Class for using global nongradient-based optimization approaches to calculate interval bounds for epistemic uncertainty quantification. More...
Inheritance diagram for NonDGlobalInterval:
Public Member Functions | |
| NonDGlobalInterval (ProblemDescDB &problem_db, Model &model) | |
| constructor | |
| ~NonDGlobalInterval () | |
| destructor | |
| void | derived_init_communicators (ParLevLIter pl_iter) |
| derived class contributions to initializing the communicators associated with this Iterator instance | |
| void | derived_set_communicators (ParLevLIter pl_iter) |
| derived class contributions to setting the communicators associated with this Iterator instance | |
| void | derived_free_communicators (ParLevLIter pl_iter) |
| derived class contributions to freeing the communicators associated with this Iterator instance | |
| void | quantify_uncertainty () |
| Performs an optimization to determine interval bounds for an entire function or interval bounds on a particular statistical estimator. | |
| const Model & | algorithm_space_model () const |
Protected Member Functions | |
| virtual void | initialize () |
| perform any required initialization | |
| virtual void | set_cell_bounds () |
| set the optimization variable bounds for each cell | |
| virtual void | get_best_sample (bool maximize, bool eval_approx) |
| determine truthFnStar and approxFnStar | |
| virtual void | post_process_cell_results (bool maximize) |
| post-process a cell minimization/maximization result | |
| virtual void | post_process_response_fn_results () |
| post-process the interval computed for a response function | |
| virtual void | post_process_final_results () |
| perform final post-processing | |
| void | post_process_run_results (bool maximize) |
| post-process an optimization execution: output results, update convergence controls, and update GP approximation | |
| void | evaluate_response_star_truth () |
| evaluate the truth response at the optimal variables solution and update the GP with the new data | |
Protected Attributes | |
| Iterator | daceIterator |
| LHS iterator for constructing initial GP for all response functions. | |
| Model | fHatModel |
| GP model of response, one approximation per response function. | |
| Iterator | intervalOptimizer |
| optimizer for solving surrogate-based subproblem: NCSU DIRECT optimizer for maximizing expected improvement or mixed EA if discrete variables. | |
| Model | intervalOptModel |
| recast model which formulates the surrogate-based optimization subproblem (recasts as design problem; may assimilate mean and variance to enable max(expected improvement)) | |
| Real | approxFnStar |
| approximate response corresponding to minimum/maximum truth response | |
| Real | truthFnStar |
| minimum/maximum truth response function value | |
Static Private Member Functions | |
| static void | EIF_objective_min (const Variables &sub_model_vars, const Variables &recast_vars, const Response &sub_model_response, Response &recast_response) |
| static function used as the objective function in the Expected Improvement Function (EIF) for minimizing the GP | |
| static void | EIF_objective_max (const Variables &sub_model_vars, const Variables &recast_vars, const Response &sub_model_response, Response &recast_response) |
| static function used as the objective function in the Expected Improvement Function (EIF) for maximizing the GP | |
| static void | extract_objective (const Variables &sub_model_vars, const Variables &recast_vars, const Response &sub_model_response, Response &recast_response) |
| static function used to extract the active objective function when optimizing for an interval lower or upper bound (non-EIF formulations). The sense of the optimization is set separately. | |
Private Attributes | |
| const int | seedSpec |
| the user seed specification (default is 0) | |
| int | numSamples |
| the number of samples used in the surrogate | |
| String | rngName |
| name of the random number generator | |
| bool | gpModelFlag |
| flag indicating use of GP surrogate emulation | |
| bool | eifFlag |
| flag indicating use of maximized expected improvement for GP iterate selection | |
| unsigned short | improvementConvergeCntr |
| counter for number of successive iterations that the iteration improvement is less than the convergenceTol | |
| unsigned short | improvementConvergeLimit |
| counter for number of successive iterations that the iteration improvement is less than the convergenceTol | |
| Real | distanceTol |
| tolerance for L_2 change in optimal solution | |
| unsigned short | distanceConvergeCntr |
| counter for number of successive iterations that the L_2 change in optimal solution is less than the convergenceTol | |
| unsigned short | distanceConvergeLimit |
| counter for number of successive iterations that the L_2 change in optimal solution is less than the convergenceTol | |
| RealVector | prevCVStar |
| stores previous optimal point for continuous variables; used for assessing convergence | |
| IntVector | prevDIVStar |
| stores previous optimal point for discrete integer variables; used for assessing convergence | |
| RealVector | prevDRVStar |
| stores previous optimal point for discrete real variables; used for assessing convergence | |
| Real | prevFnStar |
| stores previous solution value for assessing convergence | |
| size_t | sbIterNum |
| surrogate-based minimization/maximization iteration count | |
| bool | boundConverged |
| flag indicating convergence of a minimization or maximization cycle | |
| bool | allResponsesPerIter |
| flag for maximal response extraction (all response values obtained on each function call) | |
| short | dataOrder |
| order of the data used for surrogate construction, in ActiveSet request vector 3-bit format; user may override responses spec | |
Static Private Attributes | |
| static NonDGlobalInterval * | nondGIInstance |
| pointer to the active object instance used within the static evaluator functions in order to avoid the need for static data | |
Detailed Description
Class for using global nongradient-based optimization approaches to calculate interval bounds for epistemic uncertainty quantification.
The NonDGlobalInterval class supports global nongradient-based optimization apporaches to determining interval bounds for epistemic UQ. The interval bounds may be on the entire function in the case of pure interval analysis (e.g. intervals on input = intervals on output), or the intervals may be on statistics of an "inner loop" aleatory analysis such as intervals on means, variances, or percentile levels. The preliminary implementation will use a Gaussian process surrogate to determine interval bounds.
Member Function Documentation
| const Model & algorithm_space_model | ( | ) | const [inline, virtual] |
default definition that gets redefined in selected derived Minimizers
Reimplemented from Analyzer.
References NonDGlobalInterval::fHatModel.
The documentation for this class was generated from the following files:
- NonDGlobalInterval.hpp
- NonDGlobalInterval.cpp
