Sublinear-time Optimization

Sublinear-Time Optimization is an algorithmic framework that belongs to the class of optimization methods and is distinguished by its ability to compute approximate solutions to optimization problems in time strictly sublinear in the input size, typically by exploiting sampling, sketching, or local search strategies.

60

Publications

4.6K

Citations

133

Authors

86

Institutions

Publications per year

2017–2026

14

Authors

133

  1. EH
    Elad Hazan

    Princeton University, United States

    5 publications · 204 citations

    5

    h-index

  2. PI
    Piotr Indyk

    Massachusetts Institute of Technology, United States

    3 publications · 340 citations

    3

    h-index

  3. RR
    Ronitt Rubinfeld

    Massachusetts Institute of Technology, United States

    3 publications · 107 citations

    3

    h-index

  4. RS
    Roy Schwartz

    Technion – Israel Institute of Technology, Israel

    2 publications · 467 citations

    2

    h-index

  5. ZW
    Zhuang Wang

    Sichuan University, China

    2 publications · 43 citations

    2

    h-index

1–5 of 133

Venues

  1. SIAM Journal on Computing

    4 publications · 469 citations

    4

    h-index

  2. Algorithmica

    2 publications · 71 citations

    2

    h-index

  3. Neural Information Processing Systems

    Conference · 2 publications · 171 citations

    2

    h-index

  4. IEEE Transactions on Evolutionary Computation

    2 publications · 61 citations

    2

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 358 citations

    2

    h-index

1–5