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Dakota
Version 6.15
Explore and Predict with Confidence
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Performs Multilevel Monte Carlo sampling for uncertainty quantification. More...
Public Member Functions | |
NonDMultilevelSampling (ProblemDescDB &problem_db, Model &model) | |
standard constructor More... | |
~NonDMultilevelSampling () | |
destructor | |
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NonDHierarchSampling (ProblemDescDB &problem_db, Model &model) | |
standard constructor More... | |
virtual | ~NonDHierarchSampling () |
destructor (virtual declaration should be redundant with ~Iterator, but this is top of MLMF diamond so doesn't hurt to be explicit) | |
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NonDEnsembleSampling (ProblemDescDB &problem_db, Model &model) | |
standard constructor More... | |
~NonDEnsembleSampling () | |
destructor (virtual declaration should be redundant with ~Iterator, but this is top of MLMF diamond so doesn't hurt to be explicit) | |
bool | resize () |
reinitializes iterator based on new variable size | |
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NonDSampling (Model &model, const RealMatrix &sample_matrix) | |
alternate constructor for evaluating and computing statistics for the provided set of samples More... | |
~NonDSampling () | |
destructor | |
void | compute_statistics (const RealMatrix &vars_samples, const IntResponseMap &resp_samples) |
For the input sample set, computes mean, standard deviation, and probability/reliability/response levels (aleatory uncertainties) or intervals (epsitemic or mixed uncertainties) | |
void | compute_intervals (RealRealPairArray &extreme_fns) |
called by compute_statistics() to calculate min/max intervals using allResponses | |
void | compute_intervals (const IntResponseMap &samples) |
called by compute_statistics() to calculate extremeValues from samples | |
void | compute_intervals (RealRealPairArray &extreme_fns, const IntResponseMap &samples) |
called by compute_statistics() to calculate min/max intervals using samples | |
void | compute_moments (const RealVectorArray &fn_samples) |
calculates sample moments from a matrix of observations for a set of QoI | |
void | compute_moments (const IntResponseMap &samples) |
calculate sample moments and confidence intervals from a map of response observations | |
void | compute_moments (const IntResponseMap &samples, RealMatrix &moment_stats, RealMatrix &moment_grads, RealMatrix &moment_conf_ints, short moments_type, const StringArray &labels) |
convert IntResponseMap to RealVectorArray and invoke helpers | |
void | compute_moment_gradients (const RealVectorArray &fn_samples, const RealMatrixArray &grad_samples, const RealMatrix &moment_stats, RealMatrix &moment_grads, short moments_type) |
compute moment_grads from function and gradient samples | |
void | compute_moment_confidence_intervals (const RealMatrix &moment_stats, RealMatrix &moment_conf_ints, const SizetArray &sample_counts, short moments_type) |
compute moment confidence intervals from moment values | |
void | archive_moments (size_t inc_id=0) |
archive moment statistics in results DB | |
void | archive_moment_confidence_intervals (size_t inc_id=0) |
archive moment confidence intervals in results DB | |
void | archive_extreme_responses (size_t inc_id=0) |
archive extreme values (epistemic result) in results DB | |
void | compute_level_mappings (const IntResponseMap &samples) |
called by compute_statistics() to calculate CDF/CCDF mappings of z to p/beta and of p/beta to z as well as PDFs More... | |
void | print_statistics (std::ostream &s) const |
prints the statistics computed in compute_statistics() | |
void | print_intervals (std::ostream &s) const |
prints the intervals computed in compute_intervals() with default qoi_type and moment_labels | |
void | print_intervals (std::ostream &s, String qoi_type, const StringArray &interval_labels) const |
prints the intervals computed in compute_intervals() | |
void | print_moments (std::ostream &s) const |
prints the moments computed in compute_moments() with default qoi_type and moment_labels | |
void | print_moments (std::ostream &s, String qoi_type, const StringArray &moment_labels) const |
prints the moments computed in compute_moments() | |
void | print_wilks_stastics (std::ostream &s) const |
prints the Wilks stastics | |
void | update_final_statistics () |
update finalStatistics from minValues/maxValues, momentStats, and computedProbLevels/computedRelLevels/computedRespLevels | |
void | transform_samples (Pecos::ProbabilityTransformation &nataf, bool x_to_u=true) |
transform allSamples imported by alternate constructor. This is needed since random variable distribution parameters are not updated until run time and an imported sample_matrix is typically in x-space. More... | |
void | transform_samples (Pecos::ProbabilityTransformation &nataf, RealMatrix &sample_matrix, int num_samples=0, bool x_to_u=true) |
transform the specified samples matrix from x to u or u to x | |
unsigned short | sampling_scheme () const |
return sampleType | |
const String & | random_number_generator () const |
return rngName | |
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void | requested_levels (const RealVectorArray &req_resp_levels, const RealVectorArray &req_prob_levels, const RealVectorArray &req_rel_levels, const RealVectorArray &req_gen_rel_levels, short resp_lev_tgt, short resp_lev_tgt_reduce, bool cdf_flag, bool pdf_output) |
set requestedRespLevels, requestedProbLevels, requestedRelLevels, requestedGenRelLevels, respLevelTarget, cdfFlag, and pdfOutput (used in combination with alternate ctors) | |
void | print_level_mappings (std::ostream &s) const |
prints the z/p/beta/beta* mappings reflected in {requested,computed}{Resp,Prob,Rel,GenRel}Levels for default qoi_type and qoi_labels | |
void | print_level_mappings (std::ostream &s, String qoi_type, const StringArray &qoi_labels) const |
prints the z/p/beta/beta* mappings reflected in {requested,computed}{Resp,Prob,Rel,GenRel}Levels More... | |
void | print_level_mappings (std::ostream &s, const RealVector &level_maps, bool moment_offset, const String &prepend="") |
print level mapping statistics using optional pre-pend More... | |
bool | pdf_output () const |
get pdfOutput | |
void | pdf_output (bool output) |
set pdfOutput | |
short | final_moments_type () const |
get finalMomentsType | |
void | final_moments_type (short type) |
set finalMomentsType | |
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const VariablesArray & | all_variables () |
return the complete set of evaluated variables | |
const RealMatrix & | all_samples () |
return the complete set of evaluated samples | |
const IntResponseMap & | all_responses () const |
return the complete set of computed responses | |
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Iterator (std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
default constructor More... | |
Iterator (ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
alternate envelope constructor that assigns a representation pointer More... | |
Iterator (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
