Search Space Pruning

Search Space Pruning is a computational strategy within search and optimization methodologies that systematically reduces the set of possible solutions by eliminating infeasible or suboptimal regions of the search space, thereby improving efficiency without compromising the optimality or accuracy of the final outcome.

1.2K

Publications

66K

Citations

3.3K

Authors

981

Institutions

Publications per year

2017–2026

301

Authors

3.3K

  1. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    10 publications · 1.3K citations

    10

    h-index

  2. LC
    Lei Chen

    Hong Kong University of Science and Technology, Hong Kong SAR China

    9 publications · 622 citations

    9

    h-index

  3. PJ
    Peter J. Stuckey

    The University of Melbourne, Australia

    9 publications · 265 citations

    9

    h-index

  4. KA
    Karem A. Sakallah

    University of Michigan, United States

    9 publications · 2.2K citations

    9

    h-index

  5. JW
    Jianyong Wang

    Tsinghua University, China

    9 publications · 576 citations

    9

    h-index

1–5 of 3.3K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 42 publications · 2.8K citations

    29

    h-index

  2. arXiv (Cornell University)

    Repository · 35 publications · 828 citations

    18

    h-index

  3. Proceedings of the VLDB Endowment

    PVLDB · 19 publications · 1.9K citations

    16

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 14 publications · 431 citations

    12

    h-index

  5. National Conference on Artificial Intelligence

    Conference · 11 publications · 524 citations

    11

    h-index

1–5