Approximation In Parallel Computation

Approximation in Parallel Computation is a theoretical framework in parallel complexity theory that characterizes the efficiency and accuracy of parallel algorithms designed to compute near-optimal solutions for problems where exact solutions are computationally infeasible under polylogarithmic time and polynomial processor constraints.

63

Publications

2.5K

Citations

125

Authors

79

Institutions

Publications per year

2017–2026

15

Authors

125

  1. UV
    Uzi Vishkin

    University of Maryland, College Park, United States

    4 publications · 489 citations

    4

    h-index

  2. EB
    Eric Balkanski

    Harvard University Press, United States

    3 publications · 63 citations

    3

    h-index

  3. YS
    Yaron Singer

    Harvard University Press, United States

    3 publications · 63 citations

    3

    h-index

  4. RJ
    Rahul Jain

    National University of Singapore, Singapore

    3 publications · 83 citations

    3

    h-index

  5. LP
    Lorant Porkolab

    Max Planck Institute for Informatics, Germany

    3 publications · 199 citations

    3

    h-index

1–5 of 125

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 106 citations

    6

    h-index

  2. Journal of Parallel and Distributed Computing

    2 publications · 48 citations

    2

    h-index

  3. SIAM Journal on Computing

    2 publications · 267 citations

    2

    h-index

  4. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 2 publications · 55 citations

    2

    h-index

  5. Journal of Algorithms

    2 publications · 156 citations

    2

    h-index

1–5