Probabilistic Approximation Algorithms

Probabilistic Approximation Algorithms is an approximation algorithm that belongs to the class of randomized algorithms, distinguished by its use of randomness in the computational process to achieve an approximate solution with provable performance guarantees in expectation or with high probability.

237

Publications

21.1K

Citations

466

Authors

268

Institutions

Publications per year

2017–2026

37

Authors

466

  1. PM
    Per‐Gunnar Martinsson

    University of Colorado Boulder, United States

    6 publications · 4.5K citations

    6

    h-index

  2. JA
    Joel A. Tropp

    California Institute of Technology, United States

    5 publications · 4.5K citations

    5

    h-index

  3. EM
    Enrique Miranda

    Universidad de Oviedo, Spain

    4 publications · 95 citations

    4

    h-index

  4. RD
    Rina Dechter

    University of California, Irvine, United States

    4 publications · 154 citations

    4

    h-index

  5. ML
    Michael Luby

    International Computer Science Institute, United States

    3 publications · 1.1K citations

    3

    h-index

1–5 of 466

Venues

  1. arXiv (Cornell University)

    Repository · 15 publications · 490 citations

    12

    h-index

  2. Journal of Approximation Theory

    6 publications · 225 citations

    6

    h-index

  3. Journal of Complexity

    4 publications · 71 citations

    4

    h-index

  4. SIAM Journal on Computing

    4 publications · 1.1K citations

    4

    h-index

  5. International Journal of Approximate Reasoning

    4 publications · 653 citations

    4

    h-index

1–5