Dakota Reference Manual: mixed_gradients

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mixed_gradients

Gradients are needed and will be obtained from a mix of numerical and analytic sources

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Required | id_numerical_gradients | Identify which numerical gradient corresponds to which response
| Required | id_analytic_gradients | Identify which analytical gradient corresponds to which response
| Optional | method_source | Specify which finite difference routine is used
| Optional (Choose One) | Gradient Source (Group 1) | dakota | (Default) Use internal Dakota finite differences algorithm
vendor | Use non-Dakota fd algorithm
| Optional | interval_type | Specify how to compute gradients and hessians
| Optional (Choose One) | Finite Difference Type (Group 2) | forward | (Default) Use forward differences
central | Use central differences
| Optional | fd_step_size | Step size used when computing gradients and Hessians

## Description

The `mixed_gradients` specification means that some gradient information is available directly from the simulation (analytic) whereas the rest will have to be finite differenced (numerical). This specification allows the user to make use of as much analytic gradient information as is available and then finite difference for the rest.

The `method_source`, `interval_type`, and `fd_gradient_step_size` specifications pertain to those functions listed by the `id_numerical_gradients` list.

## Examples

For example, the objective function may be a simple analytic function of the design variables (e.g., weight) whereas the constraints are nonlinear implicit functions of complex analyses (e.g., maximum stress).

## See Also

These keywords may also be of interest:

- numerical_gradients
- no_gradients
- analytic_gradients