Determines a local minimum of a function of n independent variables with the Conjugate Gradient method. Details
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accuracy controls the accuracy of the minimum. The method stops if two consecutive approximations differ no more than the value of accuracy. The default is 1E - 8. |
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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 minimization A value of 0 represents an algorithm without usage of the derivatives, a value of 1 represents an algorithm with usage of the derivatives. The default is 0. |
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Start is a point in n-dimension at which the optimization process starts. |
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X is an array of strings representing the X variables. |
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f(X) is the string representing the function of the X variables. |
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Minimum is the determined local minimum in n-dimension. |
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f(Minimum) is the function value of f(X) at the determined minimum. |
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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. |
The Fletcher Reeves and the Polak Ribiere algorithm are based on the determination of best-suited directions and 1D subminimizations.
The following diagram shows a start point and a start direction. New points and new directions are calculated by the Conjugate Gradient nD VI.