optpp_g_newton

Newton method based least-squares calbration

Topics

package_optpp, local_optimization_methods

Specification

  • Alias: None

  • Arguments: None

Child Keywords:

Required/Optional

Description of Group

Dakota Keyword

Dakota Keyword Description

Optional

search_method

Select a search method for Newton-based optimizers

Optional

merit_function

Balance goals of reducing objective function and satisfying constraints

Optional

steplength_to_boundary

Controls how close to the boundary of the feasible region the algorithm is allowed to move

Optional

centering_parameter

Controls how closely the algorithm should follow the “central path”

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 Gauss-Newton algorithm is available as optpp_g_newton and supports unconstrained, bound-constrained, and generally-constrained problems. When interfaced with the unconstrained, bound-constrained, and nonlinear interior point full-Newton optimizers from the OPT++ library, it provides a Gauss-Newton least squares capability which – on zero-residual test problems – can exhibit quadratic convergence rates near the solution. (Real problems almost never have zero residuals, i.e., perfect fits.)

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: