592 research outputs found
Identification, location-domination and metric dimension on interval and permutation graphs. II. Algorithms and complexity
We consider the problems of finding optimal identifying codes, (open) locating-dominating sets and resolving sets (denoted Identifying Code, (Open) Open Locating-Dominating Set and Metric Dimension) of an interval or a permutation graph. In these problems, one asks to distinguish all vertices of a graph by a subset of the vertices, using either the neighbourhood within the solution set or the distances to the solution vertices. Using a general reduction for this class of problems, we prove that the decision problems associated to these four notions are NP-complete, even for interval graphs of diameter 2 and permutation graphs of diameter 2. While Identifying Code and (Open) Locating-Dominating Set are trivially fixed-parameter-tractable when parameterized by solution size, it is known that in the same setting Metric Dimension is W[2]-hard. We show that for interval graphs, this parameterization of Metric Dimension is fixed-parameter-tractable
Centroidal localization game
One important problem in a network is to locate an (invisible) moving entity
by using distance-detectors placed at strategical locations. For instance, the
metric dimension of a graph is the minimum number of detectors placed
in some vertices such that the vector
of the distances between the detectors and the entity's location
allows to uniquely determine . In a more realistic setting, instead
of getting the exact distance information, given devices placed in
, we get only relative distances between the entity's
location and the devices (for every , it is provided
whether , , or to ). The centroidal dimension of a
graph is the minimum number of devices required to locate the entity in
this setting.
We consider the natural generalization of the latter problem, where vertices
may be probed sequentially until the moving entity is located. At every turn, a
set of vertices is probed and then the relative distances
between the vertices and the current location of the entity are
given. If not located, the moving entity may move along one edge. Let be the minimum such that the entity is eventually located, whatever it
does, in the graph .
We prove that for every tree and give an upper bound
on in cartesian product of graphs and . Our main
result is that for any outerplanar graph . We then prove
that is bounded by the pathwidth of plus 1 and that the
optimization problem of determining is NP-hard in general graphs.
Finally, we show that approximating (up to any constant distance) the entity's
location in the Euclidean plane requires at most two vertices per turn
Metric-locating-dominating sets of graphs for constructing related subsets of vertices
© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A dominating set S of a graph is a metric-locating-dominating set if each vertex of the graph is uniquely distinguished by its distances from the elements of S , and the minimum cardinality of such a set is called the metric-location-domination number. In this paper, we undertake a study that, in general graphs and specific families, relates metric-locating-dominating sets to other special sets: resolving sets, dominating sets, locating-dominating sets and doubly resolving sets. We first characterize the extremal trees of the bounds that naturally involve metric-location-domination number, metric dimension and domination number. Then, we prove that there is no polynomial upper bound on the location-domination number in terms of the metric-location-domination number, thus extending a result of Henning and Oellermann. Finally, we show different methods to transform metric-locating-dominating sets into locating-dominating sets and doubly resolving sets. Our methods produce new bounds on the minimum cardinalities of all those sets, some of them concerning parameters that have not been related so farPeer ReviewedPostprint (author's final draft
On three domination numbers in block graphs
The problems of determining minimum identifying, locating-dominating or open
locating-dominating codes are special search problems that are challenging both
from a theoretical and a computational point of view. Hence, a typical line of
attack for these problems is to determine lower and upper bounds for minimum
codes in special graphs. In this work we study the problem of determining the
cardinality of minimum codes in block graphs (that are diamond-free chordal
graphs). We present for all three codes lower and upper bounds as well as block
graphs where these bounds are attained
Parameterized and approximation complexity of the detection pair problem in graphs
We study the complexity of the problem DETECTION PAIR. A detection pair of a
graph is a pair of sets of detectors with , the
watchers, and , the listeners, such that for every pair
of vertices that are not dominated by a watcher of , there is a listener of
whose distances to and to are different. The goal is to minimize
. This problem generalizes the two classic problems DOMINATING SET and
METRIC DIMENSION, that correspond to the restrictions and
, respectively. DETECTION PAIR was recently introduced by Finbow,
Hartnell and Young [A. S. Finbow, B. L. Hartnell and J. R. Young. The
complexity of monitoring a network with both watchers and listeners.
Manuscript, 2015], who proved it to be NP-complete on trees, a surprising
result given that both DOMINATING SET and METRIC DIMENSION are known to be
linear-time solvable on trees. It follows from an existing reduction by Hartung
and Nichterlein for METRIC DIMENSION that even on bipartite subcubic graphs of
arbitrarily large girth, DETECTION PAIR is NP-hard to approximate within a
sub-logarithmic factor and W[2]-hard (when parameterized by solution size). We
show, using a reduction to SET COVER, that DETECTION PAIR is approximable
within a factor logarithmic in the number of vertices of the input graph. Our
two main results are a linear-time -approximation algorithm and an FPT
algorithm for DETECTION PAIR on trees.Comment: 13 page
Metric dimension of maximal outerplanar graphs
In this paper, we study the metric dimension problem in maximal outerplanar graphs. Concretely, if ß(G) denotes the metric dimension of a maximal outerplanar graph G of order n, we prove that 2=ß(G)=¿2n5¿ and that the bounds are tight. We also provide linear algorithms to decide whether the metric dimension of G is 2 and to build a resolving set S of size ¿2n5¿ for G. Moreover, we characterize all maximal outerplanar graphs with metric dimension 2.Peer ReviewedPostprint (author's final draft
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