Probabilistic Analysis Of Algorithms

Probabilistic Analysis of Algorithms is the systematic evaluation of algorithmic performance under probabilistic assumptions about inputs or internal operations, distinguishing it from deterministic worst-case or average-case analyses by modeling uncertainty through stochastic processes.

209

Publications

17.9K

Citations

437

Authors

245

Institutions

Publications per year

2017–2026

25

Authors

437

  1. DA
    Daniel A. Spielman

    Yale University, United States

    4 publications · 1.1K citations

    4

    h-index

  2. ST
    Shang‐Hua Teng

    University of Southern California, United States

    4 publications · 1.1K citations

    4

    h-index

  3. OG
    Oded Goldreich

    Weizmann Institute of Science, Israel

    3 publications · 73 citations

    3

    h-index

  4. SM
    Shlomo Moran

    Technion – Israel Institute of Technology, Israel

    3 publications · 139 citations

    3

    h-index

  5. AW
    Avi Wigderson

    Hebrew University of Jerusalem, Israel

    3 publications · 259 citations

    3

    h-index

1–5 of 437

Venues

  1. Journal of Computer and System Sciences

    7 publications · 2.1K citations

    7

    h-index

  2. Information Processing Letters

    6 publications · 153 citations

    6

    h-index

  3. arXiv (Cornell University)

    Repository · 5 publications · 315 citations

    5

    h-index

  4. Journal of the ACM

    JACM · 4 publications · 2.7K citations

    4

    h-index

  5. Advances in Applied Probability

    AAP · 4 publications · 287 citations

    4

    h-index

1–5