Dakota Reference Manual: vector_parameter_study

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

- Main Page
- Related Pages

All Pages
vector_parameter_study

Samples variables along a user-defined vector

## Topics

This keyword is related to the topics:

- parameter_studies

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Required (Choose One) | Step Control (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

## 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 vector parameter study operates over all types of variables.

Expected Outputs

A vector parameter study produces a set of responses for each parameter set that is generated.

Expected HDF5 Output

If Dakota was built with HDF5 support and run with the hdf5 keyword, this method writes the following results to HDF5:

- Parameter Sets

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:

- centered_parameter_study
- multidim_parameter_study
- list_parameter_study