alternate envelope constructor which uses the ProblemDescDB but accepts a model from a higher level (meta-iterator) context, instead of constructing its own More... | |
Iterator (const String &method_string, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
alternate envelope constructor for instantiations by name without the ProblemDescDB More... | |
Iterator (const Iterator &iterator) | |
copy constructor More... | |
virtual | ~Iterator () |
destructor | |
Iterator | operator= (const Iterator &iterator) |
assignment operator | |
virtual void | derived_free_communicators (ParLevLIter pl_iter) |
derived class contributions to freeing the communicators associated with this Iterator instance | |
virtual void | post_input () |
read tabular data for post-run mode | |
virtual void | reset () |
restore initial state for repeated sub-iterator executions | |
virtual 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) | |
virtual void | initialize_iterator (int job_index) |
used by IteratorScheduler to set the starting data for a run | |
virtual void | pack_parameters_buffer (MPIPackBuffer &send_buffer, int job_index) |
used by IteratorScheduler to pack starting data for an iterator run | |
virtual void | unpack_parameters_buffer (MPIUnpackBuffer &recv_buffer, int job_index) |
used by IteratorScheduler to unpack starting data for an iterator run | |
virtual void | unpack_parameters_initialize (MPIUnpackBuffer &recv_buffer, int job_index) |
used by IteratorScheduler to unpack starting data and initialize an iterator run | |
virtual void | pack_results_buffer (MPIPackBuffer &send_buffer, int job_index) |
used by IteratorScheduler to pack results data from an iterator run | |
virtual void | unpack_results_buffer (MPIUnpackBuffer &recv_buffer, int job_index) |
used by IteratorScheduler to unpack results data from an iterator run | |
virtual void | update_local_results (int job_index) |
used by IteratorScheduler to update local results arrays | |
virtual bool | accepts_multiple_points () const |
indicates if this iterator accepts multiple initial points. Default return is false. Override to return true if appropriate. | |
virtual void | initial_point (const Variables &pt) |
sets the initial point for this iterator (user-functions mode for which Model updating is not used) | |
virtual void | initial_point (const RealVector &pt) |
sets the initial point (active continuous variables) for this iterator (user-functions mode for which Model updating is not used) | |
virtual void | initial_points (const VariablesArray &pts) |
sets the multiple initial points for this iterator. This should only be used if accepts_multiple_points() returns true. | |
virtual void | variable_bounds (const RealVector &cv_lower_bnds, const RealVector &cv_upper_bnds) |
assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used) | |
virtual void | linear_constraints (const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_lb, const RealVector &lin_ineq_ub, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgt) |
assign linear inequality and linear equality constraints for this iterator (user-functions mode for which Model updating is not used) | |
virtual void | nonlinear_constraints (const RealVector &nln_ineq_lb, const RealVector &nln_ineq_ub, const RealVector &nln_eq_tgt) |
assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used) | |
virtual void | initialize_graphics (int iterator_server_id=1) |
initialize the 2D graphics window and the tabular graphics data More... | |
virtual unsigned short | uses_method () const |
return name of any enabling iterator used by this iterator More... | |
virtual void | method_recourse () |
perform a method switch, if possible, due to a detected conflict | |
virtual void | sampling_increment () |
increment to next in sequence of refinement samples | |
virtual IntIntPair | estimate_partition_bounds () |
estimate the minimum and maximum partition sizes that can be utilized by this Iterator | |
virtual void | declare_sources () |
Declare sources to the evaluations database. | |
void | init_communicators (ParLevLIter pl_iter) |
initialize the communicators associated with this Iterator instance | |
void | set_communicators (ParLevLIter pl_iter) |
set the communicators associated with this Iterator instance | |
void | free_communicators (ParLevLIter pl_iter) |
free the communicators associated with this Iterator instance | |
void | resize_communicators (ParLevLIter pl_iter, bool reinit_comms) |
Resize the communicators. This is called from the letter's resize() | |
void | parallel_configuration_iterator (ParConfigLIter pc_iter) |
set methodPCIter | |
ParConfigLIter | parallel_configuration_iterator () const |
return methodPCIter | |
void | run (ParLevLIter pl_iter) |
invoke set_communicators(pl_iter) prior to run() | |
void | run () |
orchestrate initialize/pre/core/post/finalize phases More... | |
void | assign_rep (std::shared_ptr< Iterator > iterator_rep) |
replaces existing letter with a new one More... | |
void | iterated_model (const Model &model) |
set the iteratedModel (iterators and meta-iterators using a single model instance) | |
Model & | iterated_model () |
return the iteratedModel (iterators & meta-iterators using a single model instance) | |
ProblemDescDB & | problem_description_db () const |
return the problem description database (probDescDB) | |
ParallelLibrary & | parallel_library () const |
return the parallel library (parallelLib) | |
void | method_name (unsigned short m_name) |
set the method name to an enumeration value | |
unsigned short | method_name () const |
return the method name via its native enumeration value | |
void | method_string (const String &m_str) |
set the method name by string | |
String | method_string () const |
return the method name by string | |
String | method_enum_to_string (unsigned short method_enum) const |
convert a method name enumeration value to a string | |
unsigned short | method_string_to_enum (const String &method_str) const |
convert a method name string to an enumeration value | |
String | submethod_enum_to_string (unsigned short submethod_enum) const |
convert a sub-method name enumeration value to a string | |
const String & | method_id () const |
return the method identifier (methodId) | |
int | maximum_evaluation_concurrency () const |
return the maximum evaluation concurrency supported by the iterator | |
void | maximum_evaluation_concurrency (int max_conc) |
set the maximum evaluation concurrency supported by the iterator | |
size_t | maximum_iterations () const |
return the maximum iterations for this iterator | |
void | maximum_iterations (size_t max_iter) |
set the maximum iterations for this iterator | |
void | convergence_tolerance (Real conv_tol) |
set the method convergence tolerance (convergenceTol) | |
Real | convergence_tolerance () const |
return the method convergence tolerance (convergenceTol) | |
void | output_level (short out_lev) |
set the method output level (outputLevel) | |
short | output_level () const |
return the method output level (outputLevel) | |
void | summary_output (bool summary_output_flag) |
Set summary output control; true enables evaluation/results summary. | |
size_t | num_final_solutions () const |
return the number of solutions to retain in best variables/response arrays | |
void | num_final_solutions (size_t num_final) |
