Optimization Under Evaluation Budgets

Optimization Under Evaluation Budgets is a derivative-free optimization framework that belongs to the class of black-box optimization methods, distinguished by its explicit constraint on the number of function evaluations permitted during the search for an optimal solution.

9

Publications

259

Citations

31

Authors

24

Institutions

Publications per year

2017–2026

5

Variants

Budgeted Optimization

Authors

31

  1. VD
    Valentin Dalibard

    Google DeepMind (United Kingdom), United Kingdom

    1 publications · 19 citations

    1

    h-index

  2. MR
    Muhammad Rizwan Tanweer

    Nanyang Technological University, Singapore

    1 publications · 11 citations

    1

    h-index

  3. TK
    Tipaluck Krityakierne

    Mahidol University, Thailand

    1 publications · 30 citations

    1

    h-index

  4. TS
    Tom Schaul

    Google DeepMind (United Kingdom), United Kingdom

    1 publications · 19 citations

    1

    h-index

  5. MV
    Michal Valko

    Google DeepMind (United Kingdom), United Kingdom

    1 publications · 16 citations

    1

    h-index

1–5 of 31

Venues

  1. SIAM Journal on Optimization

    1 publications · 38 citations

    1

    h-index

  2. Hydrology and earth system sciences

    1 publications · 30 citations

    1

    h-index

  3. IEEE Transactions on Evolutionary Computation

    1 publications · 16 citations

    1

    h-index

  4. Journal of Physics Conference Series

    1 publications · 43 citations

    1

    h-index

  5. arXiv (Cornell University)

    Repository · 1 publications · 16 citations

    1

    h-index

1–5