Metric Dimension Parameterized by Feedback Vertex Set and Other Structural Parameters

Abstract

For a graph $G$, a subset $S \subseteq V(G)$ is called a \emph{resolving set}if for any two vertices $u,v \in V(G)$, there exists a vertex $w \in S$ suchthat $d(w,u) \neq d(w,v)$. The {\sc Metric Dimension} problem takes as input agraph $G$ and a positive integer $k$, and asks whether there exists a resolvingset of size at most $k$. This problem was introduced in the 1970s and is knownto be NP-hard~[GT~61 in Garey and Johnson's book]. In the realm ofparameterized complexity, Hartung and Nichterlein~[CCC~2013] proved that theproblem is W[2]-hard when parameterized by the natural parameter $k$. They alsoobserved that it is FPT when parameterized by the vertex cover number and askedabout its complexity under \emph{smaller} parameters, in particular thefeedback vertex set number. We answer this question by proving that {\sc MetricDimension} is W[1]-hard when parameterized by the feedback vertex set number.This also improves the result of Bonnet and Purohit~[IPEC 2019] which statesthat the problem is W[1]-hard parameterized by the treewidth. Regarding theparameterization by the vertex cover number, we prove that {\sc MetricDimension} does not admit a polynomial kernel under this parameterizationunless $NP\subseteq coNP/poly$. We observe that a similar result holds when theparameter is the distance to clique. On the positive side, we show that {\scMetric Dimension} is FPT when parameterized by either the distance to clusteror the distance to co-cluster, both of which are smaller parameters than thevertex cover number.<br