Smoothed Complexity

Smoothed Complexity is a computational complexity measure that belongs to the class of hybrid analysis frameworks, differing from worst-case and average-case analyses by evaluating the expected performance of algorithms under slight random perturbations of worst-case inputs.

20

Publications

1.2K

Citations

39

Authors

25

Institutions

Publications per year

2017–2026

1

Authors

39

  1. ST
    Shang‐Hua Teng

    University of Southern California, United States

    6 publications · 714 citations

    6

    h-index

  2. DA
    Daniel A. Spielman

    Yale University, United States

    3 publications · 100 citations

    3

    h-index

  3. HR
    Heiko Röglin

    University of Bonn, Germany

    3 publications · 155 citations

    3

    h-index

  4. DA
    David Arthur

    Stanford University, United States

    2 publications · 119 citations

    2

    h-index

  5. BM
    Bodo Manthey

    University of Twente, Netherlands

    2 publications · 119 citations

    2

    h-index

1–5 of 39

Venues

  1. arXiv (Cornell University)

    Repository · 2 publications · 85 citations

    2

    h-index

  2. Evolutionary Computation

    1 publications · 27 citations

    1

    h-index

  3. SIAM Journal on Computing

    1 publications · 58 citations

    1

    h-index

  4. Mathematical Programming

    1 publications · 20 citations

    1

    h-index

  5. ACM Transactions on Algorithms

    TALG · 1 publications · 13 citations

    1

    h-index

1–5