Dakota Reference Manual: optpp_q_newton

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optpp_q_newton

Quasi-Newton optimization method

## Topics

This keyword is related to the topics:

- package_optpp
- local_optimization_methods

## Specification

Alias: none

Argument(s): 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_optpp 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)
- Best Nonlinear Constraints
- Calibration (when calibration_terms are specified)