Dakota Reference Manual: allocation_control

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allocation_control

Sample allocation approach for multifidelity expansions

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Required (Choose One) | Multifidelity Sample Allocation Control (Group 1) | greedy | Sample allocation based on greedy refinement within multifidelity function train

## 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).