.. _method-optpp_g_newton:

""""""""""""""
optpp_g_newton
""""""""""""""


Newton method based least-squares calbration



**Topics**


package_optpp, local_optimization_methods


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

   method-optpp_g_newton-search_method
   method-optpp_g_newton-merit_function
   method-optpp_g_newton-steplength_to_boundary
   method-optpp_g_newton-centering_parameter
   method-optpp_g_newton-max_step
   method-optpp_g_newton-gradient_tolerance
   method-optpp_g_newton-max_iterations
   method-optpp_g_newton-convergence_tolerance
   method-optpp_g_newton-speculative
   method-optpp_g_newton-max_function_evaluations
   method-optpp_g_newton-scaling
   method-optpp_g_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_g_newton-search_method.html
__ method-optpp_g_newton-merit_function.html
__ method-optpp_g_newton-steplength_to_boundary.html
__ method-optpp_g_newton-centering_parameter.html
__ method-optpp_g_newton-max_step.html
__ method-optpp_g_newton-gradient_tolerance.html
__ method-optpp_g_newton-max_iterations.html
__ method-optpp_g_newton-convergence_tolerance.html
__ method-optpp_g_newton-speculative.html
__ method-optpp_g_newton-max_function_evaluations.html
__ method-optpp_g_newton-scaling.html
__ method-optpp_g_newton-model_pointer.html



**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 :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_constraints`
- :ref:`hdf5_results-calibration` (when :dakkw:`responses-calibration_terms` are specified)
- :ref:`hdf5_results-lsq_confidence_intervals` (when :dakkw:`responses-calibration_terms-calibration_data-num_experiments` equals 1)