Dakota Reference Manual: partitions

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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partitions

Samples variables on full factorial grid of study points

## Topics

This keyword is related to the topics:

- parameter_studies

## Specification

Alias: none

Argument(s): INTEGERLIST

## Description

Dakota's multidimensional parameter study computes response data sets for an n-dimensional grid of points. Each continuous and discrete range variable is partitioned into equally spaced intervals between its upper and lower bounds, each discrete set variable is partitioned into equally spaced index intervals. The partition boundaries in n-dimensional space define a grid of points, and every point is evaluated.

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

Since the initial values from the variables specification will not be used, they need not be specified.

## Examples

This example is taken from the Users Manual and is a good comparison to the examples on centered_parameter_study and vector_parameter_study.

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

    method
      multidim_parameter_study
        partitions = 10 8

    model
      single

    variables
      continuous_design = 2
        lower_bounds     -2.0     -2.0
        upper_bounds      2.0      2.0
        descriptors       'x1'     "x2"

    interface
      analysis_driver = 'rosenbrock'
        fork

    responses
      response_functions = 1
      no_gradients
      no_hessians

This example illustrates the full factorial combinations of parameter values created by the multidim_parameter_study. With 10 and 8 partitions, there are actually 11 and 9 values for each variable. This means that function evaluations will be required.

## See Also

These keywords may also be of interest:

- centered_parameter_study
- list_parameter_study
- vector_parameter_study