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Dakota  Version 6.2
Public Member Functions | Protected Member Functions | Protected Attributes | Static Private Member Functions | Private Attributes | Static Private Attributes
NonDGlobalInterval Class Reference

Class for using global nongradient-based optimization approaches to calculate interval bounds for epistemic uncertainty quantification. More...

Inheritance diagram for NonDGlobalInterval:
NonDInterval NonD Analyzer Iterator NonDGlobalEvidence NonDGlobalSingleInterval

List of all members.

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 Modelalgorithm_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 NonDGlobalIntervalnondGIInstance
 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: