Computational Convexity

Computational Convexity is a systematic framework within convex geometry that integrates algorithmic methods and computational techniques to analyze, construct, and optimize convex sets, functions, and related structures, distinguished by its focus on the effective implementation and complexity of geometric reasoning in discrete and continuous domains.

77

Publications

3.7K

Citations

160

Authors

120

Institutions

Publications per year

2017–2026

4

Authors

160

  1. PG
    Peter Gritzmann

    Technical University of Munich, Germany

    3 publications · 307 citations

    3

    h-index

  2. LC
    Linlin Chen

    Jiangsu University, China

    2 publications · 125 citations

    2

    h-index

  3. KV
    Kasturi Varadarajan

    University of Iowa, United States

    2 publications · 34 citations

    2

    h-index

  4. JL
    Jacques‐Olivier Lachaud

    Université Savoie Mont Blanc, France

    2 publications · 103 citations

    2

    h-index

  5. BS
    Bernd Sturmfels

    University of California, Berkeley, United States

    2 publications · 262 citations

    2

    h-index

1–5 of 160

Venues

  1. Mathematical Programming

    9 publications · 427 citations

    9

    h-index

  2. Linear Algebra and its Applications

    3 publications · 138 citations

    3

    h-index

  3. Pattern Recognition

    2 publications · 191 citations

    2

    h-index

  4. Proceedings of the American Mathematical Society

    PROC · 2 publications · 23 citations

    2

    h-index

  5. Computers & Mathematics with Applications

    2 publications · 25 citations

    2

    h-index

1–5