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|See also: optimization, gradient descent|
The simplex algorithm is a gradient search procedure and is quite popular, since it is based on simple principles and is therefore easy to understand and implement  (do not confuse this simplex algorithm with the simplex method in linear programming). The idea is as follows: if the parameter to be optimized depends on k variables, then select k+1 points. These points must not form collinear vectors. Now, find the points with the worst response, the best response, and the next-to-worst response. Next, mirror the worst point about the centroid of the face spanned by the other k points. This procedure is repeated until the best response is reached. An additional rule has to be introduced in order to prevent the simplex from oscillating about a ridge: if the mirrored point remains the worst point, then use the next-to-worst point for reflection across the centroid.
For better understanding, follow the development of the optimization process in this .
Please note that the simplex optimization is prone
to get trapped into local maxima. The results of a simplex run depend on
the starting conditions. In order to raise the chance of finding the global
optimum, one should repeat several simplex runs with different starting
|||J.A. Nelder, R. Mead
Computer Journal 7 (1965) 308
Last Update: 2004-Jul-03