Parallel Selection Algorithms

Parallel Selection Algorithms is a computational method within parallel complexity theory that identifies the k-th smallest element in an unordered dataset by concurrently executing selection and comparison operations across multiple processors to reduce time complexity relative to sequential approaches.

75

Publications

3.6K

Citations

218

Authors

108

Institutions

Publications per year

2017–2026

18

Authors

218

  1. LJ
    L. Jeff Hong

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

    4 publications · 189 citations

    4

    h-index

  2. SR
    Susan R. Hunter

    Purdue University West Lafayette, United States

    4 publications · 53 citations

    4

    h-index

  3. YZ
    Ying Zhong

    Second Affiliated Hospital of Chengdu University of Traditional Chinese, China

    3 publications · 78 citations

    3

    h-index

  4. BL
    Barry L. Nelson

    Northwestern University, United States

    3 publications · 138 citations

    3

    h-index

  5. SW
    S. W. Song

    Universidade de São Paulo, Brazil

    2 publications · 26 citations

    2

    h-index

1–5 of 218

Venues

  1. Operations Research

    3 publications · 166 citations

    3

    h-index

  2. Applied Soft Computing

    2 publications · 44 citations

    2

    h-index

  3. SIAM Journal on Computing

    2 publications · 437 citations

    2

    h-index

  4. INFORMS journal on computing

    2 publications · 40 citations

    2

    h-index

  5. Applied Intelligence

    2 publications · 73 citations

    2

    h-index

1–5