Approximation Algorithms For Network Flows

Approximation Algorithms for Network Flows is a computational methodology within algorithmic graph theory that belongs to the class of approximation algorithms and is specifically designed to efficiently compute near-optimal solutions for network flow problems where exact optimization is computationally intractable.

279

Publications

24K

Citations

493

Authors

249

Institutions

Publications per year

2017–2026

28

Authors

493

  1. AK
    Amit Kumar

    Indian Institute of Technology Delhi, India

    7 publications · 380 citations

    7

    h-index

  2. CC
    Chandra Chekuri

    University of Illinois Urbana-Champaign, United States

    7 publications · 348 citations

    7

    h-index

  3. ÉT
    Éva Tardos

    Cornell University, United States

    6 publications · 1K citations

    6

    h-index

  4. AG
    Anupam Gupta

    Carnegie Mellon University, United States

    6 publications · 360 citations

    6

    h-index

  5. SK
    Sanjeev Khanna

    University of Pennsylvania, United States

    6 publications · 246 citations

    6

    h-index

1–5 of 493

Venues

  1. SIAM Journal on Computing

    17 publications · 1.2K citations

    15

    h-index

  2. Networks

    10 publications · 366 citations

    10

    h-index

  3. Mathematics of Operations Research

    9 publications · 1.3K citations

    9

    h-index

  4. Journal of the ACM

    JACM · 8 publications · 3.6K citations

    8

    h-index

  5. Mathematical Programming

    6 publications · 281 citations

    6

    h-index

1–5