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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
NonDLocalInterval Class Reference

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

Inheritance diagram for NonDLocalInterval:
NonDInterval NonD Analyzer Iterator NonDLocalEvidence NonDLocalSingleInterval

List of all members.

Public Member Functions

 NonDLocalInterval (ProblemDescDB &problem_db, Model &model)
 constructor
 ~NonDLocalInterval ()
 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 a gradient-based optimization to determine interval bounds for an entire function or interval bounds on a particular statistical estimator.
unsigned short uses_method () const
 return name of active optimizer method
void method_recourse ()
 perform an MPP optimizer method switch due to a detected conflict

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 truncate_to_cell_bounds (RealVector &initial_pt)
 truncate initial_pt to respect current cell lower/upper bounds
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

Protected Attributes

Iterator minMaxOptimizer
 local gradient-based optimizer
Model minMaxModel
 recast model which extracts the active objective function

Static Private Member Functions

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

Private Attributes

bool npsolFlag
 flag representing the gradient-based optimization algorithm selection (NPSOL SQP or OPT++ NIP)

Static Private Attributes

static NonDLocalIntervalnondLIInstance
 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 local gradient-based optimization approaches to calculate interval bounds for epistemic uncertainty quantification.

The NonDLocalInterval class supports local gradient-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 documentation for this class was generated from the following files: