Linear Time Parameterized Algorithms for Subset Feedback Vertex Set

  • Daniel Lokshtanov
  • M. S. RamanujanEmail author
  • Saket Saurabh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9134)


In the Subset Feedback Vertex Set (Subset FVS) problem, the input is a graph \(G\) on \(n\) vertices and \(m\) edges, a subset of vertices \(T\), referred to as terminals, and an integer \(k\). The objective is to determine whether there exists a set of at most \(k\) vertices intersecting every cycle that contains a terminal. The study of parameterized algorithms for this generalization of the Feedback Vertex Set problem has received significant attention over the last few years. In fact the parameterized complexity of this problem was open until 2011, when two groups independently showed that the problem is fixed parameter tractable (FPT). Using tools from graph minors Kawarabayashi and Kobayashi obtained an algorithm for Subset FVS running in time \({\mathcal {O}}(f(k)\cdot n^2 m)\) [SODA 2012, JCTB 2012]. Independently, Cygan et al. [ICALP 2011, SIDMA 2013] designed an algorithm for Subset FVS running in time \(2^{{\mathcal {O}}(k \log k)}\cdot n^{{\mathcal {O}}(1)}\). More recently, Wahlström obtained the first single exponential time algorithm for Subset FVS, running in time \(4^{k}\cdot n^{{\mathcal {O}}(1)}\) [SODA 2014]. While the \(2^{{\mathcal {O}}(k)}\) dependence on the parameter \(k\) is optimal under the Exponential Time Hypothesis (ETH), the dependence of this algorithm as well as those preceding it, on the input size is far from linear.

In this paper we design the first linear time parameterized algorithms for Subset FVS. More precisely, we obtain two new algorithms for Subset FVS.

  • A randomized algorithm for Subset FVS running in time \({\mathcal {O}}(25.6^k k^{{\mathcal {O}}(1)} (n + m))\).

  • A deterministic algorithm for Subset FVS running in time \(2^{{\mathcal {O}}(k \log k)} (n + m)\).

In particular, the first algorithm obtains the best possible dependence on both the parameter as well as the input size, up to the constant in the exponent. Both of our algorithms are based on “cut centrality”, in the sense that solution vertices are likely to show up in minimum size cuts between vertices sampled from carefully chosen distributions.


Linear Time Input Size Internal Vertex Linear Time Algorithm Graph Minor 
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

  • Daniel Lokshtanov
    • 1
  • M. S. Ramanujan
    • 1
    Email author
  • Saket Saurabh
    • 1
    • 2
  1. 1.University of BergenBergenNorway
  2. 2.The Institute of Mathematical SciencesChennaiIndia

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