https://github.com/cran/pracma
Tip revision: 26e049d70b4a1c237987e260cba68f6a9413736c authored by Hans W. Borchers on 09 April 2019, 04:10:07 UTC
version 2.2.5
version 2.2.5
Tip revision: 26e049d
fminunc.Rd
\name{fminunc}
\alias{fminunc}
\title{
Minimize Unconstrained Multivariable Function
}
\description{
Find minimum of unconstrained multivariable functions.
}
\usage{
fminunc(x0, fn, gr = NULL, ...,
tol = 1e-08, maxiter = 0, maxfeval = 0)
}
\arguments{
\item{x0}{starting point.}
\item{fn}{objective function to be minimized.}
\item{gr}{gradient function of the objective.}
\item{...}{additional parameters to be passed to the function.}
\item{tol}{relative tolerance.}
\item{maxiter}{maximum number of iterations.}
\item{maxfeval}{maximum number of function evaluations.}
}
\details{
The method used here for unconstrained minimization is a variant of a
"variable metric" resp. quasi-Newton approach.
}
\value{
List with the following components:
\item{par}{the best minimum found.}
\item{value}{function value at the minimum.}
\item{counts}{number of function and gradient calls.}
\item{convergence}{integer indicating the terminating situation.}
\item{message}{description of the final situation.}
}
\references{
J. Nocedal and S. J. Wright (2006). Numerical Optimization. Second
Edition, Springer Science+Business Media, New York.
}
\note{
\code{fminunc} mimics the Matlab function of the same name.
}
\author{
The "variable metric" code provided by John Nash (package Rvmmin),
stripped-down version by Hans W. Borchers.
}
\seealso{
\code{\link{fminsearch}}, \code{\link{fmincon}},
}
\examples{
fun = function(x)
x[1]*exp(-(x[1]^2 + x[2]^2)) + (x[1]^2 + x[2]^2)/20
fminunc(x0 = c(1, 2), fun)
## xmin: c(-0.6691, 0.0000); fmin: -0.4052
}
\keyword{ optimize }