Search arXivSearch

arXiv · 1711.10891

Algorithmic Aspects of Semitotal Domination in Graphs

Abstract

For a graph $G=(V,E)$, a set $D \subseteq V$ is called a semitotal dominating set of $G$ if $D$ is a dominating set of $G$, and every vertex in $D$ is within distance~$2$ of another vertex of~$D$. The \textsc{Minimum Semitotal Domination} problem is to find a semitotal dominating set of minimum cardinality. Given a graph $G$ and a positive integer $k$, the \textsc{Semitotal Domination Decision} problem is to decide whether $G$ has a semitotal dominating set of cardinality at most $k$. The \textsc{Semitotal Domination Decision} problem is known to be NP-complete for general graphs. In this paper, we show that the \textsc{Semitotal Domination Decision} problem remains NP-complete for planar graphs, split graphs and chordal bipartite graphs. We give a polynomial time algorithm to solve the \textsc{Minimum Semitotal Domination} problem in interval graphs. We show that the \textsc{Minimum Semitotal Domination} problem in a graph with maximum degree~$Δ$ admits an approximation algorithm that achieves the approximation ratio of $2+3\ln(Δ+1)$, showing that the problem is in the class log-APX. We also show that the \textsc{Minimum Semitotal Domination} problem cannot be approximated within $(1 - ε)\ln |V| $ for any $ε> 0$ unless NP $\subseteq$ DTIME $(|V|^{O(\log \log |V|)})$. Finally, we prove that the \textsc{Minimum Semitotal Domination} problem is APX-complete for bipartite graphs with maximum degree $4$.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Michael A. Henning, Arti Pandey. 2017-11-29. Algorithmic Aspects of Semitotal Domination in Graphs. https://arxiv.org/abs/1711.10891

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Flip Dynamics for Sampling Colorings: Improving $(11/6-ε)$ Using a Simple Metric

We present improved bounds for randomly sampling $k$-colorings of graphs with maximum degree $Δ$; our results hold without any further structural assumptions on the graph. The Glauber dynamics is a simple single-site update Markov chain. Jerrum (1995) proved an optimal $O(n\log{n})$ mixing-time bound for Glauber dynamics whenever $k>2Δ$ where $Δ$ is the maximum degree of the input graph. This bound was improved by Vigoda (1999) to $k>(11/6)Δ$ using a "flip" dynamics which recolors (small) maximal two-colored components in each step. Vigoda's result was the best known for general graphs for 20 years until Chen et al. (2019) established optimal mixing of the flip dynamics for $k>(11/6-\varepsilon)Δ$ where $\varepsilon\approx 10^{-5}$. We present the first substantial improvement over these results. We prove an optimal mixing-time bound of $O(n\log{n})$ for the flip dynamics when $Δ\geq125$ and $k\geq1.809Δ$. This yields, through recent spectral independence results, an optimal $O(n\log{n})$ mixing time for the Glauber dynamics for every fixed $Δ\geq125$ in the same range of $k/Δ$. Our proof utilizes path coupling with a simple weighted Hamming distance for "unblocked" neighbors.

cs.DM

Factorisability of Low Dimensional Non-Negative Integer Matrices

We consider the problem of determining if a given two-dimensional nonnegative integer matrix $M$ is the product of two such matrices, excluding trivial units. A matrix $M$ with no such factorisation is called prime and therefore belongs to the minimal (infinite rank) generator of $2 \times 2$ matrices over the natural numbers, otherwise it is called composite. We also consider the problem of finding a (non-unique) factorisation of a composite matrix. Our results have applications in computational group theory and the theory of codes, where such matrices are called incidence matrices. We analyse the complexity of primality and finding a factorisation for a composite matrix, providing a first efficient algorithm.

cs.DM