Conjugate Gradient nD (Not in Base Package)

Determines a local minimum of a function of n independent variables with the Conjugate Gradient method. Details

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.
gradient method A value of 0 represents the Fletcher Reeves method, a value of 1 represents the Polak Ribiere method. The default is 0.
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.
Start is a point in n-dimension at which the optimization process starts.
X is an array of strings representing the X variables.
f(X) is the string representing the function of the X variables.
Minimum is the determined local minimum in n-dimension.
f(Minimum) is the function value of f(X) at the determined minimum.
ticks is the time in milliseconds for the whole calculation.
error returns any error or warning condition from the VI.

Conjugate Gradient nD Details

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.