A Preconditioned Hybrid SVD Method for Accurately Computing Singular Triplets of Large Matrices
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Publication:3447456
DOI10.1137/140979381zbMath1325.65055arXiv1408.5535OpenAlexW3098912714MaRDI QIDQ3447456
Lingfei Wu, Andreas Stathopoulos
Publication date: 30 October 2015
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1408.5535
Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Eigenvalues, singular values, and eigenvectors (15A18) Preconditioners for iterative methods (65F08)
Related Items (13)
Majorization bounds for SVD ⋮ Estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse ⋮ A compact heart iteration for low-rank approximations of large matrices ⋮ Two harmonic Jacobi-Davidson methods for computing a partial generalized singular value decomposition of a large matrix pair ⋮ A smallest singular value method for nonlinear eigenvalue problems ⋮ Computing the smallest singular triplets of a large matrix ⋮ TRPL+K: Thick-Restart Preconditioned Lanczos+K Method for Large Symmetric Eigenvalue Problems ⋮ A cross-product approach for low-rank approximations of large matrices ⋮ PRIMME_SVDS: A High-Performance Preconditioned SVD Solver for Accurate Large-Scale Computations ⋮ PHSVDS ⋮ On Inner Iterations of Jacobi--Davidson Type Methods for Large SVD Computations ⋮ A Golub--Kahan Davidson Method for Accurately Computing a Few Singular Triplets of Large Sparse Matrices ⋮ Filtrated Algebraic Subspace Clustering
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
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- PRIMME
- Chebyshev acceleration of iterative refinement
- Computing interior eigenvalues of large matrices
- Restarted block Lanczos bidiagonalization methods
- Computation of the fundamental singular subspace of a large matrix
- Refined iterative algorithms based on Arnoldi's process for large unsymmetric eigenproblems
- Computing smallest singular triplets with implicitly restarted Lanczos bidiagonalization
- Reorthogonalization for the Golub-Kahan-Lanczos bidiagonal reduction
- Computing singular values of large matrices with an inverse-free preconditioned Krylov subspace method
- An implicitly restarted block Lanczos bidiagonalization method using Leja shifts
- Harmonic and refined extraction methods for the singular value problem, with applications in least squares problems
- Thick-Restart Lanczos Method for Large Symmetric Eigenvalue Problems
- Matrix Algorithms
- A Jacobi--Davidson Type SVD Method
- The university of Florida sparse matrix collection
- A Refined Harmonic Lanczos Bidiagonalization Method and an Implicitly Restarted Algorithm for Computing the Smallest Singular Triplets of Large Matrices
- Improving the Accuracy of Computed Singular Values
- A Lanczos Algorithm for Computing Singular Values and Vectors of Large Matrices
- A Block Lanczos Method for Computing the Singular Values and Corresponding Singular Vectors of a Matrix
- ARPACK Users' Guide
- Dynamic Thick Restarting of the Davidson, and the Implicitly Restarted Arnoldi Methods
- A Robust Preconditioner with Low Memory Requirements for Large Sparse Least Squares Problems
- An Implicitly Restarted Refined Bidiagonalization Lanczos Method for Computing a Partial Singular Value Decomposition
- A Jacobi--Davidson Iteration Method for Linear Eigenvalue Problems
- An Inverse Free Preconditioned Krylov Subspace Method for Symmetric Generalized Eigenvalue Problems
- Approximate solutions and eigenvalue bounds from Krylov subspaces
- Harmonic projection methods for large non-symmetric eigenvalue problems
- Nearly Optimal Preconditioned Methods for Hermitian Eigenproblems under Limited Memory. Part I: Seeking One Eigenvalue
- Calculating the Singular Values and Pseudo-Inverse of a Matrix
- Augmented Implicitly Restarted Lanczos Bidiagonalization Methods
- The potential theory of several intervals and its applications
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