Dakota Reference Manual: ego

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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ego

Use the Efficient Global Optimization method

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional | gaussian_process | Gaussian Process surrogate model
| Optional | use_derivatives | Use derivative data to construct surrogate models
| Optional | import_build_points_file | File containing points you wish to use to build a surrogate
| Optional | export_approx_points_file | Output file for surrogate model value evaluations

## Description

In the case of `ego`, the efficient global optimization (EGO) method is used to calculate bounds. By default, the Surfpack GP (Kriging) model is used, but the Dakota implementation may be selected instead. If `use_derivatives` is specified the GP model will be built using available derivative data (Surfpack GP only).

See efficient_global for more information.