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\name{ipop-class}
\docType{class}
\alias{ipop-class}
\alias{primal,ipop-method}
\alias{dual,ipop-method}
\alias{how,ipop-method}
\alias{primal}
\alias{dual}
\alias{how}
\title{Class "ipop"}
\description{The quadratic problem solver class}
\section{Objects from the Class}{
Objects can be created by calls of the form \code{new("ipop", ...)}.
or by calling the \code{ipop} function.
}
\section{Slots}{
\describe{
\item{\code{primal}:}{Object of class \code{"vector"} the primal
solution of the problem}
\item{\code{dual}:}{Object of class \code{"numeric"} the dual of the
problem}
\item{\code{how}:}{Object of class \code{"character"} convergence information}
}
}
\section{Methods}{
\describe{
\item{primal}{\code{signature(object = "ipop")}: Return the primal of
the problem}
\item{dual}{\code{signature(object = "ipop")}: Return the dual of
the problem}
\item{how}{\code{signature(object = "ipop")}: Return information on
convergence}
}
}
\author{Alexandros Karatzoglou\cr
\email{alexandros.karatzoglou@ci.tuwien.ac.at}}
\seealso{
\code{\link{ipop}}
}
\examples{
## solve the Support Vector Machine optimization problem
data(spam)
## sample a scaled part (300 points) of the spam data set
m <- 300
set <- sample(1:dim(spam)[1],m)
x <- scale(as.matrix(spam[,-58]))[set,]
y <- as.integer(spam[set,58])
y[y==2] <- -1
##set C parameter and kernel
C <- 5
rbf <- rbfdot(sigma = 0.1)
## create H matrix etc.
H <- kernelPol(rbf,x,,y)
c <- matrix(rep(-1,m))
A <- t(y)
b <- 0
l <- matrix(rep(0,m))
u <- matrix(rep(C,m))
r <- 0
sv <- ipop(c,H,A,b,l,u,r)
primal(sv)
dual(sv)
how(sv)
}
\keyword{classes}
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