https://github.com/cran/cccd
Tip revision: c8cf4987ae83c508065b8b4f8f59f61fecbba00f authored by David J. Marchette on 04 November 2010, 00:00:00 UTC
version 1.00.02
version 1.00.02
Tip revision: c8cf498
dominate.Rd
\name{dominate}
\alias{dominate}
\alias{dominate.greedy}
\alias{dominate.byR}
\alias{dominate.systematic}
\alias{dominate.random.sample}
\title{ Dominating Sets}
\description{
find maximum dominating sets in (di)graphs.
}
\usage{
dominate(g, method = "greedy", verbose = TRUE)
dominate.greedy(g, weight=NULL)
dominate.byR(g)
dominate.random.sample(g)
}
\arguments{
\item{g}{ an adjacency matrix.}
\item{method}{ one of "greedy","systematic","random","byR".}
\item{weight}{ weight vector.}
\item{verbose}{ logical. In the case of systematic, whether to print out
information as it goes.}
}
\details{
\code{dominate} is the main program which calls the others,
as indicated by \code{method}. Greedy is the greedy dominating
algorithm. The weight vector is used to break ties: in the case
of a tie the vertex with the largest weight is chosen. The
"by radius" algorithm uses the vector \code{R} as the criterion
in the greedy algorithm, rather than degree.
The random routine simply uses a random weighting instead of weighting
by \code{R}. The systematic routine computes a true minimum dominating
set, but may take a long time as it is not optimized and uses brute force.
It first computes a greedy dominating set to reduce the search, but
that's all.
}
\value{
a vector of vertices corresponding to the dominating set.
Note: just like the vertex labels in igraph, these are 0-based.
}
\references{
T.W. Haynes, S.T. Hedetniemi and P.J. Slater,
Fundamentals of Domination in Graphs,
Marcel Dekker,
1998,
}
\author{ David J. Marchette david.marchette@navy.mil}
\examples{
x <- matrix(runif(100),ncol=2)
y <- matrix(runif(100,-2,2),ncol=2)
G <- cccd(x,y)
D <- dominate(G)
\dontrun{
plotCCCD(G,balls=T,D=D)
}
}
\keyword{ math }