Sublinear Cost Low Rank Approximation via Subspace Sampling
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Publication:5014667
DOI10.1007/978-3-030-43120-4_9OpenAlexW3012415416MaRDI QIDQ5014667
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Publication date: 8 December 2021
Published in: Mathematical Aspects of Computer and Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-030-43120-4_9
Cites Work
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- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- New studies of randomized augmentation and additive preprocessing
- A fast randomized algorithm for the approximation of matrices
- On the existence and computation of rank-revealing LU factorizations
- Random multipliers numerically stabilize Gaussian and block Gaussian elimination: proofs and an extension to low-rank approximation
- Numerically safe Gaussian elimination with no pivoting
- Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
- IMPROVED ANALYSIS OF THE SUBSAMPLED RANDOMIZED HADAMARD TRANSFORM
- Randomized Algorithms for Matrices and Data
- Smoothed Analysis of the Condition Numbers and Growth Factors of Matrices
- Eigenvalues and Condition Numbers of Random Matrices
- Practical Sketching Algorithms for Low-Rank Matrix Approximation
- Literature survey on low rank approximation of matrices
- Numerical Methods in Matrix Computations
- Tails of Condition Number Distributions
- Condition Numbers of Gaussian Random Matrices
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