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 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 Parameters

• Best Objective Functions (when objective_functions) are specified)

• Calibration (when calibration_terms are specified)