Blurb::
Gradient-free inequality-constrained optimization using Nonlinear Optimization With Path Augmented Constraints (NOWPAC).
Description::
NOWPAC is a provably-convergent gradient-free optimization method from MIT that solves a series of trust region surrogate-based subproblems to generate improving steps.  Due to its use of an interior penalty scheme and enforcement of strict feasibility, it does not support linear or nonlinear *equality* constraints.  As opposed to the stochastic version (SNOWPAC), NOWPAC does not currently support a feasibility restoration mode, so it is necessary to start from a feasible design.

Note: (S)NOWPAC is not configured with Dakota by default and requires a separate installation of the NOWPAC distribution from MIT, combined with its TPLs of Eigen and NLOPT.
Topics::

Examples::

.. code-block::

    method
      nowpac
        max_function_evaluations = 1000
        convergence_tolerance = 1e-4
        trust_region
          initial_size = 0.10
          minimum_size = 1.0e-6
          contract_threshold = 0.25
          expand_threshold   = 0.75
          contraction_factor = 0.50
          expansion_factor   = 1.50


Theory::

Faq::

See_Also::
