347 research outputs found
Exploiting -Closure in Kernelization Algorithms for Graph Problems
A graph is c-closed if every pair of vertices with at least c common
neighbors is adjacent. The c-closure of a graph G is the smallest number such
that G is c-closed. Fox et al. [ICALP '18] defined c-closure and investigated
it in the context of clique enumeration. We show that c-closure can be applied
in kernelization algorithms for several classic graph problems. We show that
Dominating Set admits a kernel of size k^O(c), that Induced Matching admits a
kernel with O(c^7*k^8) vertices, and that Irredundant Set admits a kernel with
O(c^(5/2)*k^3) vertices. Our kernelization exploits the fact that c-closed
graphs have polynomially-bounded Ramsey numbers, as we show
Kernelization and Sparseness: the case of Dominating Set
We prove that for every positive integer and for every graph class
of bounded expansion, the -Dominating Set problem admits a
linear kernel on graphs from . Moreover, when is only
assumed to be nowhere dense, then we give an almost linear kernel on for the classic Dominating Set problem, i.e., for the case . These
results generalize a line of previous research on finding linear kernels for
Dominating Set and -Dominating Set. However, the approach taken in this
work, which is based on the theory of sparse graphs, is radically different and
conceptually much simpler than the previous approaches.
We complement our findings by showing that for the closely related Connected
Dominating Set problem, the existence of such kernelization algorithms is
unlikely, even though the problem is known to admit a linear kernel on
-topological-minor-free graphs. Also, we prove that for any somewhere dense
class , there is some for which -Dominating Set is
W[]-hard on . Thus, our results fall short of proving a sharp
dichotomy for the parameterized complexity of -Dominating Set on
subgraph-monotone graph classes: we conjecture that the border of tractability
lies exactly between nowhere dense and somewhere dense graph classes.Comment: v2: new author, added results for r-Dominating Sets in bounded
expansion graph
A Linear Kernel for Planar Total Dominating Set
A total dominating set of a graph is a subset such
that every vertex in is adjacent to some vertex in . Finding a total
dominating set of minimum size is NP-hard on planar graphs and W[2]-complete on
general graphs when parameterized by the solution size. By the meta-theorem of
Bodlaender et al. [J. ACM, 2016], there exists a linear kernel for Total
Dominating Set on graphs of bounded genus. Nevertheless, it is not clear how
such a kernel can be effectively constructed, and how to obtain explicit
reduction rules with reasonably small constants. Following the approach of
Alber et al. [J. ACM, 2004], we provide an explicit kernel for Total Dominating
Set on planar graphs with at most vertices, where is the size of the
solution. This result complements several known constructive linear kernels on
planar graphs for other domination problems such as Dominating Set, Edge
Dominating Set, Efficient Dominating Set, Connected Dominating Set, or Red-Blue
Dominating Set.Comment: 33 pages, 13 figure
Lossy Kernels for Connected Dominating Set on Sparse Graphs
For alpha > 1, an alpha-approximate (bi-)kernel for a problem Q is a polynomial-time algorithm that takes as input an instance (I, k) of Q and outputs an instance (I\u27,k\u27) (of a problem Q\u27) of size bounded by a function of k such that, for every c >= 1, a c-approximate solution for the new instance can be turned into a (c alpha)-approximate solution of the original instance in polynomial time. This framework of lossy kernelization was recently introduced by Lokshtanov et al. We study Connected Dominating Set (and its distance-r variant) parameterized by solution size on sparse graph classes like biclique-free graphs, classes of bounded expansion, and nowhere dense classes. We prove that for every alpha > 1, Connected Dominating Set admits a polynomial-size alpha-approximate (bi-)kernel on all the aforementioned classes. Our results are in sharp contrast to the kernelization complexity of Connected Dominating Set, which is known to not admit a polynomial kernel even on 2-degenerate graphs and graphs of bounded expansion, unless NP subseteq coNP/poly. We complement our results by the following conditional lower bound. We show that if a class C is somewhere dense and closed under taking subgraphs, then for some value of r in N there cannot exist an alpha-approximate bi-kernel for the (Connected) Distance-r Dominating Set problem on C for any alpha > 1 (assuming the Gap Exponential Time Hypothesis)
On the Kernel and Related Problems in Interval Digraphs
Given a digraph , a set is said to be absorbing set
(resp. dominating set) if every vertex in the graph is either in or is an
in-neighbour (resp. out-neighbour) of a vertex in . A set
is said to be an independent set if no two vertices in are adjacent in .
