.. _method-multifidelity_polynomial_chaos-allocation_control:

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allocation_control
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Sample allocation approach for multifidelity expansions


.. toctree::
   :hidden:
   :maxdepth: 1

   method-multifidelity_polynomial_chaos-allocation_control-greedy


**Specification**

- *Alias:* None

- *Arguments:* None


**Child Keywords:**

+-------------------------+--------------------+--------------------+---------------------------------------------+
| Required/Optional       | Description of     | Dakota Keyword     | Dakota Keyword Description                  |
|                         | Group              |                    |                                             |
+=========================+====================+====================+=============================================+
| Required (Choose One)   | Multifidelity      | `greedy`__         | Sample allocation based on greedy           |
|                         | Sample Allocation  |                    | refinement within multifidelity polynomial  |
|                         | Control            |                    | chaos                                       |
+-------------------------+--------------------+--------------------+---------------------------------------------+

.. __: method-multifidelity_polynomial_chaos-allocation_control-greedy.html



**Description**


Multifidelity surrogate approaches, including polynomial chaos,
stochastic collocation, and function train, can optionally employ a
integrated greedy competition across the model sequence, where each
model index can supply one or more refinement candidates which are
competed to determine the candidate with the greatest impact on the
QoI statistics per unit cost.  This greedy competition implicitly
determines the optimal sample allocation across model indices.

*Default Behavior*

The default, when ``allocation_control`` is not specified, is to
compute or adapt separately for each model index (individual instead
of integrated refinement).