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Journal articles

Sequential Metric Dimension

Julien Bensmail 1 Dorian Mazauric 2 Fionn Mc Inerney 1 Nicolas Nisse 1 Stéphane Pérennes 1
1 COATI - Combinatorics, Optimization and Algorithms for Telecommunications
Laboratoire I3S - COMRED - COMmunications, Réseaux, systèmes Embarqués et Distribués, CRISAM - Inria Sophia Antipolis - Méditerranée
2 ABS - Algorithms, Biology, Structure
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : In the localization game, introduced by Seager in 2013, an invisible and immobile target is hidden at some vertex of a graph $G$. At every step, one vertex $v$ of $G$ can be probed which results in the knowledge of the distance between $v$ and the secret location of the target. The objective of the game is to minimize the number of steps needed to locate the target whatever be its location. We address the generalization of this game where $k\geq 1$ vertices can be probed at every step. Our game also generalizes the notion of the {\it metric dimension} of a graph. Precisely, given a graph $G$ and two integers $k,\ell \geq 1$, the {\sc Localization} problem asks whether there exists a strategy to locate a target hidden in $G$ in at most $\ell$ steps and probing at most $k$ vertices per step. We first show that, in general, this problem is \textsf{NP}-complete for every fixed $k \geq 1$ (resp., $\ell \geq 1$). We then focus on the class of trees. On the negative side, we prove that the \Localization problem is \textsf{NP}-complete in trees when $k$ and $\ell$ are part of the input. On the positive side, we design a $(+1)$-approximation algorithm for the problem in $n$-node trees, {\it i.e.}, an algorithm that computes in time $O(n \log n)$ (independent of $k$) a strategy to locate the target in at most one more step than an optimal strategy. This algorithm can be used to solve the \Localization problem in trees in polynomial time if $k$ is fixed. We also consider some of these questions in the context where, upon probing the vertices, the relative distances to the target are retrieved. This variant of the problem generalizes the notion of the {\it centroidal dimension} of a graph.
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Submitted on : Wednesday, March 25, 2020 - 10:44:10 PM
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Julien Bensmail, Dorian Mazauric, Fionn Mc Inerney, Nicolas Nisse, Stéphane Pérennes. Sequential Metric Dimension. Algorithmica, Springer Verlag, 2020, 82 (10), pp.2867-2901. ⟨10.1007/s00453-020-00707-5⟩. ⟨hal-01717629v3⟩



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