set the number of solutions to retain in best variables/response arrays | |
void | active_set (const ActiveSet &set) |
set the default active set (for use with iterators that employ evaluate_parameter_sets()) | |
const ActiveSet & | active_set () const |
return the default active set (used by iterators that employ evaluate_parameter_sets()) | |
void | active_set_request_vector (const ShortArray &asv) |
return the default active set request vector (used by iterators that employ evaluate_parameter_sets()) | |
const ShortArray & | active_set_request_vector () const |
return the default active set request vector (used by iterators that employ evaluate_parameter_sets()) | |
void | active_set_request_values (short asv_val) |
return the default active set request vector (used by iterators that employ evaluate_parameter_sets()) | |
void | sub_iterator_flag (bool si_flag) |
set subIteratorFlag (and update summaryOutputFlag if needed) | |
bool | is_null () const |
function to check iteratorRep (does this envelope contain a letter?) | |
std::shared_ptr< Iterator > | iterator_rep () const |
returns iteratorRep for access to derived class member functions that are not mapped to the top Iterator level | |
virtual void | eval_tag_prefix (const String &eval_id_str) |
set the hierarchical eval ID tag prefix More... | |
std::shared_ptr< TraitsBase > | traits () const |
returns methodTraits for access to derived class member functions that are not mapped to the top TraitsBase level | |
bool | top_level () |
Return whether the iterator is the top level iterator. | |
void | top_level (const bool &tflag) |
Set the iterator's top level flag. | |
Protected Member Functions | |
void | core_run () |
void | nested_response_mappings (const RealMatrix &primary_coeffs, const RealMatrix &secondary_coeffs) |
set primaryResponseCoefficients, secondaryResponseCoefficients within derived Iterators; Necessary for scalarization case in MLMC NonDMultilevelSampling to map scalarization in nested context | |
void | evaluate_ml_sample_increment (unsigned short step) |
helper that consolidates sequence advancement, sample generation, sample export, and sample evaluation | |
void | increment_ml_equivalent_cost (size_t new_N_l, Real lev_cost, Real ref_cost) |
increment the equivalent number of HF evaluations based on new model evaluations | |
void | accumulate_ml_Ysums (IntRealMatrixMap &sum_Y, RealMatrix &sum_YY, size_t lev, const RealVector &offset, SizetArray &num_Y) |
update accumulators for multilevel telescoping running sums using set of model evaluations within allResponses | |
void | accumulate_ml_Qsums (IntRealMatrixMap &sum_Q, size_t lev, const RealVector &offset, SizetArray &num_Q) |
update running QoI sums for one model (sum_Q) using set of model evaluations within allResponses; used for level 0 from other accumulators | |
Real | aggregate_variance_Ysum (const Real *sum_Y, const Real *sum_YY, const SizetArray &N_l) |
sum up variances across QoI (using sum_YY with means from sum_Y) | |
Real | aggregate_mse_Yvar (const Real *var_Y, const SizetArray &N_l) |
sum up Monte Carlo estimates for mean squared error (MSE) across QoI using discrepancy variances | |
Real | aggregate_mse_Ysum (const Real *sum_Y, const Real *sum_YY, const SizetArray &N_l) |
sum up Monte Carlo estimates for mean squared error (MSE) across QoI using discrepancy sums | |
void | configure_indices (unsigned short group, unsigned short form, size_t lev, short seq_type) |
manage response mode and active model key from {group,form,lev} triplet. seq_type defines the active dimension for a 1D model sequence. | |
void | configure_indices (size_t group, size_t form, size_t lev, short seq_type) |
convert group and form and call overload | |
Real | level_cost (const RealVector &cost, size_t step) |
return (aggregate) level cost | |
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void | uncorrected_surrogate_mode () |
synchronize iteratedModel and activeSet on UNCORRECTED_SURROGATE mode | |
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virtual void | print_variance_reduction (std::ostream &s) |
void | pre_run () |
pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori More... | |
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... | |
void | print_results (std::ostream &s, short results_state=FINAL_RESULTS) |
print the final iterator results More... | |
void | aggregated_models_mode () |
synchronize iteratedModel and activeSet on AGGREGATED_MODELS mode | |
void | bypass_surrogate_mode () |
synchronize iteratedModel and activeSet on BYPASS_SURROGATE mode | |
void | assign_specification_sequence (size_t index) |
advance any sequence specifications | |
int | random_seed (size_t index) const |
extract current random seed from randomSeedSeqSpec More... | |
void | compute_mc_estimator_variance (const RealVector &var_l, const SizetArray &N_l, RealVector &mc_est_var) |
compute the variance of the mean estimator (Monte Carlo sample average) | |
void | project_mc_estimator_variance (const RealVector &var_l, const SizetArray &N_l, size_t new_samp, RealVector &mc_est_var) |
compute the variance of the mean estimator (Monte Carlo sample average) after projection with additional samples (var_l remains fixed) | |
void | export_all_samples (String root_prepend, const Model &model, size_t iter, size_t step) |
export allSamples to tagged tabular file | |
void | convert_moments (const RealMatrix &raw_mom, RealMatrix &final_mom) |
convert uncentered raw moments (multilevel expectations) to standardized moments | |
Real | sum (const Real *vec, size_t vec_len) const |
compute sum of a set of observations | |
Real | average (const Real *vec, size_t vec_len) const |
compute average of a set of observations | |
Real | average (const RealVector &vec) const |
compute average of a set of observations | |
Real | average (const SizetArray &sa) const |
compute average of a set of observations | |
void | average (const RealMatrix &mat, size_t avg_index, RealVector &avg_vec) const |
compute row-averages for each column or column-averages for each row | |
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NonDSampling (ProblemDescDB &problem_db, Model &model) | |
constructor More... | |
NonDSampling (unsigned short method_name, Model &model, unsigned short sample_type, int samples, int seed, const String &rng, bool vary_pattern, short sampling_vars_mode) | |
alternate constructor for sample generation and evaluation "on the fly" More... | |
NonDSampling (unsigned short sample_type, int samples, int seed, const String &rng, const RealVector &lower_bnds, const RealVector &upper_bnds) | |
alternate constructor for sample generation "on the fly" More... | |
NonDSampling (unsigned short sample_type, int samples, int seed, const String &rng, const RealVector &means, const RealVector &std_devs, const RealVector &lower_bnds, const RealVector &upper_bnds, RealSymMatrix &correl) | |
alternate constructor for sample generation of correlated normals "on the fly" More... | |
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 () |
int | num_samples () const |
void | sampling_reset (int min_samples, bool all_data_flag, bool stats_flag) |
resets number of samples and sampling flags More... | |
void | sampling_reference (int samples_ref) |
set reference number of samples, which is a lower bound during reset | |
