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	Index
See also: optimization, gradient descent

Simplex Algorithm

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 [1] (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 conditions.
 

Last Update: 2004-Jul-03