Mixed-integer Convex Optimization

Mixed-Integer Convex Optimization is a mathematical optimization framework that belongs to the class of integer programming problems, distinguished by the requirement that discrete variables may appear alongside continuous variables in a convex optimization problem, where the objective function and constraint set are convex when integer variables are fixed.

196

Publications

13.4K

Citations

580

Authors

281

Institutions

Publications per year

2017–2026

134

Variants

MicpMixed-integer Convex Programming

Authors

580

  1. OD
    Oscar Danilo Montoya

    Universidad Tecnológica de Bolívar, Colombia

    6 publications · 126 citations

    6

    h-index

  2. RT
    Russ Tedrake

    Massachusetts Institute of Technology, United States

    5 publications · 466 citations

    5

    h-index

  3. ZH
    Zhu Han

    University of Houston, United States

    4 publications · 186 citations

    4

    h-index

  4. WG
    Walter Gil-González

    Universidad Tecnológica de Bolívar, Colombia

    4 publications · 114 citations

    4

    h-index

  5. DW
    Derrick Wing Kwan Ng

    UNSW Sydney, Australia

    4 publications · 585 citations

    4

    h-index

1–5 of 580

Venues

  1. IEEE Transactions on Vehicular Technology

    10 publications · 725 citations

    10

    h-index

  2. IEEE Transactions on Wireless Communications

    10 publications · 2.6K citations

    10

    h-index

  3. Journal of Global Optimization

    9 publications · 521 citations

    9

    h-index

  4. IEEE Transactions on Power Systems

    8 publications · 879 citations

    8

    h-index

  5. IEEE Access

    7 publications · 308 citations

    7

    h-index

1–5