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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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A conjugate gradient optimization method
This keyword is related to the topics:
Alias: none
Argument(s): none
Child Keywords:
Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
---|---|---|---|---|
Optional | max_step | Max change in design point | ||
Optional | gradient_tolerance | Stopping critiera based on L2 norm of gradient | ||
Optional | max_iterations | Number of iterations allowed for optimizers and adaptive UQ methods | ||
Optional | convergence_tolerance | Stopping criterion based on objective function or statistics convergence | ||
Optional | speculative | Compute speculative gradients | ||
Optional | max_function_evaluations | Number of function evaluations allowed for optimizers | ||
Optional | scaling | Turn on scaling for variables, responses, and constraints | ||
Optional | model_pointer | Identifier for model block to be used by a method |
The conjugate gradient method is an implementation of the Polak-Ribiere approach and handles only unconstrained problems.
See package_optpp for info related to all optpp
methods.
Expected HDF5 Output
If Dakota was built with HDF5 support and run with the hdf5 keyword, this method writes the following results to HDF5:
These keywords may also be of interest: