Bounding Functions

Bounding Functions is a mathematical procedure in optimization that belongs to the class of node evaluation techniques within Branch and Bound algorithms, distinguished by its role in computing lower or upper bounds on the optimal solution of subproblems to prune non-promising branches in the search tree.

580

Publications

32.8K

Citations

1.1K

Authors

559

Institutions

Publications per year

2017–2026

64

Variants

Bound Functions

Authors

1.1K

  1. NI
    Nicholas I. M. Gould

    Rutherford Appleton Laboratory, United Kingdom

    4 publications · 330 citations

    4

    h-index

  2. RD
    Rina Dechter

    University of California, Irvine, United States

    4 publications · 154 citations

    4

    h-index

  3. HD
    Hanif D. Sherali

    Virginia Tech, United States

    4 publications · 65 citations

    4

    h-index

  4. MA
    Michael A. Henning

    University of Johannesburg, South Africa

    4 publications · 161 citations

    4

    h-index

  5. GF
    Gang Feng

    City University of Hong Kong, Hong Kong SAR China

    4 publications · 394 citations

    4

    h-index

1–5 of 1.1K

Venues

  1. European Journal of Operational Research

    17 publications · 973 citations

    15

    h-index

  2. Mathematical Programming

    13 publications · 642 citations

    13

    h-index

  3. IEEE Transactions on Automatic Control

    11 publications · 784 citations

    11

    h-index

  4. Journal of Global Optimization

    11 publications · 254 citations

    10

    h-index

  5. Discrete Applied Mathematics

    9 publications · 472 citations

    9

    h-index

1–5