void | random_seed (int seed) |
assign randomSeed | |
void | vary_pattern (bool pattern_flag) |
set varyPattern | |
void | get_parameter_sets (Model &model) |
Uses lhsDriver to generate a set of samples from the distributions/bounds defined in the incoming model. More... | |
void | get_parameter_sets (Model &model, const int num_samples, RealMatrix &design_matrix) |
Uses lhsDriver to generate a set of samples from the distributions/bounds defined in the incoming model and populates the specified design matrix. More... | |
void | get_parameter_sets (Model &model, const int num_samples, RealMatrix &design_matrix, bool write_msg) |
core of get_parameter_sets that accepts message print control | |
void | get_parameter_sets (const RealVector &lower_bnds, const RealVector &upper_bnds) |
Uses lhsDriver to generate a set of uniform samples over lower_bnds/upper_bnds. More... | |
void | get_parameter_sets (const RealVector &means, const RealVector &std_devs, const RealVector &lower_bnds, const RealVector &upper_bnds, RealSymMatrix &correl) |
Uses lhsDriver to generate a set of normal samples. More... | |
void | update_model_from_sample (Model &model, const Real *sample_vars) |
Override default update of continuous vars only. | |
void | sample_to_variables (const Real *sample_vars, Variables &vars) |
override default mapping of continuous variables only | |
void | variables_to_sample (const Variables &vars, Real *sample_vars) |
override default mapping of continuous variables only | |
const RealVector & | response_error_estimates () const |
return error estimates associated with each of the finalStatistics | |
void | initialize_lhs (bool write_message, int num_samples) |
increments numLHSRuns, sets random seed, and initializes lhsDriver | |
void | active_set_mapping () |
in the case of sub-iteration, map from finalStatistics.active_set() requests to activeSet used in evaluate_parameter_sets() More... | |
void | mode_counts (const Variables &vars, size_t &cv_start, size_t &num_cv, size_t &div_start, size_t &num_div, size_t &dsv_start, size_t &num_dsv, size_t &drv_start, size_t &num_drv) const |
compute sampled subsets (all, active, uncertain) within all variables (acv/adiv/adrv) from samplingVarsMode and model More... | |
void | mode_bits (const Variables &vars, BitArray &active_vars, BitArray &active_corr) const |
define subset views for sampling modes | |
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NonD (ProblemDescDB &problem_db, Model &model) | |
constructor | |
NonD (unsigned short method_name, Model &model) | |
alternate constructor for sample generation and evaluation "on the fly" | |
NonD (unsigned short method_name, const RealVector &lower_bnds, const RealVector &upper_bnds) | |
alternate constructor for sample generation "on the fly" | |
~NonD () | |
destructor | |
void | derived_set_communicators (ParLevLIter pl_iter) |
derived class contributions to setting the communicators associated with this Iterator instance | |
void | initialize_run () |
utility function to perform common operations prior to pre_run(); typically memory initialization; setting of instance pointers More... | |
void | finalize_run () |
utility function to perform common operations following post_run(); deallocation and resetting of instance pointers More... | |
const Response & | response_results () const |
return the final statistics from the nondeterministic iteration | |
void | response_results_active_set (const ActiveSet &set) |
set the active set within finalStatistics | |
virtual void | initialize_response_covariance () |
initializes respCovariance | |
virtual void | initialize_final_statistics () |
initializes finalStatistics for storing NonD final results More... | |
void | pull_level_mappings (RealVector &level_maps, size_t offset) |
concatenate computed{Resp,Prob,Rel,GenRel}Levels into level_maps | |
void | push_level_mappings (const RealVector &level_maps, size_t offset) |
update computed{Resp,Prob,Rel,GenRel}Levels from level_maps | |
void | configure_sequence (size_t &num_steps, size_t &fixed_index, short &seq_type) |
configure fidelity/level counts from model hierarchy More... | |
void | configure_cost (unsigned short num_steps, bool multilevel, RealVector &cost) |
extract cost estimates from model hierarchy (forms or resolutions) | |
bool | query_cost (unsigned short num_steps, bool multilevel, RealVector &cost) |
extract cost estimates from model hierarchy, if available | |
void | load_pilot_sample (const SizetArray &pilot_spec, size_t num_steps, SizetArray &delta_N_l) |
distribute pilot sample specification across model forms or levels | |
void | load_pilot_sample (const SizetArray &pilot_spec, const Sizet3DArray &N_l, Sizet2DArray &delta_N_l) |
distribute pilot sample specification across model forms and levels | |
void | inflate_final_samples (const Sizet2DArray &N_l_2D, bool multilev, size_t fixed_index, Sizet3DArray &N_l_3D) |
update the relevant slice of N_l_3D from the final 2D multilevel or 2D multifidelity sample profile | |
void | resize_final_statistics_gradients () |
resizes finalStatistics::functionGradients based on finalStatistics ASV | |
void | update_aleatory_final_statistics () |
update finalStatistics::functionValues from momentStats and computed{Prob,Rel,GenRel,Resp}Levels | |
void | update_system_final_statistics () |
update system metrics from component metrics within finalStatistics | |
void | update_system_final_statistics_gradients () |
update finalStatistics::functionGradients | |
void | initialize_level_mappings () |
size computed{Resp,Prob,Rel,GenRel}Levels | |
void | compute_densities (const RealRealPairArray &min_max_fns, bool prob_refinement=false, bool all_levels_computed=false) |
compute the PDF bins from the CDF/CCDF values and store in computedPDF{Abscissas,Ordinates} More... | |
void | print_densities (std::ostream &s) const |
output the PDFs reflected in computedPDF{Abscissas,Ordinates} using default qoi_type and pdf_labels | |
void | print_densities (std::ostream &s, String qoi_type, const StringArray &pdf_labels) const |
output the PDFs reflected in computedPDF{Abscissas,Ordinates} | |
void | print_system_mappings (std::ostream &s) const |
print system series/parallel mappings for response levels | |
void | print_multilevel_evaluation_summary (std::ostream &s, const SizetArray &N_samp) |
print evaluation summary for multilevel sampling across 1D profile | |
void | print_multilevel_evaluation_summary (std::ostream &s, const Sizet2DArray &N_samp) |
print evaluation summary for multilevel sampling across 2D profile | |
void | print_multilevel_evaluation_summary (std::ostream &s, const Sizet3DArray &N_samp, String type="Final") |
print evaluation summary for multilevel sampling across 3D profile | |
void | construct_lhs (Iterator &u_space_sampler, Model &u_model, unsigned short sample_type, int num_samples, int seed, const String &rng, bool vary_pattern, short sampling_vars_mode=ACTIVE) |
assign a NonDLHSSampling instance within u_space_sampler | |
unsigned short | sub_optimizer_select (unsigned short requested_sub_method, unsigned short default_sub_method=SUBMETHOD_SQP) |
utility for vetting sub-method request against optimizers within the package configuration | |
size_t | one_sided_delta (Real current, Real target) |
