Derandomization

Derandomization is a theoretical framework in computational complexity that characterizes the systematic reduction or elimination of randomness in algorithms while preserving their correctness and efficiency, distinguishing it from alternative approaches by its focus on converting probabilistic computation into deterministic computation under resource constraints.

175

Publications

7.9K

Citations

279

Authors

135

Institutions

Publications per year

2017–2026

30

Authors

279

  1. AW
    Avi Wigderson

    Hebrew University of Jerusalem, Israel

    14 publications · 952 citations

    13

    h-index

  2. SV
    Salil Vadhan

    Harvard University Press, United States

    10 publications · 729 citations

    10

    h-index

  3. RI
    Russell Impagliazzo

    University of California San Diego, United States

    8 publications · 619 citations

    8

    h-index

  4. MS
    Madhu Sudan

    Massachusetts Institute of Technology, United States

    7 publications · 666 citations

    7

    h-index

  5. LT
    Luca Trevisan

    University of California, Berkeley, United States

    4 publications · 500 citations

    4

    h-index

1–5 of 279

Venues

  1. SIAM Journal on Computing

    12 publications · 521 citations

    12

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 81 citations

    6

    h-index

  3. Computational Complexity

    CC · 5 publications · 147 citations

    5

    h-index

  4. Journal of Computer and System Sciences

    3 publications · 721 citations

    3

    h-index

  5. Random Structures and Algorithms

    3 publications · 77 citations

    3

    h-index

1–5