\name{fminsearch}
\alias{fminsearch}
\title{
Minimum Finding
}
\description{
Find minimum of unconstrained multivariable function.
}
\usage{
fminsearch(f, x0, ..., minimize = TRUE, tol = .Machine$double.eps^(2/3))
}
\arguments{
\item{f}{function whose minimum or maximum is to be found.}
\item{x0}{point considered near to the optimum.}
\item{minimize}{logical; shall a minimum or a maximum be found.}
\item{tol}{relative tolerance.}
\item{...}{additional variables to be passed to the function.}
}
\details{
\code{fminsearch} finds the minimum of a nonlinear scalar multivariable
function, starting at an initial estimate and returning a value x that is
a local minimizer of the function.
This is generally referred to as unconstrained nonlinear optimization.
\code{fminsearch} may only give local solutions.
}
\value{
List with
\item{x}{location of the location of minimum resp. maximum.}
\item{fval}{function value at the optimum.}
}
\references{
Nocedal, J., and S. Wright (2006). Numerical Optimization.
Second Edition, Springer-Verlag, New York.
}
\note{
\code{fminbnd} mimics the Matlab function of the same name and uses the
R function \code{optim} with method `Nelder-Mead'.
Will be replaced with own version of Nelder Mead.
}
\seealso{
\code{\link{optim}}
}
\examples{
# Rosenbrock function
rosena <- function(x, a) 100*(x[2]-x[1]^2)^2 + (a-x[1])^2 # min: (a, a^2)
fminsearch(rosena, c(-1.2, 1), a = sqrt(2))
# x = (1.414218 2.000013) , fval = 4.115895e-11
}
\keyword{ optimize }