Dakota Reference Manual  Version 6.12
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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:

See Also

These keywords may also be of interest: