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Feedback vertex set

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In the mathematical discipline of graph theory, a feedback vertex set of a graph is a set of vertices whose removal leaves a graph without cycles. In other words, each feedback vertex set contains at least one vertex of any cycle in the graph. The feedback vertex set problem is an NP-complete problem in computational complexity theory. It was among the first problems shown to be NP-complete. It has wide applications in operating systems, database systems, and VLSI chip design.

Definition

The decision problem is as follows:

INSTANCE: An (undirected or directed) graph and a positive integer .
QUESTION: Is there a subset with such that with the vertices from deleted is cycle-free?

The graph that remains after removing from is an induced forest (resp. an induced directed acyclic graph in the case of directed graphs). Thus, finding a minimum feedback vertex set in a graph is equivalent to finding a maximum induced forest (resp. maximum induced directed acyclic graph in the case of directed graphs).

NP-hardness

Karp (1972) showed that the feedback vertex set problem for directed graphs is NP-complete. The problem remains NP-complete on directed graphs with maximum in-degree and out-degree two, and on directed planar graphs with maximum in-degree and out-degree three.[1] Karp's reduction also implies the NP-completeness of the feedback vertex set problem on undirected graphs, where the problem stays NP-hard on graphs of maximum degree four. The feedback vertex set problem can be solved in polynomial time on graphs of maximum degree at most three.[2]

Note that the problem of deleting as few edges as possible to make the graph cycle-free is equivalent to finding a spanning tree, which can be done in polynomial time. In contrast, the problem of deleting edges from a directed graph to make it acyclic, the feedback arc set problem, is NP-complete.[3]

Exact algorithms

The corresponding NP optimization problem of finding the size of a minimum feedback vertex set can be solved in time O(1.7347n), where n is the number of vertices in the graph.[4] This algorithm actually computes a maximum induced forest, and when such a forest is obtained, its complement is a minimum feedback vertex set. Alternatively, there is an algorithm [5] solving the problem with complexity O*(2m), where m can be computed efficiently and denotes the dimension of meta cycles of the graph. The number of minimal feedback vertex sets in a graph is bounded by O(1.8638n).[6] The directed feedback vertex set problem can still be solved in time O*(1.9977n), where n is the number of vertices in the given directed graph.[7] The parameterized versions of the directed and undirected problems are both fixed-parameter tractable.[8]

Approximation

The problem is APX-complete, which directly follows from the APX-completeness of the vertex cover problem,[9] and the existence of an approximation preserving L-reduction from the vertex cover problem to it.[3] The best known approximation algorithm on undirected graphs is by a factor of two.[10]

Bounds

According to the Erdős–Pósa theorem, the size of a minimum feedback vertex set is within a logarithmic factor of the maximum number of vertex-disjoint cycles in the given graph.[11]

Applications

In operating systems, feedback vertex sets play a prominent role in the study of deadlock recovery. In the wait-for graph of an operating system, each directed cycle corresponds to a deadlock situation. In order to resolve all deadlocks, some blocked processes have to be aborted. A minimum feedback vertex set in this graph corresponds to a minimum number of processes that one needs to abort.[12]

Furthermore, the feedback vertex set problem has applications in VLSI chip design.[13]

Notes

  1. ^ unpublished results due to Garey and Johnson, cf. Garey & Johnson (1979): GT7
  2. ^ Ueno, Kajitani & Gotoh (1988); Li & Liu (1999)
  3. ^ a b Karp (1972)
  4. ^ Fomin & Villanger (2010)
  5. ^ Hecht, Michael (2017), "Exact Localisations of Feedback Sets", Theory of Computing Systems, doi:10.1007/s00224-017-9777-6.
  6. ^ Fomin et al. (2008).
  7. ^ Razgon (2007).
  8. ^ Chen et al. (2008).
  9. ^ Dinur & Safra 2005
  10. ^ Becker & Geiger (1996). See also Bafna, Berman & Fujito (1999) for an alternative approximation algorithm with the same approximation ratio.
  11. ^ Erdős & Pósa (1965).
  12. ^ Silberschatz, Galvin & Gagne (2008).
  13. ^ Festa, Pardalos & Resende (2000).

References

Research articles

Textbooks and survey articles