Blurb::
Newton method based optimization
Description::
This is a full Newton method that expects a gradient and a 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)
Topics::
package_optpp, local_optimization_methods
Examples::

Theory::

Faq::

See_Also::
