Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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Local Surrogate Based Optimization


This keyword is related to the topics:


Alias: none

Argument(s): none

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
(Choose One)
Group 1 method_pointer

Pointer to sub-method to apply to a surrogate or branch-and-bound sub-problem

method_name Specify sub-method by name
Required model_pointer

Identifier for model block to be used by a method

Optional soft_convergence_limit Limit number of iterations w/ little improvement
Optional truth_surrogate_bypass Bypass lower level surrogates when performing truth verifications on a top level surrogate
Optional trust_region Use trust region search method
Optional approx_subproblem Identify functions to be included in surrogate merit function
Optional merit_function Select type of penalty or merit function
Optional acceptance_logic Set criteria for trusted surrogate
Optional constraint_relax Enable constraint relaxation
Optional max_iterations

Stopping criterion based on number of iterations

Optional convergence_tolerance

Stopping criterion based on convergence of the objective function or statistics

Optional constraint_tolerance The maximum allowable value of constraint violation still considered to be feasible


In surrogate-based optimization (SBO) and surrogate-based nonlinear least squares (SBNLS), minimization occurs using a set of one or more approximations, defined from a surrogate model, that are built and periodically updated using data from a "truth" model. The surrogate model can be a global data fit (e.g., regression or interpolation of data generated from a design of computer experiments), a multipoint approximation, a local Taylor Series expansion, or a model hierarchy approximation (e.g., a low-fidelity simulation model), whereas the truth model involves a high-fidelity simulation model. The goals of surrogate-based methods are to reduce the total number of truth model simulations and, in the case of global data fit surrogates, to smooth noisy data with an easily navigated analytic function.

In the surrogate-based local method, a trust region approach is used to manage the minimization process to maintain acceptable accuracy between the surrogate model and the truth model (by limiting the range over which the surrogate model is trusted). The process involves a sequence of minimizations performed on the surrogate model and bounded by the trust region. At the end of each approximate minimization, the candidate optimum point is validated using the truth model. If sufficient decrease has been obtained in the truth model, the trust region is re-centered around the candidate optimum point and the trust region will either shrink, expand, or remain the same size depending on the accuracy with which the surrogate model predicted the truth model decrease. If sufficient decrease has not been attained, the trust region center is not updated and the entire trust region shrinks by a user-specified factor. The cycle then repeats with the construction of a new surrogate model, a minimization, and another test for sufficient decrease in the truth model. This cycle continues until convergence is attained.


For surrogate_based_local problems with nonlinear constraints, a number of algorithm formulations exist as described in[21] and as summarized in the Advanced Examples section of the Models chapter of the Users Manual[5].

See Also

These keywords may also be of interest: