optpp_cg
A conjugate gradient optimization method
Topics
package_optpp, local_optimization_methods
Specification
Alias: None
Arguments: None
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
---|---|---|---|
Optional |
Max change in design point |
||
Optional |
Stopping critiera based on L2 norm of gradient |
||
Optional |
Number of iterations allowed for optimizers and adaptive UQ methods |
||
Optional |
Stopping criterion based on objective function or statistics convergence |
||
Optional |
Compute speculative gradients |
||
Optional |
Number of function evaluations allowed for optimizers |
||
Optional |
Turn on scaling for variables, responses, and constraints |
||
Optional |
Identifier for model block to be used by a method |
Description
The conjugate gradient method is an implementation of the Polak-Ribiere approach and handles only unconstrained problems.
See Package: OPT++ 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:
Best Objective Functions (when
objective_functions
) are specified)Calibration (when
calibration_terms
are specified)