Randomized Subspace Iteration: Analysis of Canonical Angles and Unitarily Invariant Norms
From MaRDI portal
Publication:4615298
DOI10.1137/18M1179432zbMath1406.65024arXiv1804.02614WikidataQ128616167 ScholiaQ128616167MaRDI QIDQ4615298
Publication date: 4 February 2019
Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1804.02614
Related Items
Admissible subspaces and the subspace iteration method ⋮ Randomized Low-Rank Approximation of Monotone Matrix Functions ⋮ Randomized Discrete Empirical Interpolation Method for Nonlinear Model Reduction ⋮ A randomized singular value decomposition for third-order oriented tensors ⋮ Two-Level Nyström--Schur Preconditioner for Sparse Symmetric Positive Definite Matrices ⋮ Efficient Algorithms for Eigensystem Realization Using Randomized SVD ⋮ Bootstrapping the operator norm in high dimensions: error estimation for covariance matrices and sketching ⋮ Randomized Algorithms for Low-Rank Tensor Decompositions in the Tucker Format
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- Extension of Rotfel'd Theorem
- Das Verfahren der Treppeniteration und verwandte Verfahren zur Lösung algebraischer Eigenwertprobleme
- Accuracy of singular vectors obtained by projection-based SVD methods
- Randomized linear algebra for model reduction. I. Galerkin methods and error estimation
- Harmonic and refined extraction methods for the singular value problem, with applications in least squares problems
- A DEIM Induced CUR Factorization
- Schubert Varieties and Distances between Subspaces of Different Dimensions
- FEAST As A Subspace Iteration Eigensolver Accelerated By Approximate Spectral Projection
- Efficient Dimensionality Reduction for Canonical Correlation Analysis
- Numerical Methods for Large Eigenvalue Problems
- Randomized Algorithms for Matrices and Data
- Low-Rank Matrix Approximations Do Not Need a Singular Value Gap
- Smoothed Analysis of the Condition Numbers and Growth Factors of Matrices
- A randomized tensor singular value decomposition based on the t‐product
- Practical Sketching Algorithms for Low-Rank Matrix Approximation
- Numerical Methods for Computing Angles Between Linear Subspaces
- Subspace Iteration Randomization and Singular Value Problems
- Structural Convergence Results for Approximation of Dominant Subspaces from Block Krylov Spaces
- Conditioning of Leverage Scores and Computation by QR Decomposition