A Krylov-Schur approach to the truncated SVD
From MaRDI portal
Publication:413526
DOI10.1016/j.laa.2011.07.022zbMath1241.65040OpenAlexW2094371111MaRDI QIDQ413526
Publication date: 7 May 2012
Published in: Linear Algebra and its Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.laa.2011.07.022
numerical experimentsKrylov subspacesLanczos bidiagonalizationsingular value decomposition (SVD)Krylov-Schur methodtruncated SVD
Related Items
On Approximate Reduction of Multi-Port Resistor Networks ⋮ An implicitly restarted block Lanczos bidiagonalization method using Leja shifts ⋮ An efficient, memory-saving approach for the Loewner framework ⋮ A cryptosystem for lossless/lossy grayscale images in IWT domain using chaotic map-based generated key matrices ⋮ Low-Rank Solution of Unsteady Diffusion Equations with Stochastic Coefficients ⋮ Krylov--Schur-Type Restarts for the Two-Sided Arnoldi Method ⋮ A literature survey of matrix methods for data science ⋮ GCV for Tikhonov regularization by partial SVD ⋮ Hybrid iterative refined restarted Lanczos bidiagonalization methods ⋮ Probabilistic upper bounds for the matrix two-norm ⋮ On the convergence of Krylov methods with low-rank truncations ⋮ An implicitly restarted Lanczos bidiagonalization method with refined harmonic shifts for computing smallest singular triplets ⋮ A model reduction approach for inverse problems with operator valued data ⋮ On Inner Iterations of Jacobi--Davidson Type Methods for Large SVD Computations
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A Hamiltonian Krylov-Schur-type method based on the symplectic Lanczos process
- Solving large-scale quadratic eigenvalue problems with Hamiltonian eigenstructure using a structure-preserving Krylov subspace method
- A robust and efficient parallel SVD solver based on restarted Lanczos bidiagonalization
- A new look at the Lanczos algorithm for solving symmetric systems of linear equations
- An implicit restarted Lanczos method for large symmetric eigenvalue problems
- An implicitly restarted symplectic Lanczos method for the Hamiltonian eigenvalue problem
- Computing smallest singular triplets with implicitly restarted Lanczos bidiagonalization
- A periodic Krylov-Schur algorithm for large matrix products
- 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
- A Krylov--Schur Algorithm for Large Eigenproblems
- Numerical methods for large eigenvalue problems
- The Effect of Aggressive Early Deflation on the Convergence of the QR Algorithm
- LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares
- Implicit Application of Polynomial Filters in a k-Step Arnoldi Method
- Solution of Sparse Indefinite Systems of Linear Equations
- ARPACK Users' Guide
- An Implicitly Restarted Refined Bidiagonalization Lanczos Method for Computing a Partial Singular Value Decomposition
- Low-Rank Matrix Approximation Using the Lanczos Bidiagonalization Process with Applications
- Deflation Techniques for an Implicitly Restarted Arnoldi Iteration
- A Jacobi–Davidson Iteration Method for Linear Eigenvalue Problems
- Convergence of Polynomial Restart Krylov Methods for Eigenvalue Computations
- Calculating the Singular Values and Pseudo-Inverse of a Matrix
- Augmented Implicitly Restarted Lanczos Bidiagonalization Methods
This page was built for publication: A Krylov-Schur approach to the truncated SVD
