partitions
Samples variables on full factorial grid of study points
Topics
parameter_studies
Specification
Alias: None
Arguments: 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 \(11 \times 9 = 99\) function evaluations will be required.