Randomized Counting Algorithms

Randomized Counting Algorithms is a class of probabilistic algorithms designed to estimate the size of a solution space or the number of objects satisfying certain constraints by employing random sampling and statistical inference, distinguishing it from deterministic counting methods through its use of randomness to achieve efficiency and scalability in intractable counting problems.

79

Publications

3K

Citations

154

Authors

89

Institutions

Publications per year

2017–2026

8

Authors

154

  1. MD
    Martin Dyer

    University of Leeds, United Kingdom

    8 publications · 415 citations

    8

    h-index

  2. PL
    Pinyan Lu

    Microsoft Research Asia (China), China

    4 publications · 82 citations

    4

    h-index

  3. MC
    Mary Cryan

    University of Leeds, United Kingdom

    3 publications · 74 citations

    3

    h-index

  4. JL
    Jing‐Cheng Liu

    Hunan Normal University, China

    3 publications · 64 citations

    3

    h-index

  5. RM
    Raghu Meka

    The University of Texas at Austin, United States

    3 publications · 105 citations

    3

    h-index

1–5 of 154

Venues

  1. SIAM Journal on Computing

    5 publications · 379 citations

    5

    h-index

  2. 4

    h-index

  3. Theoretical Computer Science

    4 publications · 92 citations

    4

    h-index

  4. Random Structures and Algorithms

    2 publications · 32 citations

    2

    h-index

  5. Journal of Computer and System Sciences

    2 publications · 35 citations

    2

    h-index

1–5