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.

266

Publications

15.9K

Citations

386

Authors

195

Institutions

Publications per year

2017–2026

24

Authors

386

  1. AW
    Avi Wigderson

    Hebrew University of Jerusalem, Israel

    10 publications · 410 citations

    10

    h-index

  2. SP
    Seth Pettie

    University of Michigan, United States

    8 publications · 538 citations

    8

    h-index

  3. CL
    Carsten Lund

    AT&T (United States), United States

    6 publications · 117 citations

    6

    h-index

  4. LT
    Luca Trevisan

    University of California, Berkeley, United States

    6 publications · 615 citations

    6

    h-index

  5. HK
    Hartmut Klauck

    Centre for Quantum Technologies, Singapore

    6 publications · 281 citations

    6

    h-index

1–5 of 386

Venues

  1. SIAM Journal on Computing

    27 publications · 2K citations

    19

    h-index

  2. arXiv (Cornell University)

    Repository · 11 publications · 312 citations

    10

    h-index

  3. Theoretical Computer Science

    9 publications · 198 citations

    9

    h-index

  4. Journal of the ACM

    JACM · 7 publications · 1.1K citations

    7

    h-index

  5. Journal of Computer and System Sciences

    7 publications · 2.4K citations

    7

    h-index

1–5