Computation of the fundamental singular subspace of a large matrix

DOI10.1016/S0024-3795(96)00040-7zbMath0874.65028OpenAlexW2059782748MaRDI QIDQ1355206

O. Diekmann

Publication date: 19 May 1997

Published in: Linear Algebra and its Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0024-3795(96)00040-7

zbMATH Keywords

Davidson methodblock Lanczos methodlarge matrixfundamental singular subspacesmallest singular values

Mathematics Subject Classification ID

Numerical computation of eigenvalues and eigenvectors of matrices (65F15)

Related Items (3)

A Preconditioned Hybrid SVD Method for Accurately Computing Singular Triplets of Large Matrices ⋮ PRIMME_SVDS: A High-Performance Preconditioned SVD Solver for Accurate Large-Scale Computations ⋮ Computation of pseudospectra via spectral projectors

Uses Software

• Harwell-Boeing sparse matrix collection

Cites Work

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• Preconditioning techniques for nonsymmetric and indefinite linear systems
• A new look at the Lanczos algorithm for solving symmetric systems of linear equations
• Iterative algorithms for computing the singular subspace of a matrix associated with its smallest singular values
• Estimating the largest singular values of large sparse matrices via modified moments
• The iterative calculation of a few of the lowest eigenvalues and corresponding eigenvectors of large real-symmetric matrices
• Generalizations of Davidson’s Method for Computing Eigenvalues of Sparse Symmetric Matrices
• Sparse matrix test problems
• On the Rates of Convergence of the Lanczos and the Block-Lanczos Methods
• A Block Lanczos Method for Computing the Singular Values and Corresponding Singular Vectors of a Matrix
• The Lanczos Algorithm with Selective Orthogonalization
• The Davidson Method

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