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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Gradients are needed and will be obtained from a mix of numerical and analytic sources
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 |
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.
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).
These keywords may also be of interest: