Interpolation-based Optimization

Interpolation-Based Optimization is a derivative-free optimization method that constructs and iteratively updates a surrogate model by fitting interpolating functions to sampled function values, distinguishing itself through the exact matching of these values at observed points to guide the search for optimal solutions.

160

Publications

9.2K

Citations

412

Authors

223

Institutions

Publications per year

2017–2026

39

Authors

412

  1. SM
    Stefan M. Wild

    Argonne National Laboratory, United States

    5 publications · 439 citations

    5

    h-index

  2. MJ
    M. J. D. Powell

    University of Cambridge, United Kingdom

    5 publications · 1.5K citations

    5

    h-index

  3. AR
    Andrew R. Conn

    IBM (United States), United States

    3 publications · 268 citations

    3

    h-index

  4. KS
    Katya Scheinberg

    Lehigh University, United States

    3 publications · 307 citations

    3

    h-index

  5. RG
    Rommel G. Regis

    Saint Joseph's University, United States

    3 publications · 433 citations

    3

    h-index

1–5 of 412

Venues

  1. Optimization methods & software

    7 publications · 218 citations

    7

    h-index

  2. Applied Soft Computing

    5 publications · 217 citations

    5

    h-index

  3. 4

    h-index

  4. Mathematical Programming

    4 publications · 606 citations

    4

    h-index

  5. Mathematics of Computation

    MCOM · 3 publications · 71 citations

    3

    h-index

1–5