On the Parameterized Complexity of Reconfiguration Problems

Amer E. Mouawad, Naomi Nishimura, Venkatesh Raman, Narges Simjour, Akira Suzuki

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

We present the first results on the parameterized complexity of reconfiguration problems, where a reconfiguration variant of an optimization problem Q takes as input two feasible solutions S and T and determines if there is a sequence of reconfiguration steps, i.e. a reconfiguration sequence, that can be applied to transform S into T such that each step results in a feasible solution to Q. For most of the results in this paper, S and T are sets of vertices of a given graph and a reconfiguration step adds or removes a vertex. Our study is motivated by results establishing that for many NP-hard problems, the classical complexity of reconfiguration is PSPACE-complete. We address the question for several important graph properties under two natural parameterizations: k, a bound on the size of solutions, and ℓ, a bound on the length of reconfiguration sequences. Our first general result is an algorithmic paradigm, the reconfiguration kernel, used to obtain fixed-parameter tractable algorithms for reconfiguration variants of Vertex Cover and, more generally, Bounded Hitting Set and Feedback Vertex Set, all parameterized by k. In contrast, we show that reconfiguring Unbounded Hitting Set is W[2]-hard when parameterized by k+ ℓ. We also demonstrate the W[1]-hardness of reconfiguration variants of a large class of maximization problems parameterized by k+ ℓ, and of their corresponding deletion problems parameterized by ℓ; in doing so, we show that there exist problems in FPT when parameterized by k, but whose reconfiguration variants are W[1]-hard when parameterized by k+ ℓ.

Original languageEnglish
Pages (from-to)274-297
Number of pages24
JournalAlgorithmica
Volume78
Issue number1
DOIs
Publication statusPublished - 2017 May 1

Keywords

  • Hitting set
  • Parameterized complexity
  • Reconfiguration
  • Solution space
  • Vertex cover

ASJC Scopus subject areas

  • Computer Science(all)
  • Computer Science Applications
  • Applied Mathematics

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