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NonDGlobalReliability Class Reference

Class for global reliability methods within DAKOTA/UQ. More...

Inheritance diagram for NonDGlobalReliability:
NonDReliability NonD Analyzer Iterator

Public Member Functions

 NonDGlobalReliability (ProblemDescDB &problem_db, Model &model)
 constructor
 
 ~NonDGlobalReliability ()
 destructor
 
bool resize ()
 reinitializes iterator based on new variable size
 
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 pre_run ()
 pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori More...
 
void core_run ()
 core portion of run; implemented by all derived classes and may include pre/post steps in lieu of separate pre/post More...
 
void print_results (std::ostream &s, short results_state=FINAL_RESULTS)
 print the final iterator results More...
 

Private Member Functions

void optimize_gaussian_process ()
 construct the GP using EGO/SKO
 
void importance_sampling ()
 perform multimodal adaptive importance sampling on the GP
 
void get_best_sample ()
 determine current best solution from among sample data for expected imporovement function in Performance Measure Approach (PMA)
 
Real constraint_penalty (const Real &constraint, const RealVector &c_variables)
 calculate the penalty to be applied to the PMA constraint value
 
Real expected_improvement (const RealVector &expected_values, const Variables &recast_vars)
 expected improvement function for the GP
 
Real expected_feasibility (const RealVector &expected_values, const Variables &recast_vars)
 expected feasibility function for the GP
 
void x_truth_evaluation (short mode)
 evaluate iteratedModel at current point to collect x-space truth data
 
void x_truth_evaluation (const RealVector &c_vars_u, short mode)
 evaluate iteratedModel at specified point to collect x-space truth data
 
void u_truth_evaluation (const RealVector &c_vars_u, short mode)
 evaluate uSpaceModel in BYPASS_SURROGATE mode to collect u-space truth data at specified point
 
void u_evaluation (const RealVector &c_vars_u, short mode)
 evaluate uSpaceModel to collect u-space surrogate data at specified point
 

Static Private Member Functions

static void EIF_objective_eval (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 (EIF) problem formulation for PMA
 
static void EFF_objective_eval (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 Feasibility (EFF) problem formulation for RIA
 

Private Attributes

Real fnStar
 minimum penalized response from among true function evaluations
 
short meritFunctionType
 type of merit function used to penalize sample data
 
Real lagrangeMult
 Lagrange multiplier for standard Lagrangian merit function.
 
Real augLagrangeMult
 Lagrange multiplier for augmented Lagrangian merit function.
 
Real penaltyParameter
 penalty parameter for augmented Lagrangian merit funciton
 
Real lastConstraintViolation
 constraint violation at last iteration, used to determine if the current iterate should be accepted (must reduce violation)
 
bool lastIterateAccepted
 flag to determine if last iterate was accepted this controls update of parameters for augmented Lagrangian merit fn
 
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 NonDGlobalReliabilitynondGlobRelInstance
 pointer to the active object instance used within the static evaluator functions in order to avoid the need for static data
 

Additional Inherited Members

- Protected Member Functions inherited from NonDReliability
 NonDReliability (ProblemDescDB &problem_db, Model &model)
 constructor
 
 ~NonDReliability ()
 destructor
 
void nested_variable_mappings (const SizetArray &c_index1, const SizetArray &di_index1, const SizetArray &ds_index1, const SizetArray &dr_index1, const ShortArray &c_target2, const ShortArray &di_target2, const ShortArray &ds_target2, const ShortArray &dr_target2)
 set primaryA{CV,DIV,DRV}MapIndices, secondaryA{CV,DIV,DRV}MapTargets within derived Iterators; supports computation of higher-level sensitivities in nested contexts (e.g., derivatives of statistics w.r.t. inserted design variables)
 
bool resize ()
 reinitializes iterator based on new variable size
 
void post_run (std::ostream &s)
 post-run portion of run (optional); verbose to print results; re-implemented by Iterators that can read all Variables/Responses and perform final analysis phase in a standalone way More...
 
const Modelalgorithm_space_model () const
 
- Static Protected Member Functions inherited from Iterator
static void gnewton_set_recast (const Variables &recast_vars, const ActiveSet &recast_set, ActiveSet &sub_model_set)
 conversion of request vector values for the Gauss-Newton Hessian approximation More...
 
- Protected Attributes inherited from NonDReliability
Model uSpaceModel
 Model representing the limit state in u-space, after any recastings and data fits.
 
Model mppModel
 RecastModel which formulates the optimization subproblem: RIA, PMA, EGO.
 
Iterator mppOptimizer
 Iterator which optimizes the mppModel.
 
unsigned short mppSearchType
 the MPP search type selection: Local: MV, x/u-space {AMV,AMV+,TANA,QMEA} or NO_APPROX Global x/u-space EGRA
 
Iterator importanceSampler
 importance sampling instance used to compute/refine probabilities
 
unsigned short integrationRefinement
 integration refinement type (NO_INT_REFINE, IS, AIS, or MMAIS) provided by refinement specification
 
size_t numRelAnalyses
 number of invocations of core_run()
 
size_t approxIters
 number of approximation cycles for the current respFnCount/levelCount
 
bool approxConverged
 indicates convergence of approximation-based iterations
 
int respFnCount
 counter for which response function is being analyzed
 
size_t levelCount
 counter for which response/probability level is being analyzed
 
size_t statCount
 counter for which final statistic is being computed
 
bool pmaMaximizeG
 flag indicating maximization of G(u) within PMA formulation
 
Real requestedTargetLevel
 the {response,reliability,generalized reliability} level target for the current response function
 
- Static Protected Attributes inherited from NonD
static NonDnondInstance
 pointer to the active object instance used within static evaluator functions in order to avoid the need for static data
 

Detailed Description

Class for global reliability methods within DAKOTA/UQ.

The NonDGlobalReliability class implements EGO/SKO for global MPP search, which maximizes an expected improvement function derived from Gaussian process models. Once the limit state has been characterized, a multimodal importance sampling approach is used to compute probabilities.

Member Function Documentation

void pre_run ( )
virtual

pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori

pre-run phase, which a derived iterator may optionally reimplement; when not present, pre-run is likely integrated into the derived run function. This is a virtual function; when re-implementing, a derived class must call its nearest parent's pre_run(), if implemented, typically before performing its own implementation steps.

Reimplemented from Analyzer.

References Model::initialize_mapping(), Iterator::methodPCIter, NonD::miPLIndex, NonDReliability::mppModel, Analyzer::pre_run(), and Model::update_from_subordinate_model().

void core_run ( )
virtual

core portion of run; implemented by all derived classes and may include pre/post steps in lieu of separate pre/post

Virtual run function for the iterator class hierarchy. All derived classes need to redefine it.

Reimplemented from Iterator.

References NonDGlobalReliability::importance_sampling(), NonDGlobalReliability::nondGlobRelInstance, and NonDGlobalReliability::optimize_gaussian_process().

void print_results ( std::ostream &  s,
short  results_state = FINAL_RESULTS 
)
virtual

print the final iterator results

This virtual function provides additional iterator-specific final results outputs beyond the function evaluation summary printed in finalize_run().

Reimplemented from Analyzer.

References NonD::cdfFlag, NonD::computedGenRelLevels, NonD::computedProbLevels, NonD::computedRespLevels, Iterator::iteratedModel, Analyzer::numFunctions, NonD::print_densities(), Model::response_labels(), and Dakota::write_precision.


The documentation for this class was generated from the following files: