Dakota Reference Manual: list_parameter_study

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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list_parameter_study

Samples variables as a specified values

## 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) | Points Source (Group 1) | list_of_points | List of variable values to evaluate in a list parameter study
import_points_file | File containing list of variable values to evaluate in a list parameter study
| Optional | model_pointer | Identifier for model block to be used by a method

## Description

Dakota's list parameter study allows for evaluations at user selected points of interest.

Default Behavior

By default, the list parameter study operates over all types of variables.

The number of real values in the `list_of_points` specification or file referenced by `import_points_file` must be a multiple of the total number of variables (including continuous and discrete types) contained in the variables specification.

Expected Outputs

A list parameter study produces a set of responses for each parameter set that is specified.

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

- This parameter study simply performs simulations for the first parameter set (the first `n` entries in the list), followed by the next parameter set (the next `n` entries), and so on, until the list of points has been exhausted.
- Since the initial values from the variables specification will not be used, they need not be specified.
- When the points are specified in the Dakota input file using the `list_of_points` keyword, discrete set values must be referred to using 0-based indexes, and not the values themselves. However, when using `import_points_file`, refer to discrete set values directly, not by index.
- For both `list_of_points` and `import_points_file`, Dakota expects the values for each evaluation to be ordered by type. The type ordering matches that of the variables section of this Reference Manual and of the listing in the Parameters file format section of the Dakota User's Manual[4]. When multiple variables are present for a single type, the ordering within that type must match the order specified by the user in the variables section of her input file.

## Examples

This shows the method and variables block of a Dakota input file that runs a list_parameter_study.

    method
      list_parameter_study
        list_of_points =
                     3.1e6    0.0029    0.31
                     3.2e6    0.0028    0.32
                     3.3e6    0.0027    0.34
                     3.3e6    0.0026    0.36

    variables
      continuous_design = 3
        descriptors = 'E'    'MASS'     'DENSITY'

Note that because of the way Dakota treats whitespace, the above example is equivalent to:

    method
      list_parameter_study
        list_of_points =
    3.1e6    0.0029    0.31  3.2e6    0.0028
    0.32    3.3e6    0.0027
    0.34  3.3e6    0.0026    0.36

    variables
      continuous_design = 3
        descriptors = 'E'  'MASS'   'DENSITY'

Although the first example is much more readable.

And here's a full input file:

    # tested on Dakota 6.0 on 140501
    environment
      tabular_data
        tabular_data_file 'List_param_study.dat'

    method
      list_parameter_study
        list_of_points =     0.1    0.1
                             0.2    0.1
                             0.3    0.0
                             0.3    1.0

    model
      single

    variables
      active design
      continuous_design = 2
        descriptors    'x1' 'x2'
      continuous_state = 1
        descriptors =   'constant1'
        initial_state = 100

    interface
      analysis_drivers 'text_book'
        fork
      asynchronous
        evaluation_concurrency 2

    responses
      response_functions = 1
      no_gradients
      no_hessians

This example illustrates the list_parameter_study.

- The function evaluations are independent, so any level of `evaluation_concurrency` can be used
- Default behavior for parameter studies is to iterate on all variables. However, because `active` `design` is specified, this study will only iterate on the `continuous_design` variables.

## See Also

These keywords may also be of interest:

- centered_parameter_study
- multidim_parameter_study
- vector_parameter_study