\chapter{Conclusion}
That tool might be extended in future releases so that it provides the following features :
\begin{itemize}
\item Kelley restart based on simplex gradient [9],
\item C-based implementation (a prototype is provided in appendix B),
\item parallel implementation of the DIRECT algorithm,
\item implementation of the Hook-Jeeves and Multidimensional Search methods [9]
\item parallel implementation of the Nelder-Mead algorithm. See for example [21].
?This paper generalizes the widely used Nelder and Mead (Comput J
7:308?313, 1965) simplex algorithm to parallel processors. Unlike most
previous parallelization methods, which are based on parallelizing the
tasks required to compute a specific objective function given a vector
of parameters, our parallel simplex algorithm uses parallelization at
the parameter level. Our parallel simplex algorithm assigns to each
processor a separate vector of parameters corresponding to a point on a
simplex. The processors then conduct the simplex search steps for an
improved point, communicate the results, and a new simplex is formed.
The advantage of this method is that our algorithm is generic and can be
applied, without re-writing computer code, to any optimization problem
which the non-parallel Nelder?Mead is applicable. The method is also
easily scalable to any degree of parallelization up to the number of
parameters. In a series of Monte Carlo experiments, we show that this
parallel simplex method yields computational savings in some experiments
up to three times the number of processors.?
\end{itemize}