Sublinear-time Linear Algebra
Sublinear-Time Linear Algebra is a computational framework within the field of sublinear algorithms that enables the approximation of fundamental linear algebra operations—such as matrix multiplication, norm estimation, and low-rank approximation—in time sublinear in the size of the input matrices, thereby distinguishing itself from classical linear algebra algorithms by prioritizing computational efficiency over exact computation.
8
Publications
672
Citations
9
Authors
8
Institutions
Publications per year
2017–2026
4
Parents
8
Sublinear time algorithms for metric space problems
Piotr Indyk · 1999 · 202 citations
Solving linear programs in the current matrix multiplication time
Michael B. Cohen, Yin Tat Lee, Zhao Song · 2019 · 154 citations
Solving Linear Programs in the Current Matrix Multiplication Time
Michael B. Cohen, Yin Tat Lee, Zhao Song · Journal of the ACM · 2021 · 132 citations · Full text
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The University of Texas at Austin
Austin, United States
3 h-index
3 publications
307 citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
3 publications
307 citations
University of Washington
Seattle, United States
3 h-index
4 publications
461 citations
Google (United States)
Mountain View, United States
1 h-index
2 publications
44 citations
University of Bonn
Bonn, Germany
1 h-index
1 publications
26 citations
Carnegie Mellon University
Pittsburgh, United States
1 h-index
2 publications
20 citations
Stanford University
Stanford, United States
1 h-index
1 publications
202 citations
Microsoft (United States)
Redmond, United States
1 h-index
1 publications
154 citations
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Repository · 3 publications · 53 citations
3
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JACM · 1 publications · 132 citations
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CC · 1 publications · 26 citations
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1 publications · 105 citations
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