Blurb::
Quasi-Newton optimization 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 :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::
