Kernelization Lower Bounds

Kernelization Lower Bounds is a theoretical framework in parameterized complexity that establishes limits on the efficiency of kernelization algorithms by proving that certain parameterized problems cannot admit polynomial or smaller kernels unless widely believed complexity-theoretic assumptions fail.

52

Publications

3K

Citations

84

Authors

55

Institutions

Publications per year

2017–2026

11

Authors

84

  1. SK
    Stefan Kratsch

    Utrecht University, Netherlands

    10 publications · 656 citations

    10

    h-index

  2. SS
    Saket Saurabh

    Institute of Mathematical Sciences, India

    7 publications · 437 citations

    7

    h-index

  3. BM
    Bart M. P. Jansen

    Utrecht University, Netherlands

    6 publications · 229 citations

    6

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  4. MP
    Marcin Pilipczuk

    University of Warsaw, Poland

    6 publications · 197 citations

    5

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  5. NM
    Neeldhara Misra

    Indian Institute of Science Bangalore, India

    4 publications · 215 citations

    4

    h-index

1–5 of 84

Venues

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

    Repository · 5 publications · 106 citations

    5

    h-index

  2. SIAM Journal on Discrete Mathematics

    4 publications · 375 citations

    4

    h-index

  3. ACM Transactions on Algorithms

    TALG · 4 publications · 247 citations

    4

    h-index

  4. Algorithmica

    3 publications · 40 citations

    3

    h-index

  5. Theoretical Computer Science

    3 publications · 80 citations

    3

    h-index

1–5