Average-case Complexity

Average-Case Complexity is a computational complexity measure that characterizes the expected resource consumption of an algorithm over all possible inputs of a given size, under a specified probability distribution, distinguishing it from worst-case and best-case complexity measures by focusing on typical rather than extremal input behavior.

237

Publications

11.3K

Citations

393

Authors

214

Institutions

Publications per year

2017–2026

28

Authors

393

  1. LT
    Luca Trevisan

    University of California, Berkeley, United States

    6 publications · 476 citations

    6

    h-index

  2. HW
    H. Woźniakowski

    University of Warsaw, Poland

    6 publications · 421 citations

    6

    h-index

  3. PW
    Peter W. Shor

    AT&T (United States), United States

    5 publications · 322 citations

    5

    h-index

  4. OG
    Oded Goldreich

    Weizmann Institute of Science, Israel

    5 publications · 283 citations

    5

    h-index

  5. JC
    Jin‐Yi Cai

    University of Wisconsin–Madison, United States

    4 publications · 116 citations

    4

    h-index

1–5 of 393

Venues

  1. SIAM Journal on Computing

    12 publications · 743 citations

    11

    h-index

  2. Journal of Complexity

    12 publications · 421 citations

    11

    h-index

  3. arXiv (Cornell University)

    Repository · 11 publications · 506 citations

    10

    h-index

  4. Theoretical Computer Science

    4 publications · 58 citations

    4

    h-index

  5. Information Processing Letters

    4 publications · 123 citations

    4

    h-index

1–5