Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Samples variables along a userdefined vector
This keyword is related to the topics:
Alias: none
Argument(s): none
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Required (Choose One)  Group 1  final_point  Final variable values defining vector in vector parameter study  
step_vector  Size of step for each variable  
Required  num_steps  Number of sampling steps along the vector in a vector parameter study  
Optional  model_pointer  Identifier for model block to be used by a method 
Dakota's vector parameter study computes response data sets at selected intervals along a vector in parameter space. It is often used for singlecoordinate parameter studies (to study the effect of a single variable on a response set), but it can be used more generally for multiple coordinate vector studies (to investigate the response variations along some ndimensional vector such as an optimizer search direction).
Default Behavior
By default, the multidimensional parameter study operates over all types of variables.
Expected Outputs
A multidimensional parameter study produces a set of responses for each parameter set that is generated.
Usage Tips
Group 1 is used to define the vector along which the parameters are varied. Both cases also rely on the variables specification of an initial value, through:
From the initial value, the vector can be defined using one of the two keyword choices.
Once the vector is defined, the samples are then fully specifed by num_steps.
The following example is a good comparison to the examples on multidim_parameter_study and centered_parameter_study.
# tested on Dakota 6.0 on 140501 environment tabular_data tabular_data_file = 'rosen_vector.dat' method vector_parameter_study num_steps = 10 final_point = 2.0 2.0 model single variables continuous_design = 2 initial_point = 2.0 2.0 descriptors = 'x1' "x2" interface analysis_driver = 'rosenbrock' fork responses response_functions = 1 no_gradients no_hessians
These keywords may also be of interest: