Randomized Complexity Theory
Randomized Complexity Theory is a branch of computational complexity theory that studies the classification of computational problems according to their inherent difficulty under models of computation that allow randomized algorithms, distinguished by its focus on probabilistic Turing machines and complexity classes such as BPP, RP, and ZPP.
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15.9K
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386
Authors
195
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2017–2026
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The space complexity of approximating the frequency moments
Noga Alon, Yossi Matias, Márió Szegedy · 1996 · 847 citations
Trading group theory for randomness
László Babai · 1985 · 782 citations
Adi Shamir · Journal of the ACM · 1992 · 657 citations · Full text
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Princeton University
Princeton, United States
11 h-index
18 publications
768 citations

Weizmann Institute of Science
Rehovot, Israel
10 h-index
10 publications
939 citations

Tel Aviv University
Tel Aviv, Israel
10 h-index
10 publications
1.2K citations
Massachusetts Institute of Technology
Cambridge, United States
10 h-index
17 publications
809 citations
University of California, Berkeley
Berkeley, United States
10 h-index
11 publications
647 citations
ETH Zurich
Zurich, Switzerland
10 h-index
13 publications
864 citations
University of Washington
Seattle, United States
9 h-index
16 publications
577 citations
University of Chicago
Chicago, United States
9 h-index
11 publications
1.7K citations
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27 publications · 2K citations
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Repository · 11 publications · 312 citations
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9 publications · 198 citations
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JACM · 7 publications · 1.1K citations
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7 publications · 2.4K citations
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