Dakota Reference Manual  Version 6.12
Explore and Predict with Confidence
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snowpac


Stochastic version of NOWPAC that incorporates error estimates and noise mitigation.

Specification

Alias: none

Argument(s): none

Child Keywords:

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Optional seed

Seed of the random number generator

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. The stochastic version is SNOWPAC, which incorporates noise estimates in its objective and inequality constraints. SNOWPAC modifies its trust region controls and adds smoothing from a Gaussian process surrogate in order to mitigate noise. SNOWPAC also supports a feasibility restoration mode, so it is not 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

Relative to the NOWPAC specification, SNOWPAC supports a seed control for repeatability of runs and also requires the return of error estimates from the underlying evaluator (e.g., UQ method such as Monte Carlo sampling).

method,
    snowpac
      seed = 2504
      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