Dakota Reference Manual: multipoint

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multipoint

Construct a surrogate from multiple existing training points

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Required (Choose One) | Multipoint Surrogate (Group 1) | tana | Local multi-point model via two-point nonlinear approximation
qmea | Multi-point surrogate approximation based on QMEA algorithm
| Required | actual_model_pointer | Pointer to specify a "truth" model, from which to construct a surrogate

## Description

Multipoint approximations use data from previous design points to improve the accuracy of local approximations. The data often comes from the current and previous iterates of a minimization algorithm.

Currently, only the Two-point Adaptive Nonlinearity Approximation (TANA-3) method of [95] is supported with the `tana` keyword.

The truth model to be used to generate the value/gradient data used in the approximation is identified through the required `actual_model_pointer` specification.

## See Also

These keywords may also be of interest:

- local
- global
- hierarchical