Blurb::
Samples variables along a user-defined vector
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
:dakkw:`environment-results_output-hdf5` keyword, this method
writes the following results to HDF5:


- :ref:`hdf5_results-pstudies`

*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 :dakkw:`variables-continuous_design-initial_point` keyword
- the :dakkw:`variables-continuous_state-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 :dakkw:`method-vector_parameter_study-num_steps`.
Topics::
parameter_studies
Examples::
The following example is a good comparison to the examples on
:dakkw:`method-multidim_parameter_study` and :dakkw:`method-centered_parameter_study`.

.. code-block::

    # 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
    


Theory::

Faq::

See_Also::
