Blurb::
Finite Difference Newton optimization method
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 \c optpp methods.

<b>Expected HDF5 Output</b> 

If Dakota was built with HDF5 support and run with the 
\ref 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 \ref responses-objective_functions) are specified) 
- \ref hdf5_results-best_constraints 
- \ref hdf5_results-calibration (when \ref responses-calibration_terms are specified) 

Topics::	package_optpp, local_optimization_methods
Examples::
Theory::
Faq::
See_Also::	method-optpp_cg, method-optpp_g_newton, method-optpp_pds, method-optpp_newton, method-optpp_g_newton
