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_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

Description

The conjugate gradient method is an implementation of the Polak-Ribiere approach and handles only unconstrained problems.

See topic-package_optpp for info related to all optpp methods.

Expected HDF5 Output

If Dakota was built with HDF5 support and run with the environment-results_output-hdf5 keyword, this method writes the following results to HDF5:

• hdf5_results-best_params

• hdf5_results-best_obj_fncs (when responses-objective_functions) are specified)

• hdf5_results-calibration (when responses-calibration_terms are specified)