Set the active variables view a method will see


  • Alias: None

  • Arguments: None

  • Default: Infer from response or method specification

Child Keywords:


Description of Group

Dakota Keyword

Dakota Keyword Description

Required (Choose One)

Active Variables


Option for the active keyword


Option for the active keyword


Option for the active keyword


Option for the active keyword


Option for the active keyword


Option for the active keyword


There are certain situations where the user may want to explicitly control the subset of variables that is considered active for a certain Dakota method. This is done by specifying the keyword active in the variables specification block, followed by one of the following: all, design, uncertain, aleatory, epistemic, or state.

Specifying one of these subsets of variables will allow the Dakota method to operate on the specified variable types and override the default active subset.

If the user does not specify any explicit override of the active view of the variables, Dakota first considers the response function specification.

  • If the user specifies objectivefunctions or calibration terms in the response specification block, then we can infer that the active variables should be the design variables (since design variables are used within optimization and least squares methods).

  • If the user instead specifies the genericresponse type of response_functions, then Dakota cannot infer the active variable subset from the response specification and will instead infer it from the method selection.

    1. If the method is a parameter study, or any of the methods available under dace, psuade, or fsu methods, the active view is set to all variables.

    2. For uncertainty quantification methods, if the method is sampling, then the view is set to aleatory if only aleatory variables are present, epistemic if only epistemic variables are present, or uncertain (covering both aleatory and epistemic) if both are present.

    3. If the uncertainty method involves interval estimation or evidence calculations, the view is set to epistemic.

    4. For other uncertainty quantification methods not mentioned in the previous sentences (e.g., reliability methods or stochastic expansion methods), the default view is set to aleatory.

    5. Finally, for verification studies using the Richardson extrapolation method, the active view is set to state.

    6. Note that in surrogate-based optimization, where the surrogate is built on points defined by the method defined by the dace_method_pointer, the sampling used to generate the points is performed only over the design variables as a default unless otherwise specified (e.g. state variables will not be sampled for surrogate construction).

As alluded to in the previous section, the iterative method selected for use in Dakota determines what subset, or view, of the variables data is active in the iteration. The general case of having a mixture of various different types of variables is supported within all of the Dakota methods even though certain methods will only modify certain types of variables (e.g., optimizers and least squares methods only modify design variables, and uncertainty quantification methods typically only utilize uncertain variables). This implies that variables which are not under the direct control of a particular iterator will be mapped through the interface in an unmodified state. This allows for a variety of parameterizations within the model in addition to those which are being used by a particular iterator, which can provide the convenience of consolidating the control over various modeling parameters in a single file (the Dakota input file). An important related point is that the variable set that is active with a particular iterator is the same variable set for which derivatives are typically computed.


For example, the default behavior for a nondeterministic sampling method is to sample the uncertain variables. However, if the user specifed active all in the variables specification block, the sampling would be performed over all variables (e.g. design and state variables in addition to the uncertain variables). This may be desired in situations such as surrogate-based optimization under uncertainty, where a surrogate may be constructed to span both design and uncertain variables. This is an example where we expand the active subset beyond the default, but in other situations, we may wish to restrict from the default. An example of this would be performing design of experiments in the presence of multiple variable types (for which all types are active by default), but only wanting to sample over the design variables for purposes of constructing a surrogate model for optimization.


The optional status of the different variable type specifications allows the user to specify only those variables which are present (rather than explicitly specifying that the number of a particular type of variables is zero). However, at least one type of variables that are active for the iterator in use must have nonzero size or an input error message will result.