compute a one-sided sample increment for multilevel methods to move current sampling level to a new target | |
size_t | one_sided_delta (const SizetArray ¤t, const RealVector &targets, size_t power) |
compute a one-sided sample increment for multilevel methods to move current sampling level to a new target | |
void | archive_allocate_mappings () |
allocate results array storage for distribution mappings | |
void | archive_from_resp (size_t fn_index, size_t inc_id=0) |
archive the mappings from specified response levels for specified fn | |
void | archive_to_resp (size_t fn_index, size_t inc_id=0) |
archive the mappings to computed response levels for specified fn and (optional) increment id. | |
void | archive_allocate_pdf () |
allocate results array storage for pdf histograms | |
void | archive_pdf (size_t fn_index, size_t inc_id=0) |
archive a single pdf histogram for specified function | |
void | archive_equiv_hf_evals (const Real equiv_hf_evals) |
archive the equivalent number of HF evals (used by ML/MF methods) | |
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Analyzer () | |
default constructor | |
Analyzer (ProblemDescDB &problem_db, Model &model) | |
standard constructor | |
Analyzer (unsigned short method_name, Model &model) | |
alternate constructor for instantiations "on the fly" with a Model | |
Analyzer (unsigned short method_name) | |
alternate constructor for instantiations "on the fly" without a Model | |
~Analyzer () | |
destructor | |
virtual void | update_model_from_variables (Model &model, const Variables &vars) |
update model's current variables with data from vars | |
void | update_from_model (const Model &model) |
set inherited data attributes based on extractions from incoming model | |
void | pre_output () |
const Model & | algorithm_space_model () const |
const Variables & | variables_results () const |
return a single final iterator solution (variables) | |
const VariablesArray & | variables_array_results () |
return multiple final iterator solutions (variables). This should only be used if returns_multiple_points() returns true. | |
const ResponseArray & | response_array_results () |
return multiple final iterator solutions (response). This should only be used if returns_multiple_points() returns true. | |
bool | compact_mode () const |
returns Analyzer::compactMode | |
bool | returns_multiple_points () const |
indicates if this iterator returns multiple final points. Default return is false. Override to return true if appropriate. | |
void | evaluate_parameter_sets (Model &model, bool log_resp_flag, bool log_best_flag) |
perform function evaluations to map parameter sets (allVariables) into response sets (allResponses) More... | |
void | get_vbd_parameter_sets (Model &model, int num_samples) |
generate replicate parameter sets for use in variance-based decomposition More... | |
void | compute_vbd_stats (const int num_samples, const IntResponseMap &resp_samples) |
compute VBD-based Sobol indices More... | |
void | archive_sobol_indices () const |
archive VBD-based Sobol indices More... | |
virtual void | archive_model_variables (const Model &, size_t idx) const |
archive model evaluation points | |
virtual void | archive_model_response (const Response &, size_t idx) const |
archive model evaluation responses | |
void | read_variables_responses (int num_evals, size_t num_vars) |
convenience function for reading variables/responses (used in derived classes post_input) More... | |
void | print_sobol_indices (std::ostream &s) const |
Printing of VBD results. More... | |
void | samples_to_variables_array (const RealMatrix &sample_matrix, VariablesArray &vars_array) |
convert samples array to variables array; e.g., allSamples to allVariables | |
void | variables_array_to_samples (const VariablesArray &vars_array, RealMatrix &sample_matrix) |
convert variables array to samples array; e.g., allVariables to allSamples | |
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Iterator (BaseConstructor, ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
constructor initializes the base class part of letter classes (BaseConstructor overloading avoids infinite recursion in the derived class constructors - Coplien, p. 139) More... | |
Iterator (NoDBBaseConstructor, unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
alternate constructor for base iterator classes constructed on the fly More... | |
Iterator (NoDBBaseConstructor, unsigned short method_name, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase())) | |
alternate constructor for base iterator classes constructed on the fly More... | |
virtual void | derived_init_communicators (ParLevLIter pl_iter) |
derived class contributions to initializing the communicators associated with this Iterator instance | |
virtual const VariablesArray & | initial_points () const |
gets the multiple initial points for this iterator. This will only be meaningful after a call to initial_points mutator. | |
StrStrSizet | run_identifier () const |
get the unique run identifier based on method name, id, and number of executions | |
void | initialize_model_graphics (Model &model, int iterator_server_id) |
helper function that encapsulates initialization operations, modular on incoming Model instance More... | |
void | export_final_surrogates (Model &data_fit_surr_model) |
export final surrogates generated, e.g., GP in EGO and friends More... | |
Private Member Functions | |
void | multilevel_mc_Ysum () |
Perform multilevel Monte Carlo across the discretization levels for a particular model form using discrepancy accumulators (sum_Y) More... | |
void | multilevel_mc_Qsum () |
Perform multilevel Monte Carlo across the discretization levels for a particular model form using QoI accumulators (sum_Q) More... | |
void | initialize_ml_Ysums (IntRealMatrixMap &sum_Y, size_t num_lev) |
initialize the ML accumulators for computing means, variances, and covariances across fidelity levels | |
void | initialize_ml_Qsums (IntRealMatrixMap &sum_Ql, IntRealMatrixMap &sum_Qlm1, IntIntPairRealMatrixMap &sum_QlQlm1, size_t num_lev) |
initialize the ML accumulators for computing means, variances, and covariances across fidelity levels | |
void | accumulate_offsets (RealVector &mu) |
accumulate initial approximation to mean vector, for use as offsets in subsequent accumulations | |
void | accumulate_sums (IntRealMatrixMap &sum_Ql, IntRealMatrixMap &sum_Qlm1, IntIntPairRealMatrixMap &sum_QlQlm1, const size_t step, const RealVectorArray &offset, Sizet2DArray &N_l) |
update running QoI sums for one model (sum_Q) using set of model evaluations within allResponses; used for level 0 from other accumulators | |
void | accumulate_ml_Qsums (IntRealMatrixMap &sum_Ql, IntRealMatrixMap &sum_Qlm1, IntIntPairRealMatrixMap &sum_QlQlm1, size_t lev, const RealVector &offset, SizetArray &num_Q) |
update running QoI sums for two models (sum_Ql, sum_Qlm1) using set of model evaluations within allResponses | |
void | compute_ml_equivalent_cost (const SizetArray &raw_N_l, const RealVector &cost) |
void | compute_error_estimates (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const Sizet2DArray &num_Q) |
populate finalStatErrors for MLMC based on Q sums | |
Real | variance_Ysum (Real sum_Y, Real sum_YY, size_t Nlq) |
compute variance from sum accumulators | |
