Smoothed Analysis

Smoothed Analysis is a method for analyzing algorithm performance that considers the expected behavior of an algorithm under small random perturbations of worst-case inputs, thereby interpolating between worst-case and average-case complexity.

108

Publications

7.6K

Citations

264

Authors

150

Institutions

Publications per year

2017–2026

16

Authors

264

  1. HR
    Heiko Röglin

    University of Bonn, Germany

    13 publications · 424 citations

    12

    h-index

  2. ST
    Shang‐Hua Teng

    University of Southern California, United States

    12 publications · 1.7K citations

    12

    h-index

  3. DA
    Daniel A. Spielman

    Yale University, United States

    11 publications · 1.7K citations

    11

    h-index

  4. BM
    Bodo Manthey

    University of Twente, Netherlands

    10 publications · 331 citations

    10

    h-index

  5. DA
    David Arthur

    Stanford University, United States

    6 publications · 321 citations

    6

    h-index

1–5 of 264

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 366 citations

    10

    h-index

  2. University of Twente Research Information

    Repository · 3 publications · 53 citations

    3

    h-index

  3. ACM Transactions on Algorithms

    TALG · 3 publications · 50 citations

    3

    h-index

  4. Mathematical Programming

    3 publications · 107 citations

    3

    h-index

  5. ArXiv.org

    Repository · 3 publications · 104 citations

    3

    h-index

1–5