A kernel (resp. solution) of is an independent and absorbing (resp.
dominating) set in . We explore the algorithmic complexity of these problems
in the well known class of interval digraphs. A digraph is an interval
digraph if a pair of intervals can be assigned to each vertex
of such that if and only if .
Many different subclasses of interval digraphs have been defined and studied in
the literature by restricting the kinds of pairs of intervals that can be
assigned to the vertices. We observe that several of these classes, like
interval catch digraphs, interval nest digraphs, adjusted interval digraphs and
chronological interval digraphs, are subclasses of the more general class of
reflexive interval digraphs -- which arise when we require that the two
intervals assigned to a vertex have to intersect. We show that all the problems
mentioned above are efficiently solvable, in most of the cases even linear-time
solvable, in the class of reflexive interval digraphs, but are APX-hard on even
the very restricted class of interval digraphs called point-point digraphs,
where the two intervals assigned to each vertex are required to be degenerate,
i.e. they consist of a single point each. The results we obtain improve and
generalize several existing algorithms and structural results for subclasses of
reflexive interval digraphs.Comment: 26 pages, 3 figure
The effect of girth on the kernelization complexity of Connected Dominating Set
In the Connected Dominating Set problem we are given as input a graph and a positive integer , and are asked if there is a set of at most vertices of such that is a dominating set of and the subgraph induced by is connected. This is a basic connectivity problem that is known to be NP-complete, and it has been extensively studied using several algorithmic approaches. In this paper we study the effect of excluding short cycles, as a subgraph, on the kernelization complexity of Connected Dominating Set.
Kernelization algorithms are polynomial-time algorithms that take an input and a positive integer (the parameter) and output an equivalent instance where the size of the new instance and the new parameter are both bounded by some function . The new instance is called a kernel for the problem. If is a polynomial in then we say that the problem admits polynomial kernels. The girth of a graph is the length of a shortest cycle in . It turns out that Connected Dominating Set is ``hard\u27\u27 on graphs with small cycles, and becomes progressively easier as the girth increases. More specifically, we obtain the following interesting trichotomy: Connected Dominating Set (a) does not have a kernel of any size on graphs of girth or (since the problem is W[2]-hard); (b) admits a kernel, where is , on graphs of girth or but has no polynomial kernel (unless the Polynomial Hierarchy (PH) collapses to the third level) on these graphs; (c) has a cubic () kernel on graphs of girth at least .
While there is a large and growing collection of parameterized complexity results available for problems on graph classes characterized by excluded minors, our results add to the very few known in the field for graph classes characterized by excluded subgraphs
Minimum Fill-in of Sparse Graphs: Kernelization and Approximation
The Minimum Fill-in problem is to decide if a graph can be triangulated by adding at most k edges. The problem has important applications in numerical algebra, in particular in sparse matrix computations. We develop kernelization algorithms for the problem on several classes of sparse graphs. We obtain linear kernels on planar graphs, and kernels of size O(k^{3/2}) in graphs excluding some fixed graph as a minor and in graphs of bounded degeneracy. As a byproduct of our results, we obtain approximation algorithms with approximation ratios O(log{k}) on planar graphs and O(sqrt{k} log{k}) on H-minor-free graphs. These results significantly improve the previously known kernelization and approximation results for Minimum Fill-in on sparse graphs.publishedVersio
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