Randomized Matrix Algorithms
Randomized Matrix Algorithms is a class of computational methods that employ randomness as a fundamental mechanism to achieve efficient approximation or decomposition of matrix operations, distinguishing itself from deterministic linear algebra techniques by leveraging probabilistic constructs to reduce complexity in high-dimensional settings.
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23.8K
Citations
503
Authors
233
Institutions
Publications per year
2017–2026
88
Parents
320
Matrix completion from a few entries
Raghunandan H. Keshavan, Sewoong Oh, Andrea Montanari · 2009 · 662 citations
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Stanford University
Stanford, United States
18 h-index
43 publications
7.7K citations
Rensselaer Polytechnic Institute
Troy, United States
11 h-index
18 publications
2.4K citations
IBM Research - Almaden
San Jose, United States
11 h-index
13 publications
986 citations
University of California, Berkeley
Berkeley, United States
11 h-index
21 publications
513 citations
Yale University
New Haven, United States
10 h-index
19 publications
4K citations
California Institute of Technology
Pasadena, United States
9 h-index
12 publications
471 citations
IBM (United States)
Armonk, United States
9 h-index
12 publications
1.6K citations

University of Waterloo
Waterloo, Canada
8 h-index
12 publications
395 citations
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22 publications · 3K citations
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Repository · 45 publications · 1.5K citations
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12 publications · 510 citations
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10 publications · 593 citations
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Repository · 6 publications · 248 citations
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