Randomized Linear Algebra
Randomized Linear Algebra is a computational framework within numerical linear algebra that employs randomized algorithms to approximate matrix computations, distinguishing itself by leveraging probabilistic methods to achieve efficiency and scalability in large-scale problems.
205
Publications
13K
Citations
352
Authors
190
Institutions
Publications per year
2017–2026
76
205
Low rank approximation and regression in input sparsity time
Kenneth L. Clarkson, David P. Woodruff · 2013 · 395 citations
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190
Stanford University
Stanford, United States
11 h-index
13 publications
1.2K citations
Yale University
New Haven, United States
7 h-index
14 publications
3.8K citations

Cornell University
Ithaca, United States
6 h-index
8 publications
368 citations
University of California, Berkeley
Berkeley, United States
6 h-index
11 publications
421 citations
Rensselaer Polytechnic Institute
Troy, United States
6 h-index
8 publications
469 citations
Lehman College
New York, United States
6 h-index
6 publications
148 citations

Chinese Academy of Sciences
Beijing, China
6 h-index
11 publications
548 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
5 h-index
9 publications
377 citations
1–8 of 190
Repository · 25 publications · 772 citations
15
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10 publications · 524 citations
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9 publications · 1.5K citations
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5 publications · 161 citations
5
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4 publications · 196 citations
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