Real | variance_Qsum (Real sum_Ql, Real sum_Qlm1, Real sum_QlQl, Real sum_QlQlm1, Real sum_Qlm1Qlm1, size_t Nlq) |
compute variance from sum accumulators | |
Real | var_lev_l (Real sum_Ql, Real sum_Qlm1, Real sum_QlQl, Real sum_Qlm1Qlm1, size_t Nlq) |
void | aggregate_variance_target_Qsum (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const Sizet2DArray &N_l, const size_t step, RealMatrix &agg_var_qoi) |
sum up variances for QoI (using sum_YY with means from sum_Y) based on allocation target | |
Real | aggregate_variance_mean_Qsum (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const Sizet2DArray &N_l, const size_t step, const size_t qoi) |
wrapper for aggregate_variance_Qsum | |
Real | aggregate_variance_Qsum (const Real *sum_Ql, const Real *sum_Qlm1, const Real *sum_QlQl, const Real *sum_QlQlm1, const Real *sum_Qlm1Qlm1, const SizetArray &N_l, const size_t lev) |
sum up variances across QoI (using sum_YY with means from sum_Y) | |
Real | aggregate_variance_Qsum (const Real *sum_Ql, const Real *sum_Qlm1, const Real *sum_QlQl, const Real *sum_QlQlm1, const Real *sum_Qlm1Qlm1, const SizetArray &N_l, const size_t lev, const size_t qoi) |
sum up variances for QoI (using sum_YY with means from sum_Y) | |
Real | aggregate_variance_variance_Qsum (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const Sizet2DArray &N_l, const size_t step, const size_t qoi) |
wrapper for var_of_var_ml | |
Real | aggregate_variance_sigma_Qsum (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const Sizet2DArray &N_l, const size_t step, const size_t qoi) |
wrapper for var_of_sigma_ml | |
Real | aggregate_variance_scalarization_Qsum (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const Sizet2DArray &N_l, const size_t step, const size_t qoi) |
wrapper for var_of_scalarization_ml More... | |
void | aggregate_mse_target_Qsum (RealMatrix &agg_var_qoi, const Sizet2DArray &N_l, const size_t step, RealVector &estimator_var0_qoi) |
sum up Monte Carlo estimates for mean squared error (MSE) for QoI using discrepancy sums based on allocation target | |
void | set_convergence_tol (const RealVector &estimator_var0_qoi, const RealVector &cost, RealVector &eps_sq_div_2_qoi) |
compute epsilon^2/2 term for each qoi based on reference estimator_var0 and relative convergence tolereance | |
void | compute_sample_allocation_target (const RealMatrix &agg_var_qoi, const RealVector &cost, const Sizet2DArray &N_l, SizetArray &delta_N_l) |
compute sample allocation delta based on a budget constraint | |
void | compute_sample_allocation_target (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const RealVector &eps_sq_div_2, const RealMatrix &agg_var_qoi, const RealVector &cost, const Sizet2DArray &N_l, SizetArray &delta_N_l) |
compute sample allocation delta based on current samples and based on allocation target. Single allocation target for each qoi, aggregated using max operation. | |
void | compute_moments (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const Sizet2DArray &N_l) |
void | assign_static_member (const Real &conv_tol, size_t &qoi, const size_t &qoi_aggregation, const size_t &num_functions, const RealVector &level_cost_vec, const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const RealVector &pilot_samples, const RealMatrix &scalarization_response_mapping) const |
void | assign_static_member_problem18 (Real &var_L_exact, Real &var_H_exact, Real &mu_four_L_exact, Real &mu_four_H_exact, Real &Ax, RealVector &level_cost_vec) const |
Static Private Member Functions | |
static Real | variance_Ysum_static (Real sum_Y, Real sum_YY, size_t Nlq_pilot, size_t Nlq, bool compute_gradient, Real &grad) |
compute variance from sum accumulators, necessary for sample allocation optimization | |
static Real | variance_Qsum_static (Real sum_Ql, Real sum_Qlm1, Real sum_QlQl, Real sum_QlQlm1, Real sum_Qlm1Qlm1, size_t Nlq_pilot, size_t Nlq, bool compute_gradient, Real &grad) |
compute variance from sum accumulators, necessary for sample allocation optimization | |
static Real | var_lev_l_static (Real sum_Ql, Real sum_Qlm1, Real sum_QlQl, Real sum_Qlm1Qlm1, size_t Nlq_pilot, size_t Nlq, bool compute_gradient, Real &grad) |
static Real | unbiased_mean_product_pair (const Real sumQ1, const Real sumQ2, const Real sumQ1Q2, const size_t Nlq) |
compute the unbiased product of two sampling means | |
static Real | unbiased_mean_product_triplet (const Real sumQ1, const Real sumQ2, const Real sumQ3, const Real sumQ1Q2, const Real sumQ1Q3, const Real sumQ2Q3, const Real sumQ1Q2Q3, const size_t Nlq) |
compute the unbiased product of three sampling means | |
static Real | unbiased_mean_product_pairpair (const Real sumQ1, const Real sumQ2, const Real sumQ1Q2, const Real sumQ1sq, const Real sumQ2sq, const Real sumQ1sqQ2, const Real sumQ1Q2sq, const Real sumQ1sqQ2sq, const size_t Nlq) |
compute the unbiased product of two pairs of products of sampling means | |
static Real | var_of_var_ml_l0 (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const size_t Nlq_pilot, const Real Nlq, const size_t qoi, const bool compute_gradient, Real &grad_g) |
static Real | var_of_var_ml_lmax (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const size_t Nlq_pilot, const Real Nlq, const size_t qoi, const bool compute_gradient, Real &grad_g) |
static Real | var_of_var_ml_l (const IntRealMatrixMap &sum_Ql, const IntRealMatrixMap &sum_Qlm1, const IntIntPairRealMatrixMap &sum_QlQlm1, const size_t Nlq_pilot, const Real Nlq, const size_t qoi, const size_t lev, const bool compute_gradient, Real &grad_g) |
static void | target_cost_objective_eval_optpp (int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode) |
OPTPP definition. | |
static void | target_cost_constraint_eval_optpp (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static void | target_var_constraint_eval_optpp (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static void | target_var_constraint_eval_logscale_optpp (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static void | target_sigma_constraint_eval_optpp (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static void | target_sigma_constraint_eval_logscale_optpp (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static void | target_scalarization_constraint_eval_optpp (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static void | target_scalarization_constraint_eval_logscale_optpp (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static void | target_var_objective_eval_optpp (int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode) |
static void | target_var_objective_eval_logscale_optpp (int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode) |
static void | target_sigma_objective_eval_optpp (int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode) |
static void | target_sigma_objective_eval_logscale_optpp (int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode) |
static void | target_scalarization_objective_eval_optpp (int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode) |
static void | target_scalarization_objective_eval_logscale_optpp (int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode) |
static void | target_cost_objective_eval_npsol (int &mode, int &n, double *x, double &f, double *gradf, int &nstate) |
