Online Approximation Algorithms

Online Approximation Algorithms is an algorithmic method that belongs to the class of approximation algorithms, distinguished by its ability to compute solutions for optimization problems in an online setting where input is revealed incrementally and irrevocably, without knowledge of future requests, while providing performance guarantees relative to the optimal offline solution.

300

Publications

19.2K

Citations

698

Authors

316

Institutions

Publications per year

2017–2026

80

Authors

698

  1. EH
    Elad Hazan

    Princeton University, United States

    7 publications · 1.3K citations

    7

    h-index

  2. VM
    Vahab Mirrokni

    Google (United States), United States

    6 publications · 455 citations

    6

    h-index

  3. AG
    Anupam Gupta

    Carnegie Mellon University, United States

    6 publications · 159 citations

    6

    h-index

  4. LE
    Leah Epstein

    University of Haifa, Israel

    6 publications · 293 citations

    6

    h-index

  5. DS
    Danny Segev

    Tel Aviv University, Israel

    5 publications · 210 citations

    5

    h-index

1–5 of 698

Venues

  1. arXiv (Cornell University)

    Repository · 19 publications · 661 citations

    14

    h-index

  2. SIAM Journal on Computing

    10 publications · 351 citations

    10

    h-index

  3. Algorithmica

    10 publications · 537 citations

    10

    h-index

  4. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)

    Repository · 9 publications · 230 citations

    9

    h-index

  5. ACM Transactions on Algorithms

    TALG · 8 publications · 303 citations

    8

    h-index

1–5