Output-sensitive Algorithms

Output-Sensitive Algorithms is an algorithmic paradigm in computational geometry that belongs to the class of algorithms whose runtime complexity is characterized not only by the size of the input but explicitly in addition by the size of the output, thereby distinguishing it from traditional algorithms that depend solely on input parameters.

97

Publications

3.8K

Citations

173

Authors

97

Institutions

Publications per year

2017–2026

6

Authors

173

  1. TM
    Timothy M. Chan

    University of Waterloo, Canada

    5 publications · 241 citations

    5

    h-index

  2. MO
    M.H. Overmars

    Utrecht University, Netherlands

    4 publications · 56 citations

    4

    h-index

  3. MT
    Michael T. Goodrich

    Johns Hopkins University, United States

    4 publications · 92 citations

    4

    h-index

  4. SH
    Sariel Har-Peled

    University of Illinois Urbana-Champaign, United States

    3 publications · 62 citations

    3

    h-index

  5. MK
    Michael Kerber

    Institute of Science and Technology Austria, Austria

    3 publications · 150 citations

    3

    h-index

1–5 of 173

Venues

  1. Symposium on Discrete Algorithms

    Conference · 5 publications · 112 citations

    5

    h-index

  2. SIAM Journal on Computing

    5 publications · 375 citations

    5

    h-index

  3. ACM Transactions on Graphics

    TOG · 4 publications · 343 citations

    4

    h-index

  4. Discrete & Computational Geometry

    4 publications · 356 citations

    4

    h-index

  5. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 3 publications · 108 citations

    3

    h-index

1–5