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\name{tnbc}
\alias{tnbc}
\encoding{UTF-8}
\title{Truncated Newton function minimization with bounds constraints}
\description{A bounds-constarined R implementation of a truncated Newton method
for minimization of nonlinear functions subject to bounds (box) constraints.
}
\usage{
tnbc(x, fgfun, lower, upper, trace=0, \dots)
}
\arguments{
\item{x}{A numeric vector of starting estimates.}
\item{fgfun}{A function that returns the value of the objective at
the supplied set of parameters \code{par} using auxiliary data in
\dots. The gradient is returned as attribute "gradient".
The first argument of \code{fgfun} must be \code{par}. }
\item{lower}{A vector of lower bounds on the parameters.}
\item{upper}{A vector of upper bounds on the parameters.}
\item{trace}{Set >0 to cause intermediate output to allow progress
to be followed.}
\item{\dots}{Further arguments to be passed to \code{fn}.}
}
\details{
Function \code{fgfun} must return a numeric value in list item \code{f}
and a numeric vector in list item \code{g}.
}
\value{
A list with components:
\item{xstar}{The best set of parameters found.}
\item{f}{The value of the objective at the best set of parameters found.}
\item{g}{The gradient of the objective at the best set of parameters found.}
\item{ierror}{An integer indicating the situation on termination. \code{0}
indicates that the method believes it has succeeded; \code{2} that
more than \code{maxfun} (default 150*n, where there are n parameters);
\code{3} if the line search appears to have failed (which may not be serious);
and \code{-1} if there appears to be an error in the input parameters.}
\item{nfngr}{A number giving a measure of how many conjugate gradient solutions
were used during the minimization process.}
}
\references{
Stephen G. Nash (1984) "Newton-type minimization via the Lanczos method",
SIAM J Numerical Analysis, vol. 21, no. 4, pages 770-788.
For Matlab code, see http://www.netlib.org/opt/tn
}
\seealso{\code{\link{optim}}}
\examples{
## See tn.Rd
}
\keyword{nonlinear}
\keyword{optimize}
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