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\name{summary.solveLP}
\alias{summary.solveLP}
\alias{print.summary.solveLP}
\title{Summary Results for Objects of Class solveLP}
\description{
These methods prepare and print summary results of the Linear Programming
algorithm.
}
\usage{
\method{summary}{solveLP}(object,...)
\method{print}{summary.solveLP}(x,...)
}
\arguments{
\item{object}{an object returned by \code{\link{solveLP}}.}
\item{x}{an object returned by \code{summary.solveLP}.}
\item{...}{currently ignored.}
}
\value{
\code{summary.solveLP} returns an object of class \code{summary.solveLP}.
\code{print.summary.solveLP} invisibly returns the object given
in argument \code{x}.
}
\author{
Arne Henningsen
}
\seealso{
\code{\link{solveLP}}, \code{\link{print.solveLP}},
\code{\link{readMps}}, \code{\link{writeMps}}
}
\examples{
## example of Steinhauser, Langbehn and Peters (1992)
\dontrun{library( linprog )}
## Production activities
cvec <- c(1800, 600, 600) # gross margins
names(cvec) <- c("Milk","Bulls","Pigs")
## Constraints (quasi-fix factors)
bvec <- c(40, 90, 2500) # endowment
names(bvec) <- c("Land","Stable","Labor")
## Needs of Production activities
Amat <- rbind( c( 0.7, 0.35, 0 ),
c( 1.5, 1, 3 ),
c( 50, 12.5, 20 ) )
## Maximize the gross margin
res <- solveLP( cvec, bvec, Amat, TRUE )
## prepare and print the summary results
summary( res )
}
\keyword{ optimize }
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