Randomized Time Complexity

Randomized Time Complexity is a computational complexity measure that characterizes the asymptotic time required by a randomized algorithm to solve a problem, distinguished from deterministic time complexity by incorporating probabilistic transitions and expected or high-probability performance bounds over random choices made during execution.

55

Publications

2.7K

Citations

107

Authors

72

Institutions

Publications per year

2017–2026

6

Authors

107

  1. KE
    Klim Efremenko

    Carnegie Mellon University, United States

    2 publications · 38 citations

    2

    h-index

  2. EC
    Edith Cohen

    AT&T (United States), United States

    2 publications · 73 citations

    2

    h-index

  3. JC
    Joseph Cheriyan

    University of Waterloo, Canada

    2 publications · 40 citations

    2

    h-index

  4. DR
    Dariusz R. Kowalski

    University of Liverpool, United Kingdom

    2 publications · 101 citations

    2

    h-index

  5. AP
    Andrzej Pelc

    Université du Québec en Outaouais, Canada

    2 publications · 101 citations

    2

    h-index

1–5 of 107

Venues

  1. SIAM Journal on Computing

    8 publications · 296 citations

    8

    h-index

  2. Journal of Computer and System Sciences

    2 publications · 486 citations

    2

    h-index

  3. Journal of Complexity

    2 publications · 60 citations

    2

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 1 publications · 11 citations

    1

    h-index

  5. 1

    h-index

1–5