Search Complexity Analysis

Search Complexity Analysis is a theoretical framework within algorithmic analysis that characterizes the efficiency of search operations in data structures, particularly probabilistic hierarchical structures like skip lists, by quantifying the expected number of comparisons and pointer traversals required to locate an element as a function of input size.

344

Publications

19K

Citations

682

Authors

349

Institutions

Publications per year

2017–2026

29

Authors

682

  1. RE
    Richard E. Korf

    University of California, Los Angeles, United States

    6 publications · 259 citations

    6

    h-index

  2. MT
    Mikkel Thorup

    AT&T (United States), United States

    5 publications · 328 citations

    5

    h-index

  3. AA
    Arne Andersson

    Uppsala University, Sweden

    5 publications · 164 citations

    5

    h-index

  4. WR
    Wheeler Ruml

    University of New Hampshire at Manchester, United States

    4 publications · 114 citations

    4

    h-index

  5. JS
    Jonathan Schaeffer

    University of Alberta, Canada

    4 publications · 326 citations

    4

    h-index

1–5 of 682

Venues

  1. SIAM Journal on Computing

    11 publications · 781 citations

    11

    h-index

  2. Journal of the ACM

    JACM · 12 publications · 1.2K citations

    11

    h-index

  3. Theoretical Computer Science

    9 publications · 418 citations

    9

    h-index

  4. Information Processing Letters

    8 publications · 551 citations

    8

    h-index

  5. Symposium on Discrete Algorithms

    Conference · 5 publications · 113 citations

    5

    h-index

1–5