Find a Minimum nD (Quasi-Newton » VI) (G Dataflow)

Arbitrary values passed to the strictly typed VI reference.

Strictly typed reference to the VI that implements the objective function.

Values of the variables at which the optimization starts.

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

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.

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:

$\frac{\mathrm{abs}(f_n-f_{n-1})}{\mathrm{abs}\left(f_n\right)+\epsilon }$

where

• f_n is the function value of the current iteration
• f_{n - 1} is the function value of the previous iteration
• ε is the machine epsilon

Default: 1E-06

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:

$\frac{\mathrm{abs}(P_n-P_{n-1})}{\mathrm{abs}\left(P_n\right)+\epsilon }$

where

• P_n is the parameter value of the current iteration
• P_{n - 1} is the parameter value of the previous iteration
• ε is the machine epsilon

Default: 1E-06

gradient tolerance

Minimum 2-norm of the gradient.

Default: 1E-06

maximum iterations

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

Default: 100

maximum function calls

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

Default: 1000

maximum time

Maximum amount of time in seconds allowed for the optimization.

Default: -1 — The optimization never times out.

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

Value of the objective function at minimum.

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

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.