Determines the minima of an n-dimension function in a given n-dimension interval.
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accuracy controls the accuracy of the minima. The default is 1E - 8. |
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When algorithm is 0 it represents the Conjugate Gradient method. When algorithm is 1, it represents the Downhill Simplex method. The default is 0. |
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gradient method A value of 0 represents the Fletcher Reeves method, a value of 1 represents the Polak Ribiere method. The default is 0. |
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line minimum A value of 0 represents the line optimization without derivatives. A line minimum value of 1 represents the line optimization with derivatives. The default is 0. |
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number of trials is the number of the randomly chosen start points of the optimization process. These points belong to the interval (start,end). The default is 5. |
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Start is the start point in n D. |
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End is the end point in n-dimension. |
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X is an array of strings describing the n variables. |
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F(X) is a string describing the n-dimension function of X. |
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X Values is a matrix describing all determined local minima. |
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F Values is the function values at the points X Values. |
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ticks is the time in milliseconds for the whole calculation. |
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error returns any error or warning condition from the VI. |