Randomized Space Complexity

Randomized Space Complexity is a complexity measure in computational theory that characterizes the minimum amount of memory space required by a probabilistic Turing machine to solve a decision problem with bounded error probability.

41

Publications

5K

Citations

76

Authors

52

Institutions

Publications per year

2017–2026

2

Authors

76

  1. YM
    Yossi Matias

    Google (United States), United States

    4 publications · 2.6K citations

    4

    h-index

  2. DP
    David P. Woodruff

    IBM Research - Almaden, United States

    3 publications · 78 citations

    3

    h-index

  3. SC
    Saar Cohen

    Johns Hopkins University Applied Physics Laboratory, United States

    2 publications · 532 citations

    2

    h-index

  4. MP
    Mert Pilancı

    University of California, Berkeley, United States

    2 publications · 166 citations

    2

    h-index

  5. VK
    Valerie King

    University of Victoria, Canada

    2 publications · 107 citations

    2

    h-index

1–5 of 76

Venues

  1. Journal of Computer and System Sciences

    4 publications · 1.9K citations

    4

    h-index

  2. SIAM Journal on Computing

    3 publications · 238 citations

    3

    h-index

  3. Theoretical Computer Science

    2 publications · 35 citations

    2

    h-index

  4. arXiv (Cornell University)

    Repository · 2 publications · 47 citations

    2

    h-index

  5. Information Processing Letters

    2 publications · 110 citations

    2

    h-index

1–5