.. _method-nowpac: """""" nowpac """""" Gradient-free inequality-constrained optimization using Nonlinear Optimization With Path Augmented Constraints (NOWPAC). .. toctree:: :hidden: :maxdepth: 1 method-nowpac-trust_region method-nowpac-max_iterations method-nowpac-max_function_evaluations method-nowpac-scaling method-nowpac-model_pointer **Specification** - *Alias:* None - *Arguments:* None **Child Keywords:** +-------------------------+--------------------+------------------------------+---------------------------------------------+ | Required/Optional | Description of | Dakota Keyword | Dakota Keyword Description | | | Group | | | +=========================+====================+==============================+=============================================+ | Optional | `trust_region`__ | Use trust region as the globalization | | | | strategy. | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `max_iterations`__ | Number of iterations allowed for optimizers | | | | and adaptive UQ methods | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `max_function_evaluations`__ | Number of function evaluations allowed for | | | | optimizers | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `scaling`__ | Turn on scaling for variables, responses, | | | | and constraints | +----------------------------------------------+------------------------------+---------------------------------------------+ | Optional | `model_pointer`__ | Identifier for model block to be used by a | | | | method | +----------------------------------------------+------------------------------+---------------------------------------------+ .. __: method-nowpac-trust_region.html __ method-nowpac-max_iterations.html __ method-nowpac-max_function_evaluations.html __ method-nowpac-scaling.html __ method-nowpac-model_pointer.html **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. **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