Blurb::
Newton method based least-squares calbration
Description::
The Gauss-Newton algorithm is available as \c 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 \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_constraints 
- \ref hdf5_results-calibration (when \ref responses-calibration_terms are specified) 
- \ref hdf5_results-lsq_confidence_intervals (when 
\ref responses-calibration_terms-calibration_data-num_experiments equals 1) 

Topics::	package_optpp, local_optimization_methods
Examples::
Theory::
Faq::
See_Also::	method-optpp_cg, method-optpp_pds, method-optpp_fd_newton, method-optpp_newton, method-optpp_g_newton, responses-calibration_terms-field_calibration_terms
