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Find Multiple Minima nD (Conjugate Gradient) (G Dataflow)

Last Modified: January 12, 2018

Determines multiple minima of an n-dimension function in a given interval using the conjugate gradient method.

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line minimization

Value that determines whether this node uses the derivatives in the algorithm.

Name Value Description
Without Derivatives 0 Does not use the derivatives in the algorithm.
With Derivatives 1 Uses the derivatives in the algorithm.

Default: Without Derivatives

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gradient method

Algorithm this node uses to compute the derivatives.

Name Value Description
Fletcher-Reeves 0 Uses the Fletcher-Reeves method.
Polak-Ribiere 1 Uses the Polak-Ribiere method.

Default: Fletcher-Reeves

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objective function

Formula that defines the objective function. The formula can contain any number of valid variables

Entering Valid Variables

This node accepts variables that use the following format rule: variables must start with a letter or an underscore followed by any number of alphanumeric characters or underscores.

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variables

Names of the variables.

Variable names must start with a letter or an underscore followed by any number of alphanumeric characters or underscores.

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start

Point in n dimension at which the optimization starts.

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end

Point in n dimension at which the optimization ends.

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error in

Error conditions that occur before this node runs.

The node responds to this input according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Default: No error

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accuracy

Accuracy of the minimum of the objective function.

Default: 1E-08

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number of trials

Number of the randomly chosen start points of the optimization. These points belong to the interval (start, end).

Default: 5

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minima

Determined values of the variables where the objective function has the local minimum value. Each row contains n elements that represent the values of the n variables.

The values are an approximation of the variables where the objective function has the local minimum value.

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f(minima)

Values of the objective function at each row of minima.

The values are an approximation of the actual minimum value of the objective function.

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error out

Error information.

The node produces this output according to standard error behavior.

Standard Error Behavior

Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.

error in does not contain an error error in contains an error
If no error occurred before the node runs, the node begins execution normally.

If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

Where This Node Can Run:

Desktop OS: Windows

FPGA: Not supported

Web Server: Not supported in VIs that run in a web application


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