NPSOL definition (Wrapper using OPTPP implementation above under the hood) | |
static void | target_cost_constraint_eval_npsol (int &mode, int &m, int &n, int &ldJ, int *needc, double *x, double *g, double *grad_g, int &nstate) |
static void | target_var_constraint_eval_npsol (int &mode, int &m, int &n, int &ldJ, int *needc, double *x, double *g, double *grad_g, int &nstate) |
static void | target_var_constraint_eval_logscale_npsol (int &mode, int &m, int &n, int &ldJ, int *needc, double *x, double *g, double *grad_g, int &nstate) |
static void | target_sigma_constraint_eval_npsol (int &mode, int &m, int &n, int &ldJ, int *needc, double *x, double *g, double *grad_g, int &nstate) |
static void | target_sigma_constraint_eval_logscale_npsol (int &mode, int &m, int &n, int &ldJ, int *needc, double *x, double *g, double *grad_g, int &nstate) |
static void | target_scalarization_constraint_eval_npsol (int &mode, int &m, int &n, int &ldJ, int *needc, double *x, double *g, double *grad_g, int &nstate) |
static void | target_scalarization_constraint_eval_logscale_npsol (int &mode, int &m, int &n, int &ldJ, int *needc, double *x, double *g, double *grad_g, int &nstate) |
static void | target_var_objective_eval_npsol (int &mode, int &n, double *x, double &f, double *gradf, int &nstate) |
static void | target_var_objective_eval_logscale_npsol (int &mode, int &n, double *x, double &f, double *gradf, int &nstate) |
static void | target_sigma_objective_eval_npsol (int &mode, int &n, double *x, double &f, double *gradf, int &nstate) |
static void | target_sigma_objective_eval_logscale_npsol (int &mode, int &n, double *x, double &f, double *gradf, int &nstate) |
static void | target_scalarization_objective_eval_npsol (int &mode, int &n, double *x, double &f, double *gradf, int &nstate) |
static void | target_scalarization_objective_eval_logscale_npsol (int &mode, int &n, double *x, double &f, double *gradf, int &nstate) |
static void | target_var_constraint_eval_optpp_problem18 (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static void | target_sigma_constraint_eval_optpp_problem18 (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode) |
static double | exact_var_of_var_problem18 (const RealVector &Nl) |
static double | exact_var_of_sigma_problem18 (const RealVector &Nl) |
Private Attributes | |
unsigned short | seq_index |
short | allocationTarget |
store the allocation_target input specification, prior to run-time Options right now: More... | |
bool | useTargetVarianceOptimizationFlag |
option to switch on numerical optimization for solution of sample alloation of allocationTarget Variance | |
short | qoiAggregation |
store the qoi_aggregation_norm input specification, prior to run-time Options right now: More... | |
short | convergenceTolType |
store the convergence_tolerance_type input specification, prior to run-time Options right now: More... | |
short | convergenceTolTarget |
store the convergence_tolerance_target input specification, prior to run-time Options right now: More... | |
RealVector | convergenceTolVec |
RealMatrix | scalarizationCoeffs |
"scalarization" response_mapping matrix applied to the mlmc sample allocation when a scalarization, i.e. alpha_1 * mean + alpha_2 * sigma, is the target. | |
RealMatrix | NTargetQoi |
Helper data structure to store intermedia sample allocations. | |
RealMatrix | NTargetQoiFN |
Additional Inherited Members | |
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static void | compute_moments (const RealVectorArray &fn_samples, SizetArray &sample_counts, RealMatrix &moment_stats, short moments_type, const StringArray &labels) |
core compute_moments() implementation with all data as inputs | |
static void | compute_moments (const RealVectorArray &fn_samples, RealMatrix &moment_stats, short moments_type) |
core compute_moments() implementation with all data as inputs | |
static void | compute_moments (const RealMatrix &fn_samples, RealMatrix &moment_stats, short moments_type) |
alternate RealMatrix samples API for use by external clients | |
static void | print_moments (std::ostream &s, const RealMatrix &moment_stats, const RealMatrix moment_cis, String qoi_type, short moments_type, const StringArray &moment_labels, bool print_cis) |
core print moments that can be called without object | |
static int | compute_wilks_sample_size (unsigned short order, Real alpha, Real beta, bool twosided=false) |
calculates the number of samples using the Wilks formula Static so I can test without instantiating a NonDSampling object - RWH | |
static Real | compute_wilks_residual (unsigned short order, int nsamples, Real alpha, Real beta, bool twosided) |
Helper function - calculates the Wilks residual. | |
static Real | compute_wilks_alpha (unsigned short order, int nsamples, Real beta, bool twosided=false) |
calculates the alpha paramter given number of samples using the Wilks formula Static so I can test without instantiating a NonDSampling object - RWH | |
static Real | compute_wilks_beta (unsigned short order, int nsamples, Real alpha, bool twosided=false) |
calculates the beta paramter given number of samples using the Wilks formula Static so I can test without instantiating a NonDSampling object - RWH | |
static Real | get_wilks_alpha_min () |
Get the lower and upper bounds supported by Wilks bisection solves. | |
static Real | get_wilks_alpha_max () |
static Real | get_wilks_beta_min () |
static Real | get_wilks_beta_max () |
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static void | uncentered_to_centered (Real rm1, Real rm2, Real rm3, Real rm4, Real &cm1, Real &cm2, Real &cm3, Real &cm4, size_t Nlq) |
convert uncentered (raw) moments to centered moments; biased estimators More... | |
static void | uncentered_to_centered (Real rm1, Real rm2, Real rm3, Real rm4, Real &cm1, Real &cm2, Real &cm3, Real &cm4) |
convert uncentered (raw) moments to centered moments; unbiased estimators More... | |
static void | centered_to_standard (Real cm1, Real cm2, Real cm3, Real cm4, Real &sm1, Real &sm2, Real &sm3, Real &sm4) |
convert centered moments to standardized moments | |
static void | check_negative (Real &cm) |
detect, warn, and repair a negative central moment (for even orders) | |
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Sizet3DArray | NLev |
total number of successful sample evaluations (excluding faults) for each model form, discretization level, and QoI | |
SizetArray | pilotSamples |
store the pilot_samples input specification, prior to run-time invocation of load_pilot_sample() | |
SizetArray | randomSeedSeqSpec |
user specification for seed_sequence | |
size_t | mlmfIter |
major iteration counter | |
Real | equivHFEvals |
equivalent number of high fidelity evaluations accumulated using samples across multiple model forms and/or discretization levels | |
bool | exportSampleSets |
if defined, export each of the sample increments in ML, CV, MLCV using tagged tabular files | |
unsigned short | exportSamplesFormat |
format for exporting sample increments using tagged tabular files | |
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static NonD * | nondInstance |
pointer to the active object instance used within static evaluator functions in order to avoid the need for static data | |
Performs Multilevel Monte Carlo sampling for uncertainty quantification.
