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Find a Minimum nD (Conjugate Gradient » Formula) (G Dataflow)

Version:
Last Modified: September 22, 2017

Uses the conjugate gradient method to determine a local minimum of a function of n independent variables defined with formulas.

The conjugate gradient method uses the derivatives of a function to update the conjugate direction. This node uses the Fletcher-Reeves algorithm or the Polak-Ribiere algorithm to calculate the conjugate direction.

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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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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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start

Values of the variables at which the optimization starts.

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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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conjugate gradient settings

Algorithms this node uses to compute the derivatives and minimize the lines.

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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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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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stopping criteria

Conditions that terminate the optimization.

This node terminates the optimization if this node reaches all the tolerance thresholds or passes any of the maximum thresholds.

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function tolerance

Minimum relative change in function values between two internal iterations.

Definition of Relative Change in Function Values

The relative change in function values between two internal iterations is defined as follows:

abs ( f n f n 1 ) abs ( f n ) + ε

where

  • fn is the function value of the current iteration
  • fn - 1 is the function value of the previous iteration
  • ε is the machine epsilon

Default: 1E-06

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parameter tolerance

Minimum relative change in parameter values between two internal iterations.

Definition of Relative Change in Parameter Values

The relative change in parameter values between two internal iterations is defined as follows:

abs ( P n P n 1 ) abs ( P n ) + ε

where

  • Pn is the parameter value of the current iteration
  • Pn - 1 is the parameter value of the previous iteration
  • ε is the machine epsilon

Default: 1E-06

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gradient tolerance

Minimum 2-norm of the gradient.

Default: 1E-06

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maximum iterations

Maximum number of iterations that the node runs in the optimization.

Default: 100

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maximum function calls

Maximum number of calls to the objective function allowed in the optimization.

Default: 1000

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maximum time

Maximum amount of time in seconds allowed for the optimization.

Default: -1 — The optimization never times out.

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minimum

Values of the variables where the objective function has the local minimum.

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f(minimum)

Value of the objective function at minimum.

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function calls

Number of times that this node called the objective function(s) in the optimization.

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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: This product does not support FPGA devices

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


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