On Kernelization and Approximation for the Vector Connectivity Problem

Stefan Kratsch, Manuel Sorge

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


In the Vector Connectivity problem we are given an undirected graph G= (V, E) , a demand function λ: V→ { 0 , … , d} , and an integer k. The question is whether there exists a set S of at most k vertices such that every vertex v∈ V\ S has at least λ(v) vertex-disjoint paths to S; this abstractly captures questions about placing servers or warehouses relative to demands. The problem is NP-hard already for instances with d= 4 (Cicalese et al., Theoretical Computer Science ’15), admits a log-factor approximation (Boros et al., Networks ’14), and is fixed-parameter tractable in terms of k (Lokshtanov, unpublished ’14). We prove several results regarding kernelization and approximation for Vector Connectivity and the variant Vectord-Connectivity where the upper bound d on demands is a fixed constant. For Vectord-Connectivity we give a factor d-approximation algorithm and construct a vertex-linear kernelization, that is, an efficient reduction to an equivalent instance with f(d) k= O(k) vertices. For Vector Connectivity we have a factor opt-approximation and we can show that it has no kernelization to size polynomial in k or even k+ d unless NP⊆ coNP/ poly, which shows that f(d) poly (k) is optimal for Vectord-Connectivity. Finally, we give a simple randomized fixed-parameter algorithm for Vector Connectivity with respect to k based on matroid intersection.

Original languageEnglish
Pages (from-to)96-138
Number of pages43
Issue number1
StatePublished - 1 Sep 2017
Externally publishedYes


  • Approximation
  • Graph algorithms
  • Kernelization
  • NP-hard problem
  • Parameterized complexity
  • Separators

ASJC Scopus subject areas

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


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