Approximate Optimization

Approximate Optimization is a computational strategy within the domain of bounded rationality that, as a method, prescribes the use of simplified models or heuristics to yield solutions that are sufficiently close to the optimal outcome when exact optimization is infeasible due to cognitive, temporal, or informational constraints.

736

Publications

47.3K

Citations

1.9K

Authors

721

Institutions

Publications per year

2017–2026

340

Authors

1.9K

  1. SK
    Satoshi KITAYAMA

    Kanazawa University, Japan

    13 publications · 588 citations

    12

    h-index

  2. RS
    Ruslan Shaydulin

    Argonne National Laboratory, United States

    15 publications · 626 citations

    12

    h-index

  3. JE
    John E. Renaud

    University of Notre Dame, United States

    11 publications · 528 citations

    10

    h-index

  4. YA
    Yuri Alexeev

    Argonne National Laboratory, United States

    10 publications · 384 citations

    10

    h-index

  5. EF
    Edward Farhi

    Massachusetts Institute of Technology, United States

    9 publications · 952 citations

    9

    h-index

1–5 of 1.9K

Venues

  1. arXiv (Cornell University)

    Repository · 57 publications · 2.7K citations

    28

    h-index

  2. Physical review. A/Physical review, A

    PRA · 24 publications · 1.3K citations

    19

    h-index

  3. Structural and Multidisciplinary Optimization

    19 publications · 1.8K citations

    17

    h-index

  4. Quantum

    15 publications · 1.2K citations

    13

    h-index

  5. Physical Review Research

    12 publications · 537 citations

    12

    h-index

1–5