responses
Description of the model output data returned to Dakota upon evaluation of an interface.
Topics
block
Specification
Alias: None
Arguments: None
Child Keywords:
Required/Optional 
Description of Group 
Dakota Keyword 
Dakota Keyword Description 

Optional 
Name the responses block; helpful when there are multiple 

Optional 
Labels for the responses 

Required (Choose One) 
Response Type 
Response type suitable for optimization 

Response type suitable for calibration or least squares 

Generic response type 

Required (Choose One) 
Gradient Type 
Gradients will not be used 

Analysis driver will return gradients 

Gradients are needed and will be obtained from a mix of numerical and analytic sources 

Gradients are needed and will be approximated by finite differences 

Required (Choose One) 
Hessian Type 
Hessians will not be used 

Hessians are needed and will be approximated by finite differences 

Hessians are needed and will be approximated by secant updates (BFGS or SR1) from a series of gradient evaluations 

Hessians are needed and are available directly from the analysis driver 

Hessians are needed and will be obtained from a mix of numerical, analytic, and “quasi” sources 

Optional 
(Experimental) Labels for floating point response metadata 
Description
The responses
specification in a Dakota input file indicates the
types of data that can be returned by an interface when invoked during
Dakota’s execution. The specification includes three groups and two
optional keywords.
The response type group indicates the type and number of responses expected by Dakota. It must be one of three types:
Optimization: objective and constraint functions Calibration: calibration (least squares) terms and constraint functions Uncertainty Quantifiation: generic response functions
The response type specified should be consistent with the iterative technique called for in the method specification. Certain generalpurpose iterative techniques, such as parameter studies and design of experiments methods, can be used with any of these response types.
The gradient type group indicates the availability of first derivatives (gradient vectors) for the response functions.
The gradient specification also links back to the iterative method used. Gradients commonly are needed when the iterative study involves gradientbased optimization, local reliability analysis for uncertainty quantification, or local sensitivity analysis. They can optionally be used to build some types of surrogate models.
The Hessian type group specifies the availability of second derivatives (Hessian matrices) for the response functions.
Hessian availability for the response functions is similar to the gradient availability specifications, with the addition of support for “quasiHessians”. The Hessian specification also links back to the iterative method in use; Hessians commonly would be used in gradientbased optimization by full Newton methods or in reliability analysis with secondorder limit state approximations or secondorder probability integrations.
Examples
Several examples follow. The first example shows an optimization data set containing an objective function and two nonlinear inequality constraints. These three functions have analytic gradient availability and no Hessian availability.
responses
objective_functions = 1
nonlinear_inequality_constraints = 2
analytic_gradients
no_hessians
The next example shows a typical specification for a calibration data set. The six residual functions will have numerical gradients computed using the dakota finite differencing routine with central differences of 0.1% (plus/minus delta relative to current variables value = .001*value).
responses
calibration_terms = 6
numerical_gradients
method_source dakota
interval_type central
fd_gradient_step_size = .001
no_hessians
The last example shows a generic specification that could be used with a nondeterministic sampling iterator. The three response functions have no gradient or Hessian availability; therefore, only function values will be used by the iterator.
responses
response_functions = 3
no_gradients
no_hessians
Parameter study and design of experiments iterators are not restricted in terms of the response data sets which may be catalogued; they may be used with any of the function specification examples shown above.
Theory
Responses specify the total data set that is available for use by the method over the course of iteration. This is distinguished from the data subset described by an active set vector (see Dakota File Data Formats in the Users Manual [Adams et al., 2010]) indicating the particular subset of the response data needed for a particular function evaluation. Thus, the responses specification is a broad description of the data to be used during a study whereas the active set vector indicates the subset currently needed.