Multilevel Monte Carlo (MLMC) is a variance-reduction technique that utilitizes lower fidelity simulations that have response QoI that are correlated with the high-fidelity response QoI.
NonDMultilevelSampling | ( | ProblemDescDB & | problem_db, |
Model & | model | ||
) |
standard constructor
This constructor is called for a standard letter-envelope iterator instantiation. In this case, set_db_list_nodes has been called and probDescDB can be queried for settings from the method specification.
References Dakota::abort_handler(), NonDMultilevelSampling::allocationTarget, ProblemDescDB::get_rv(), Analyzer::numFunctions, Iterator::probDescDB, NonDMultilevelSampling::qoiAggregation, and NonDMultilevelSampling::scalarizationCoeffs.
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protectedvirtual |
The primary run function manages the general case: a hierarchy of model forms (from the ordered model fidelities within a HierarchSurrModel), each of which may contain multiple discretization levels.
Reimplemented from Iterator.
References Iterator::iteratedModel, Model::multifidelity_precedence(), NonDMultilevelSampling::multilevel_mc_Qsum(), and NonDMultilevelSampling::multilevel_mc_Ysum().
Referenced by NonDMultilevControlVarSampling::core_run().
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private |
Perform multilevel Monte Carlo across the discretization levels for a particular model form using discrepancy accumulators (sum_Y)
This function performs MLMC on a model sequence, either defined by model forms or discretization levels.
References NonDMultilevelSampling::accumulate_ml_Ysums(), NonDMultilevelSampling::aggregate_mse_Ysum(), NonDMultilevelSampling::aggregate_variance_Ysum(), NonDEnsembleSampling::average(), NonD::configure_cost(), NonDMultilevelSampling::configure_indices(), NonD::configure_sequence(), Iterator::convergenceTol, NonDEnsembleSampling::convert_moments(), NonDMultilevelSampling::evaluate_ml_sample_increment(), NonDMultilevelSampling::increment_ml_equivalent_cost(), NonD::inflate_final_samples(), NonDMultilevelSampling::initialize_ml_Ysums(), NonDMultilevelSampling::level_cost(), NonD::load_pilot_sample(), Iterator::maxFunctionEvals, Iterator::maxIterations, NonDEnsembleSampling::mlmfIter, NonD::momentStats, NonDEnsembleSampling::NLev, Analyzer::numFunctions, NonDSampling::numSamples, NonD::one_sided_delta(), Iterator::outputLevel, NonDEnsembleSampling::pilotSamples, and Dakota::SZ_MAX.
Referenced by NonDMultilevelSampling::core_run().
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Perform multilevel Monte Carlo across the discretization levels for a particular model form using QoI accumulators (sum_Q)
This function performs "geometrical" MLMC on a single model form with multiple discretization levels.
References Dakota::abort_handler(), NonDMultilevelSampling::accumulate_sums(), NonDMultilevelSampling::aggregate_mse_target_Qsum(), NonDMultilevelSampling::aggregate_variance_target_Qsum(), NonDMultilevelSampling::allocationTarget, NonDMultilevelSampling::compute_error_estimates(), NonDMultilevelSampling::compute_sample_allocation_target(), NonD::configure_cost(), NonDMultilevelSampling::configure_indices(), NonD::configure_sequence(), Iterator::convergenceTol, NonDMultilevelSampling::evaluate_ml_sample_increment(), NonDMultilevelSampling::increment_ml_equivalent_cost(), NonD::inflate_final_samples(), NonDMultilevelSampling::initialize_ml_Qsums(), NonDMultilevelSampling::level_cost(), NonD::load_pilot_sample(), Iterator::maxFunctionEvals, Iterator::maxIterations, NonDEnsembleSampling::mlmfIter, NonDEnsembleSampling::NLev, Analyzer::numFunctions, NonDSampling::numSamples, NonDEnsembleSampling::pilotSamples, NonDMultilevelSampling::scalarizationCoeffs, NonDMultilevelSampling::set_convergence_tol(), and Dakota::SZ_MAX.
Referenced by NonDMultilevelSampling::core_run().
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wrapper for var_of_scalarization_ml
For TARGET_SCALARIZATION we have the special case that we can also combine scalarization over multiple qoi This is respresented in the scalarization response mapping stored in scalarizationCoeffs This is for now neglecting cross terms for covariance terms inbetween different qois, e.g. V[mu_1 + 2 sigma_1 + 3 mu_2] = V[mu_1] + V[2 sigma_1] + 2 Cov[mu_1, 2 sigma_1] + V[3 mu_2] + 2 Cov[2 mu_1, 3 mu_2] + 2 Cov[2 sigma_1, 3 mu_2] V[mu_1] + V[2 sigma_1] + 2 Cov[mu_1, 2 sigma_1] + V[3 mu_2] (What we do)
References NonDMultilevelSampling::aggregate_variance_mean_Qsum(), NonDMultilevelSampling::aggregate_variance_sigma_Qsum(), Analyzer::numFunctions, and NonDMultilevelSampling::scalarizationCoeffs.
Referenced by NonDMultilevelSampling::aggregate_variance_target_Qsum().
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store the allocation_target input specification, prior to run-time Options right now:
Referenced by NonDMultilevelSampling::aggregate_variance_target_Qsum(), NonDMultilevelSampling::compute_sample_allocation_target(), NonDMultilevelSampling::multilevel_mc_Qsum(), NonDMultilevelSampling::nested_response_mappings(), and NonDMultilevelSampling::NonDMultilevelSampling().
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store the qoi_aggregation_norm input specification, prior to run-time Options right now:
Referenced by NonDMultilevelSampling::compute_sample_allocation_target(), and NonDMultilevelSampling::NonDMultilevelSampling().
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store the convergence_tolerance_type input specification, prior to run-time
Options right now:
Referenced by NonDMultilevelSampling::set_convergence_tol().
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store the convergence_tolerance_target input specification, prior to run-time Options right now:
Referenced by NonDMultilevelSampling::compute_sample_allocation_target(), and NonDMultilevelSampling::set_convergence_tol().