\texttt{pylspack}: parallel algorithms and data structures for sketching, column subset selection, regression, and leverage scores
From MaRDI portal
Publication:6599985
DOI10.1145/3555370MaRDI QIDQ6599985
Aleksandros Sobczyk, Efstratios Gallopoulos
Publication date: 6 September 2024
Published in: ACM Transactions on Mathematical Software (Search for Journal in Brave)
sketchingpreconditioningparallel algorithmsregressionsparse data structurescolumn subset selectionstatistical leverage scores
Cites Work
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- Faster least squares approximation
- Fast dimension reduction using Rademacher series on dual BCH codes
- Column subset selection is NP-complete
- Column subset selection problem is UG-hard
- Iterative Hessian sketch: Fast and accurate solution approximation for constrained least-squares
- Exploiting Multiple Levels of Parallelism in Sparse Matrix-Matrix Multiplication
- LSRN: A Parallel Iterative Solver for Strongly Over- or Underdetermined Systems
- Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
- Computational Advertising: Techniques for Targeting Relevant Ads
- Dimensionality Reduction for k-Means Clustering and Low Rank Approximation
- Fast sparse matrix multiplication
- Newton Sketch: A Near Linear-Time Optimization Algorithm with Linear-Quadratic Convergence
- Uniform Sampling for Matrix Approximation
- A fast randomized algorithm for overdetermined linear least-squares regression
- IMPROVED ANALYSIS OF THE SUBSAMPLED RANDOMIZED HADAMARD TRANSFORM
- Blendenpik: Supercharging LAPACK's Least-Squares Solver
- Low-Rank Approximation and Regression in Input Sparsity Time
- Sparser Johnson-Lindenstrauss Transforms
- Extensions of Lipschitz mappings into a Hilbert space
- Direct Methods for Sparse Linear Systems
- Two Fast Algorithms for Sparse Matrices: Multiplication and Permuted Transposition
- A simple method for generating gamma variables
- Matrix rank certification
- Nearly Tight Oblivious Subspace Embeddings by Trace Inequalities
- Input Sparsity Time Low-rank Approximation via Ridge Leverage Score Sampling
- SparseX
- Faster Kernel Ridge Regression Using Sketching and Preconditioning
- High-Dimensional Probability
- Estimating Leverage Scores via Rank Revealing Methods and Randomization
- Limits on All Known (and Some Unknown) Approaches to Matrix Multiplication
- <scp>Ginkgo</scp> : A Modern Linear Operator Algebra Framework for High Performance Computing
- Randomized Linear Algebra Approaches to Estimate the von Neumann Entropy of Density Matrices
- On fast multiplication of a matrix by its transpose
- Packing LPs are Hard to Solve Accurately, Assuming Linear Equations are Hard
- Fast matrix rank algorithms and applications
- Faster Subset Selection for Matrices and Applications
- An overview of the sparse basic linear algebra subprograms
- Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression
- Sparsity lower bounds for dimensionality reducing maps
- Randomized numerical linear algebra: Foundations and algorithms
- Hutch++: Optimal Stochastic Trace Estimation
- Near-optimal algorithms for linear algebra in the current matrix multiplication time
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