Dakota Reference Manual  Version 6.1
Large-Scale Engineering Optimization and Uncertainty Analysis
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vector_parameter_study

Samples variables along a user-defined vector

Topics

This keyword is related to the topics:

Specification

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

Number of sampling steps along the vector in a vector parameter study

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

Description

Dakota's vector parameter study computes response data sets at selected intervals along a vector in parameter space. It is often used for single-coordinate 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 n-dimensional 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:

  • the initial_point keyword
  • the initial_state keyword
  • relying on the default initial value, based on the rest of the variables specification

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.

Examples

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

See Also

These keywords may also be of interest: