Sublinear-Time Spectral Algorithms is a class of algorithmic methods in theoretical computer science that, by operating in time strictly sublinear in the input size, approximate spectral properties—such as eigenvalues, eigenvectors, or graph expansion—of large matrices or graphs through localized or sampled computations.
5
Publications
189
Citations
15
Authors
14
Institutions
Publications per year
2017–2026
3
Parents
Publications
5
An SDP-based algorithm for linear-sized spectral sparsification
Yin Tat Lee, He Sun · 2017 · 70 citations
The Kikuchi Hierarchy and Tensor PCA
Alexander S. Wein, A. El Alaoui, Cristopher Moore · 2019 · 38 citations
Computing, Computer Science, Physics +14
Fully dynamic spectral vertex sparsifiers and applications
David Durfee, Yu Gao, Gramoz Goranci et al. · 2019 · 34 citations · Full text
Spectral Approaches to Nearest Neighbor Search
Amirali Abdullah, Alexandr Andoni, Ravindran Kannan et al. · 2014 · 13 citations
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Authors
15
- AA
1
h-index
- RP
1
h-index
- AC
1
h-index
- ASAlexander S. Wein
Courant Institute of Mathematical Sciences, United States
1 publications · 38 citations
1
h-index
- DD
1
h-index
1–5 of 15
Institutions
14
Microsoft (United States)
Redmond, United States
2 h-index
3 publications
96 citations
Stanford University
Stanford, United States
1 h-index
1 publications
38 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
1 h-index
2 publications
26 citations
Michigan State University
East Lansing, United States
1 h-index
2 publications
68 citations
Santa Fe Institute
Santa Fe, United States
1 h-index
1 publications
38 citations
University of Vienna
Vienna, Austria
1 h-index
1 publications
34 citations
Courant Institute of Mathematical Sciences
New York, United States
1 h-index
1 publications
38 citations
Statistical and Applied Mathematical Sciences Institute
Durham, United States
1 h-index
1 publications
34 citations
1–8 of 14
Venues
- Applied and Computational Harmonic Analysis
1 publications · 34 citations
1
h-index
1–1