Blurb::
Newton method based least-squares calbration
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)
Topics::
package_optpp, local_optimization_methods
Examples::

Theory::

Faq::

See_Also::
