.. _method-optpp_fd_newton:

"""""""""""""""
optpp_fd_newton
"""""""""""""""


Finite Difference Newton optimization method



**Topics**


package_optpp, local_optimization_methods


.. toctree::
   :hidden:
   :maxdepth: 1

   method-optpp_fd_newton-search_method
   method-optpp_fd_newton-merit_function
   method-optpp_fd_newton-steplength_to_boundary
   method-optpp_fd_newton-centering_parameter
   method-optpp_fd_newton-max_step
   method-optpp_fd_newton-gradient_tolerance
   method-optpp_fd_newton-max_iterations
   method-optpp_fd_newton-convergence_tolerance
   method-optpp_fd_newton-speculative
   method-optpp_fd_newton-max_function_evaluations
   method-optpp_fd_newton-scaling
   method-optpp_fd_newton-model_pointer


**Specification**

- *Alias:* None

- *Arguments:* None


**Child Keywords:**

+-------------------------+--------------------+------------------------------+-----------------------------------------------+
| Required/Optional       | Description of     | Dakota Keyword               | Dakota Keyword Description                    |
|                         | Group              |                              |                                               |
+=========================+====================+==============================+===============================================+
| 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                                        |
+----------------------------------------------+------------------------------+-----------------------------------------------+

.. __: method-optpp_fd_newton-search_method.html
__ method-optpp_fd_newton-merit_function.html
__ method-optpp_fd_newton-steplength_to_boundary.html
__ method-optpp_fd_newton-centering_parameter.html
__ method-optpp_fd_newton-max_step.html
__ method-optpp_fd_newton-gradient_tolerance.html
__ method-optpp_fd_newton-max_iterations.html
__ method-optpp_fd_newton-convergence_tolerance.html
__ method-optpp_fd_newton-speculative.html
__ method-optpp_fd_newton-max_function_evaluations.html
__ method-optpp_fd_newton-scaling.html
__ method-optpp_fd_newton-model_pointer.html



**Description**


This is a Newton method that expects a gradient and computes a
finite-difference 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 :ref:`topic-package_optpp` for info related to all ``optpp`` methods.

*Expected HDF5 Output*

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


- :ref:`hdf5_results-best_params`
- :ref:`hdf5_results-best_obj_fncs` (when :dakkw:`responses-objective_functions`) are specified)
- :ref:`hdf5_results-best_constraints`
- :ref:`hdf5_results-calibration` (when :dakkw:`responses-calibration_terms` are specified)


