Maximum Cut Approximation

Maximum Cut Approximation is an algorithmic method in combinatorial optimization that achieves a performance guarantee for the Maximum Cut problem by computing a solution whose cut size approximates the optimal cut within a proven factor.

216

Publications

19.9K

Citations

422

Authors

242

Institutions

Publications per year

2017–2026

30

Authors

422

  1. DR
    David R. Karger

    Massachusetts Institute of Technology, United States

    7 publications · 1.2K citations

    7

    h-index

  2. DP
    David P. Williamson

    Cornell University, United States

    6 publications · 4.4K citations

    6

    h-index

  3. LT
    Luca Trevisan

    University of California, Berkeley, United States

    6 publications · 504 citations

    6

    h-index

  4. SK
    Subhash Khot

    Georgia Institute of Technology, United States

    5 publications · 867 citations

    5

    h-index

  5. MS
    Madhu Sudan

    Massachusetts Institute of Technology, United States

    5 publications · 775 citations

    5

    h-index

1–5 of 422

Venues

  1. Mathematical Programming

    12 publications · 529 citations

    12

    h-index

  2. Operations Research Letters

    9 publications · 465 citations

    9

    h-index

  3. SIAM Journal on Computing

    9 publications · 2.1K citations

    9

    h-index

  4. arXiv (Cornell University)

    Repository · 6 publications · 93 citations

    6

    h-index

  5. Journal of the ACM

    JACM · 5 publications · 5.1K citations

    5

    h-index

1–5