Dakota Reference Manual: nowpac

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nowpac

Gradient-free inequality-constrained optimization using Nonlinear Optimization With Path Augmented Constraints (NOWPAC).

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| 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

## 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

    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