Kernel Lower Bounds Using Co-nondeterminism: Finding Induced Hereditary Subgraphs

  • Stefan Kratsch
  • Marcin Pilipczuk
  • Ashutosh Rai
  • Venkatesh Raman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7357)


This work further explores the applications of co-nondeterminism for showing kernelization lower bounds. The only known example excludes polynomial kernelizations for the Ramsey problem of finding an independent set or a clique of at least k vertices in a given graph (Kratsch 2012, SODA). We study the more general problem of finding induced subgraphs on k vertices fulfilling some hereditary property Π, called Π-Induced Subgraph. The problem is NP-hard for all non-trivial choices of Π by a classic result of Lewis and Yannakakis (JCSS 1980). The parameterized complexity of this problem was classified by Khot and Raman (TCS 2002) depending on the choice of Π. The interesting cases for kernelization are for Π containing all independent sets and all cliques, since the problem is trivial or W[1]-hard otherwise.

Our results are twofold. Regarding Π-Induced Subgraph, we show that for a large choice of natural graph properties Π, including chordal, perfect, cluster, and cograph, there is no polynomial kernel with respect to k. This is established by two theorems: one using a co-nondeterministic variant of cross-composition and one by a polynomial parameter transformation from Ramsey.

Additionally, we show how to use improvement versions of NP-hard problems as source problems for lower bounds, without requiring their NP-hardness. E.g., for Π-Induced Subgraph our compositions may assume existing solutions of size k − 1. We believe this to be useful for further lower bound proofs, since improvement versions simplify the construction of a disjunction (OR) of instances required in compositions. This adds a second way of using co-nondeterminism for lower bounds.


Polynomial Kernel Chordal Graph Computation Path Graph Class Perfect Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Stefan Kratsch
    • 1
  • Marcin Pilipczuk
    • 2
  • Ashutosh Rai
    • 3
  • Venkatesh Raman
    • 3
  1. 1.Utrecht UniversityUtrechtThe Netherlands
  2. 2.Institute of InformaticsUniversity of WarsawPoland
  3. 3.The Institute of Mathematical SciencesChennaiIndia

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