20,566 research outputs found
On upper bounds for total -domination number via the probabilistic method
summary:For a fixed positive integer and a connected graph of order , whose minimum vertex degree is at least , a set is a total -dominating set, also known as a -tuple total dominating set, if every vertex has at least neighbors in . The minimum size of a total -dominating set for is called the total -domination number of , denoted by . The total -domination problem is to determine a minimum total -dominating set of . Since the exact problem is in general quite difficult to solve, it is also of interest to have good upper bounds on the total -domination number. In this paper, we present a probabilistic approach to computing an upper bound for the total -domination number that improves on some previous results
Weighted domination models and randomized heuristics
We consider the minimum weight and smallest weight minimum-size dominating
set problems in vertex-weighted graphs and networks. The latter problem is a
two-objective optimization problem, which is different from the classic minimum
weight dominating set problem that requires finding a dominating set of the
smallest weight in a graph without trying to optimize its cardinality. First,
we show how to reduce the two-objective optimization problem to the minimum
weight dominating set problem by using Integer Linear Programming formulations.
Then, under different assumptions, the probabilistic method is developed to
obtain upper bounds on the minimum weight dominating sets in graphs. The
corresponding randomized algorithms for finding small-weight dominating sets in
graphs are described as well. Computational experiments are used to illustrate
the results for two different types of random graphs.Comment: 23 page
Mixed-Weight Open Locating-Dominating Sets
The detection and location of issues in a network is a common problem encompassing a wide variety of research areas. Location-detection problems have been studied for wireless sensor networks and environmental monitoring, microprocessor fault detection, public utility contamination, and finding intruders in buildings. Modeling these systems as a graph, we want to find the smallest subset of nodes that, when sensors are placed at those locations, can detect and locate any anomalies that arise. One type of set that solves this problem is the open locating-dominating set (OLD-set), a set of nodes that forms a unique and nonempty neighborhood with every node in the graph. For this work, we begin with a study of OLD-sets in circulant graphs. Circulant graphs are a group of regular cyclic graphs that are often used in massively parallel systems. We prove the optimal OLD-set size for two circulant graphs using two proof techniques: the discharging method and Hall\u27s Theorem. Next we introduce the mixed-weight open locating-dominating set (mixed-weight OLD-set), an extension of the OLD-set. The mixed-weight OLD-set allows nodes in the graph to have different weights, representing systems that use sensors of varying strengths. This is a novel approach to the study of location-detection problems. We show that the decision problem for the minimum mixed-weight OLD-set, for any weights up to positive integer d, is NP-complete. We find the size of mixed-weight OLD-sets in paths and cycles for weights 1 and 2. We consider mixed-weight OLD-sets in random graphs by providing probabilistic bounds on the size of the mixed-weight OLD-set and use simulation to reinforce the theoretical results. Finally, we build and study an integer linear program to solve for mixed-weight OLD-sets and use greedy algorithms to generate mixed-weight OLD-set estimates in random geometric graphs. We also extend our results for mixed-weight OLD-sets in random graphs to random geometric graphs by estimating the probabilistic upper bound for the size of the set
Upper bounds for alpha-domination parameters
In this paper, we provide a new upper bound for the alpha-domination number.
This result generalises the well-known Caro-Roditty bound for the domination
number of a graph. The same probabilistic construction is used to generalise
another well-known upper bound for the classical domination in graphs. We also
prove similar upper bounds for the alpha-rate domination number, which combines
the concepts of alpha-domination and k-tuple domination.Comment: 7 pages; Presented at the 4th East Coast Combinatorial Conference,
Antigonish (Nova Scotia, Canada), May 1-2, 200
Bounds for identifying codes in terms of degree parameters
An identifying code is a subset of vertices of a graph such that each vertex
is uniquely determined by its neighbourhood within the identifying code. If
\M(G) denotes the minimum size of an identifying code of a graph , it was
conjectured by F. Foucaud, R. Klasing, A. Kosowski and A. Raspaud that there
exists a constant such that if a connected graph with vertices and
maximum degree admits an identifying code, then \M(G)\leq
n-\tfrac{n}{d}+c. We use probabilistic tools to show that for any ,
\M(G)\leq n-\tfrac{n}{\Theta(d)} holds for a large class of graphs
containing, among others, all regular graphs and all graphs of bounded clique
number. This settles the conjecture (up to constants) for these classes of
graphs. In the general case, we prove \M(G)\leq n-\tfrac{n}{\Theta(d^{3})}.
In a second part, we prove that in any graph of minimum degree and
girth at least 5, \M(G)\leq(1+o_\delta(1))\tfrac{3\log\delta}{2\delta}n.
Using the former result, we give sharp estimates for the size of the minimum
identifying code of random -regular graphs, which is about
Multiple domination models for placement of electric vehicle charging stations in road networks
Electric and hybrid vehicles play an increasing role in the road transport
networks. Despite their advantages, they have a relatively limited cruising
range in comparison to traditional diesel/petrol vehicles, and require
significant battery charging time. We propose to model the facility location
problem of the placement of charging stations in road networks as a multiple
domination problem on reachability graphs. This model takes into consideration
natural assumptions such as a threshold for remaining battery load, and
provides some minimal choice for a travel direction to recharge the battery.
Experimental evaluation and simulations for the proposed facility location
model are presented in the case of real road networks corresponding to the
cities of Boston and Dublin.Comment: 20 pages, 5 figures; Original version from March-April 201
Approximate Clustering via Metric Partitioning
In this paper we consider two metric covering/clustering problems -
\textit{Minimum Cost Covering Problem} (MCC) and -clustering. In the MCC
problem, we are given two point sets (clients) and (servers), and a
metric on . We would like to cover the clients by balls centered at
the servers. The objective function to minimize is the sum of the -th
power of the radii of the balls. Here is a parameter of the
problem (but not of a problem instance). MCC is closely related to the
-clustering problem. The main difference between -clustering and MCC is
that in -clustering one needs to select balls to cover the clients.
For any \eps > 0, we describe quasi-polynomial time (1 + \eps)
approximation algorithms for both of the problems. However, in case of
-clustering the algorithm uses (1 + \eps)k balls. Prior to our work, a
and a approximation were achieved by
polynomial-time algorithms for MCC and -clustering, respectively, where is an absolute constant. These two problems are thus interesting examples of
metric covering/clustering problems that admit (1 + \eps)-approximation
(using (1+\eps)k balls in case of -clustering), if one is willing to
settle for quasi-polynomial time. In contrast, for the variant of MCC where
is part of the input, we show under standard assumptions that no
polynomial time algorithm can achieve an approximation factor better than
for .Comment: 19 page
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