least Squares Computations by Givens Transformations Without Square Roots
- Analysis of preconditioners for conjugate gradients through distribution of eigenvalues
- Modified Cholesky Factorizations for Sparse Preconditioners
- Solving linear least squares problems by Gram-Schmidt orthogonalization
- Extensions and Applications of the Householder Algorithm for Solving Linear Least Squares Problems
- Calculating the Singular Values and Pseudo-Inverse of a Matrix
- Bidiagonalization of Matrices and Solution of Linear Equations
- Methods of conjugate gradients for solving linear systems
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