Optimization Object Configuration

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Description

This functions configures the current optimization object with the given value for the given key.

Usage

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  optimbase.set(this = NULL, key = NULL, value = NULL)
  optimbase.histset(this = NULL, iter = NULL, key = NULL, value = NULL)

Arguments

this

The current optimization object.

key

The key to configure. See details for the list of possible keys.

value

The value to assign to the key.

iter

The iteration at which the data must be stored.

Details

optimbase.set set the content of the key element of the optimization object this to value.

The only available keys in optimbase.set are the following:

'verbose'

Set to 1 to enable verbose logging.

'x0'

The initial guesses, as a n x 1 column vector, where n is the number of variables.

'fx0'

The value of the cost function at the initial point estimate.

'xopt'

The optimum point estimate.

'fopt'

The value of the cost function at the optimum point estimate.

'tolfunabsolute'

The absolute tolerance for the function value.

'tolfunrelative'

The relative tolerance for the function value.

'tolfunmethod'

The method used for the tolerance on function value in the termination criteria. The following values are available: TRUE, FALSE. If this criteria is triggered, the status of the optimization is set to 'tolf'.

'tolxabsolute'

The absolute tolerance on x.

'tolxrelative'

The relative tolerance on x.

'tolxmethod'

The method used for the tolerance on x in the termination criteria. The following values are available: TRUE, FALSE. If this criteria is triggered during optimization, the status of the optimization is set to 'tolx'.

'maxfunevals'

The maximum number of function evaluations. If this criteria is triggered during optimization, the status of the optimization is set to 'maxfuneval' (see vignette('optimbase',package='optimbase') for more details).

'funevals'

The number of function evaluations.

'maxiter'

The maximum number of iterations. If this criteria is triggered during optimization, the status of the optimization is set to 'maxiter' (see vignette('optimbase',package='optimbase') for more details).

'iterations'

The number of iterations.

'function'

The objective function, which computes the value of the cost function and the non linear constraints, if any. See vignette('optimbase',package='optimbase') for the details of the communication between the optimization system and the cost function.

'status'

A string containing the status of the optimization.

'historyxopt'

A list, with nbiter element, containing the history of x during the iterations. This list is available after optimization if the history storing was enabled with the storehistory element.

'historyfopt'

An vector, with nbiter values, containing the history of the function value during the iterations. This vector is available after optimization if the history storing was enabled with the storehistory element.

'verbosetermination'

Set to 1 to enable verbose termination logging.

'outputcommand'

A command which is called back for output. Details of the communication between the optimization system and the output command function are provided in vignette('optimbase',package='optimbase').

'outputcommandarg'

An additionnal argument, passed to the output command.

'numberofvariables'

The number of variables to optimize.

'storehistory'

Set to TRUE to enable the history storing.

'costfargument'

An additionnal argument, passed to the cost function.

'boundsmin'

The minimum bounds for the parameters.

'boundsmax'

The maximum bounds for the parameters.

'nbineqconst'

The number of inequality constraints.

'logfile'

The name of the log file.

'logfilehandle'

Set to 1 if logging has been started

'logstartup'

Set to 1 if logging has been started

'withderivatives'

Set to TRUE if the algorithm uses derivatives.

The only available keys in optimbase.histset are 'historyxopt' and 'historyfopt'. Contrary to optimbase.set, this function only alters the value of historyxopt and historyfopt at the specific iteration iter.

Value

An updated optimization object.

Author(s)

Author of Scilab optimbase module: Michael Baudin (INRIA - Digiteo)

Author of R adaptation: Sebastien Bihorel (sb.pmlab@gmail.com)

See Also

optimbase