optpp_q_newton

Quasi-Newton 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

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

This is a Newton method that expects a gradient and computes a low-rank approximation to the Hessian. Each of the Newton-based methods are automatically bound to the appropriate OPT++ algorithm based on the user constraint specification (unconstrained, bound-constrained, or generally-constrained). In the generally-constrained case, the Newton methods use a nonlinear interior-point approach to manage the constraints.

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: