Nelder-Mead downhill simplex

The Nelder-Mead downhill simplex method is the optimization technique incorporated in the software package Matlab as fin ins or fininsearch. 

The Nelder-Mead downhill simplex algorithm has the advantage of being very reliable and requiring no derivative evaluations. On the other hand, it is slower than methods that do use derivative information. Of course, even a relatively slow method may be quite fast enough for a not-too-large problem on a fairly fast computer. 

Commercially available software developed to process individual impedance spectra use few general algorithms such as Levenberg-Marquardt algorithm, the Nelder-Mead downhill simplex method or genetic algorithms [3-7]. The software is optimized to process only... 

The simplex method given by Nelder and Mead (1965), sometimes called the downhill simplex method, is one of the few robust and efficient methods that does not use any derivative information. This greatly simplifies computational requirements and reduces the chances of errors that can crop up in the differentiation of complex rate expressions. 

The simplex method belongs to a group of optimisation methods finding the minimum of a predefined multiparameter function (error functional). The downhill simplex method of Nelder and Mead [8] requires only function... 

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