Find All Minima nD VI
- 已更新2025-07-30
- 閱讀時間為 3 分鐘
Determines the minima of an n-dimension function in a given n-dimension interval.

Inputs/Outputs
accuracy
—
accuracy controls the accuracy of the minima. The default is 1.00E-8.
algorithm
—
algorithm specifies the method used by the VI. When algorithm is 0 it represents the Conjugate Gradient method. When algorithm is 1, it represents the Downhill Simplex method. The default is 0.
gradient method
—
gradient method specifies the algorithm used to compute the derivatives. A value of 0 represents the Fletcher Reeves method. A value of 1 represents the Polak Ribiere method. The default is 0.
line minimum
—
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.
number of trials
—
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.
Start
—
Start is the start point in n dimension.
End
—
End is the end point in n dimension.
X
—
X is an array of strings describing the n variables.
F(X)
—
F(X) is a string describing the n dimension function of X. The formula can contain any number of valid variables.
X Values
—
X Values is a matrix describing all determined local minima.
F Values
—
F Values is the function values at the points X Values.
ticks
—
ticks is the time in milliseconds for the whole calculation.
error
—
error returns any error or warning from the VI. You can wire error to the Error Cluster From Error Code VI to convert the error code or warning into an error cluster. |
Examples
Refer to the following example files included with LabVIEW.
- labview\examples\Mathematics\Optimization\2D Explorer.vi
accuracy
—
algorithm
—
number of trials
—
Start
—
X
—
F(X)
—
X Values
—
F Values
—
ticks
—
error
—