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Near-Optimal Algorithms for Linear Algebra in the Current Matrix
  Multiplication Time
v1v2 (latest)

Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time

ACM-SIAM Symposium on Discrete Algorithms (SODA), 2021
16 July 2021
Nadiia Chepurko
K. Clarkson
Praneeth Kacham
David P. Woodruff
ArXiv (abs)PDFHTML

Papers citing "Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time"

4 / 4 papers shown
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched Preconditioning
Faster Linear Systems and Matrix Norm Approximation via Multi-level Sketched PreconditioningACM-SIAM Symposium on Discrete Algorithms (SODA), 2024
Michal Dereziñski
Christopher Musco
Jiaming Yang
437
8
0
09 May 2024
Distributed Least Squares in Small Space via Sketching and Bias
  Reduction
Distributed Least Squares in Small Space via Sketching and Bias ReductionNeural Information Processing Systems (NeurIPS), 2024
Sachin Garg
Kevin Tan
Michal Dereziñski
220
2
0
08 May 2024
Optimal Embedding Dimension for Sparse Subspace Embeddings
Optimal Embedding Dimension for Sparse Subspace Embeddings
Shabarish Chenakkod
Michal Dereziñski
Xiaoyu Dong
M. Rudelson
335
22
0
17 Nov 2023
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra
Quantum-Inspired Algorithms from Randomized Numerical Linear Algebra
Nadiia Chepurko
K. Clarkson
L. Horesh
Honghao Lin
David P. Woodruff
793
25
0
09 Nov 2020
1
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