Probabilistic Number-theoretic Algorithms

Probabilistic Number-Theoretic Algorithms is a class of algorithms in computational number theory that employs probabilistic methods, such as random sampling or stochastic processes, to solve number-theoretic problems with a high likelihood of correctness, distinguishing it from deterministic number-theoretic algorithms by its inherent reliance on randomness and bounded error probability.

24

Publications

1.8K

Citations

39

Authors

26

Institutions

Publications per year

2017–2026

0

Authors

39

  1. CP
    Carl Pomerance

    University of Georgia, United States

    3 publications · 111 citations

    3

    h-index

  2. RL
    Renet Lovorn

    1 publications · 23 citations

    1

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  3. ST
    Simon Tavaré

    University of Cambridge, United Kingdom

    1 publications · 39 citations

    1

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  4. RP
    René Peralta

    National Institute of Standards and Technology, United States

    1 publications · 63 citations

    1

    h-index

  5. JK
    John Komlos

    Ludwig-Maximilians-Universität München, Germany

    1 publications · 35 citations

    1

    h-index

1–5 of 39

Venues

  1. Mathematics of Computation

    MCOM · 4 publications · 259 citations

    4

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  2. Journal of Algorithms

    2 publications · 34 citations

    2

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  3. Constraints

    1 publications · 71 citations

    1

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  4. Zurich Open Repository and Archive (University of Zurich)

    Repository · 1 publications · 39 citations

    1

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  5. Statistical Methodology

    1 publications · 35 citations

    1

    h-index

1–5