Fast Algorithms for Parameterized Problems with Relaxed Disjointness Constraints

  • Ariel Gabizon
  • Daniel Lokshtanov
  • Michał Pilipczuk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9294)


In this paper we consider generalized versions of four well-studied problems in parameterized complexity and exact exponential time algorithms: k-Path, Set Packing, Multilinear Monomial Testing and Hamiltonian Path. The generalization is in every case obtained by introducing a relaxation parameter, which relaxes the constraints on feasible solutions. For example, the k-Path problem is generalized to r-Simple k-Path where the task is to find a walk of length k that never visits any vertex more than r times. This problem was first considered by Abasi et al. [1]. Hamiltonian Path is generalized to Degree Bounded Spanning Tree, where the input is a graph G and integer d, and the task is to find a spanning tree T of G such that every vertex has degree at most d in T.

The generalized problems can easily be shown to be NP-complete for every fixed value of the relaxation parameter. On the other hand, we give algorithms for the generalized problems whose worst-case running time (a) matches the running time of the best algorithms for the original problems up to constants in the exponent, and (b) improves significantly as the relaxation parameter increases. For example, we give a deterministic algorithm with running time \(O^{*}(2^{O(k\frac{\log r}{r})})\) for r-Simple k-Path matching up to a constant in the exponent the randomized algorithm of Abasi et al. [1], and a randomized algorithm with running time \(O^{*}(2^{O(n\frac{\log d}{d})})\) for Degree Bounded Spanning Tree improving upon an O(2n + o(n)) algorithm of Fomin et al. [8].

On the way to obtain our results we generalize the notion of representative sets to multisets, and give an efficient algorithm to compute such representative sets. Both the generalization of representative sets to multisets and the algorithm to compute them may be of independent interest.


Exact Algorithm Packing Problem Parameterized Problem Relaxation Parameter Hamiltonian Path 
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 2015

Authors and Affiliations

  • Ariel Gabizon
    • 1
  • Daniel Lokshtanov
    • 2
  • Michał Pilipczuk
    • 3
  1. 1.Department of Computer ScienceTechnionHaifaIsrael
  2. 2.Department of InformaticsUniversity of BergenBergenNorway
  3. 3.Institute of InformaticsUniversity of WarsawWarsawPoland

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