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.
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3K
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84
Authors
55
Institutions
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2017–2026
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Parametric Duality and Kernelization: Lower Bounds and Upper Bounds on Kernel Size
Jianer Chen, Henning Fernau, Iyad Kanj et al. · SIAM Journal on Computing · 2007 · 154 citations
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Utrecht University
Utrecht, The Netherlands
9 h-index
12 publications
928 citations
University of Bergen
Bergen, Norway
9 h-index
14 publications
622 citations

Institute of Mathematical Sciences
Chennai, India
8 h-index
10 publications
605 citations

University of Warsaw
Warsaw, Poland
5 h-index
16 publications
569 citations
Max Planck Society
Munich, Germany
4 h-index
7 publications
592 citations
Eindhoven University of Technology
Eindhoven, The Netherlands
3 h-index
4 publications
107 citations
Max Planck Institute for Informatics
Saarbrücken, Germany
3 h-index
3 publications
202 citations
University of Bonn
Bonn, Germany
3 h-index
3 publications
257 citations
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Repository · 5 publications · 106 citations
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4 publications · 375 citations
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TALG · 4 publications · 247 citations
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3 publications · 40 citations
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3 publications · 80 citations
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