Randomized Geometric Optimization

Randomized Geometric Optimization is a computational framework within randomized geometric algorithms that employs probabilistic methods to efficiently solve optimization problems involving geometric structures by iteratively refining candidate solutions through random sampling and geometric partitioning.

13

Publications

545

Citations

27

Authors

23

Institutions

Publications per year

2017–2026

1

Authors

27

  1. PK
    Pankaj K. Agarwal

    Duke University, United States

    3 publications · 119 citations

    3

    h-index

  2. MS
    Micha Sharir

    Tel Aviv University, Israel

    2 publications · 104 citations

    2

    h-index

  3. AS
    Arik Sityon

    Stony Brook University, United States

    2 publications · 38 citations

    2

    h-index

  4. JS
    Joseph S. B. Mitchell

    Stony Brook University, United States

    2 publications · 38 citations

    2

    h-index

  5. OH
    Olaf Hall-Holt

    Stanford University, United States

    2 publications · 38 citations

    2

    h-index

1–5 of 27

Venues

  1. Discrete & Computational Geometry

    2 publications · 189 citations

    2

    h-index

  2. Pattern Recognition

    1 publications · 16 citations

    1

    h-index

  3. Mathematical Programming

    1 publications · 140 citations

    1

    h-index

  4. 1

    h-index

  5. Canadian Conference on Computational Geometry

    Conference · 1 publications · 12 citations

    1

    h-index

1–5