Randomized Geometric Algorithms

Randomized Geometric Algorithms is a class of algorithms in computational geometry that solve problems on geometric objects by incorporating random choices into their design or execution to obtain provable performance guarantees—typically on expected running time or simplicity of analysis—that are superior or more robust than those of comparable deterministic geometric algorithms.

1K

Publications

56.4K

Citations

1.6K

Authors

650

Institutions

Publications per year

2017–2026

82

Authors

1.6K

  1. MS
    Micha Sharir

    Tel Aviv University, Israel

    34 publications · 1.7K citations

    21

    h-index

  2. PK
    Pankaj K. Agarwal

    Duke University, United States

    22 publications · 1.3K citations

    19

    h-index

  3. TM
    Timothy M. Chan

    University of Waterloo, Canada

    17 publications · 961 citations

    13

    h-index

  4. JS
    Joseph S. B. Mitchell

    Stony Brook University, United States

    12 publications · 372 citations

    12

    h-index

  5. BC
    Bernard Chazelle

    Princeton University, United States

    11 publications · 621 citations

    11

    h-index

1–5 of 1.6K

Venues

  1. SIAM Journal on Computing

    52 publications · 4.1K citations

    32

    h-index

  2. Discrete & Computational Geometry

    50 publications · 2.4K citations

    27

    h-index

  3. Information Processing Letters

    46 publications · 2.6K citations

    24

    h-index

  4. Journal of Algorithms

    25 publications · 2.2K citations

    23

    h-index

  5. Algorithmica

    37 publications · 1.7K citations

    23

    h-index

1–5