DataMethodRep Class Reference

Body class for method specification data. More...

Public Member Functions
	~DataMethodRep ()
	destructor

Public Attributes
String	idMethod
	string identifier for the method specification data set (from the id_method specification in MethodIndControl)
String	modelPointer
	string pointer to the model specification to be used by this method (from the model_pointer specification in MethodIndControl)
String	lowFidModelPointer
	string to point to the low fidelity model for Bayesian experimental design
short	methodOutput
	method verbosity control: {SILENT,QUIET,NORMAL,VERBOSE,DEBUG}_OUTPUT (from the output specification in MethodIndControl)
size_t	maxIterations
	maximum number of iterations allowed for the method (from the max_iterations specification in MethodIndControl)
size_t	maxRefineIterations
	maximum number of refinement iterations allowed for a uniform/adaptive refinement approach (from the max_refinement_iterations specification in MethodIndControl)
size_t	maxSolverIterations
	maximum number of internal solver iterations allowed for the method (from the max_solver_iterations specification in MethodIndControl)
size_t	maxFunctionEvals
	maximum number of function evaluations allowed for the method (from the max_function_evaluations specification in MethodIndControl)
bool	speculativeFlag
	flag for use of speculative gradient approaches for maintaining parallel load balance during the line search portion of optimization algorithms (from the speculative specification in MethodIndControl)
bool	methodUseDerivsFlag
	flag for usage of derivative data to enhance the computation of surrogate models (PCE/SC expansions, GP models for EGO/EGRA/EGIE) based on the use_derivatives specification
Real	constraintTolerance
	tolerance for controlling the amount of infeasibility that is allowed before an active constraint is considered to be violated (from the constraint_tolerance specification in MethodIndControl)
bool	methodScaling
	flag indicating scaling status (from the scaling specification in MethodIndControl)
size_t	numFinalSolutions
	number of final solutions returned from the iterator
Real	convergenceTolerance
	iteration convergence tolerance for the method (from the convergence_tolerance specification in MethodIndControl)
bool	relativeConvMetric
	controls use of convergence tolerance in a relative (true) or absolute (false) context
short	statsMetricMode
	mode of computing statistics metrics used for convergence assessment of multilevel/multifidelity refinement processes: active or combined
unsigned short	methodName
	the method selection: one of the optimizer, least squares, nond, dace, or parameter study methods
unsigned short	subMethod
	enum value for a sub-method type
String	subMethodName
	string identifier for a sub-method name within a multi-option method specification (e.g., from meta-iterators)
String	subModelPointer
	string pointer for a sub-model specification used by a meta-iterator
String	subMethodPointer
	string pointer for a sub-method specification used by a meta-iterator
int	iteratorServers
	number of servers for concurrent iterator parallelism (from the iterator_servers specification)
int	procsPerIterator
	number of processors for each concurrent iterator partition (from the processors_per_iterator specification)
short	iteratorScheduling
	type of scheduling ({DEFAULT,MASTER,PEER}_SCHEDULING) used in concurrent iterator parallelism (from the iterator_scheduling specification)
StringArray	hybridMethodNames
	array of methods for the sequential and collaborative hybrid meta-iterators (from the method_name_list specification)
StringArray	hybridModelPointers
	array of models for the sequential and collaborative hybrid meta-iterators (from the model_pointer_list specification)
StringArray	hybridMethodPointers
	array of methods for the sequential and collaborative hybrid meta-iterators (from the method_pointer_list specification)
String	hybridGlobalMethodName
	global method name for embedded hybrids (from the global_method_name specification)
String	hybridGlobalModelPointer
	global model pointer for embedded hybrids (from the global_model_pointer specification)
String	hybridGlobalMethodPointer
	global method pointer for embedded hybrids (from the global_method_pointer specification)
String	hybridLocalMethodName
	local method name for embedded hybrids (from the local_method_name specification)
String	hybridLocalModelPointer
	local model pointer for embedded hybrids (from the local_model_pointer specification)
String	hybridLocalMethodPointer
	local method pointer for embedded hybrids (from the local_method_pointer specification)
Real	hybridLSProb
	local search probability for embedded hybrids (from the local_search_probability specification)
int	concurrentRandomJobs
	number of random jobs to perform in the pareto_set and multi_start meta-iterators (from the random_starts and random_weight_sets specifications)
RealVector	concurrentParameterSets
	user-specified (i.e., nonrandom) parameter sets to evaluate in the pareto_set and multi_start meta-iterators (from the starting_points and weight_sets specifications)
unsigned short	softConvLimit
	number of consecutive iterations with change less than convergenceTolerance required to trigger convergence
bool	surrBasedLocalLayerBypass
	flag to indicate user-specification of a bypass of any/all layerings in evaluating truth response values in SBL.
RealVector	trustRegionInitSize
	initial trust region sizes in the surrogate-based local method (from the initial_size specification in MethodSBL), one size per surrogate model (notes: no trust region for the truth model; sizes are relative values, e.g., 0.1 = 10% of range of global bounds for each variable
Real	trustRegionMinSize
	minimum trust region size in the surrogate-based local method (from the minimum_size specification in MethodSBL), if the trust region size falls below this threshold the SBL iterations are terminated (note: if kriging is used with SBL, the min trust region size is set to 1.0e-3 in attempt to avoid ill-conditioned matrixes that arise in kriging over small trust regions)
Real	trustRegionContractTrigger
	trust region minimum improvement level (ratio of actual to predicted decrease in objective fcn) in the surrogate-based local method (from the contract_threshold specification in MethodSBL), the trust region shrinks or is rejected if the ratio is below this value ("eta_1" in the Conn-Gould-Toint trust region book)
Real	trustRegionExpandTrigger
	trust region sufficient improvement level (ratio of actual to predicted decrease in objective fn) in the surrogate-based local method (from the expand_threshold specification in MethodSBL), the trust region expands if the ratio is above this value ("eta_2" in the Conn-Gould-Toint trust region book)
Real	trustRegionContract
	trust region contraction factor in the surrogate-based local method (from the contraction_factor specification in MethodSBL)
Real	trustRegionExpand
	trust region expansion factor in the surrogate-based local method (from the expansion_factor specification in MethodSBL)
short	surrBasedLocalSubProbObj
	SBL approximate subproblem objective: ORIGINAL_PRIMARY, SINGLE_OBJECTIVE, LAGRANGIAN_OBJECTIVE, or AUGMENTED_LAGRANGIAN_OBJECTIVE.
short	surrBasedLocalSubProbCon
	SBL approximate subproblem constraints: NO_CONSTRAINTS, LINEARIZED_CONSTRAINTS, or ORIGINAL_CONSTRAINTS.
short	surrBasedLocalMeritFn
	SBL merit function type: BASIC_PENALTY, ADAPTIVE_PENALTY, BASIC_LAGRANGIAN, or AUGMENTED_LAGRANGIAN.
short	surrBasedLocalAcceptLogic
	SBL iterate acceptance logic: TR_RATIO or FILTER.
short	surrBasedLocalConstrRelax
	SBL constraint relaxation method: NO_RELAX or HOMOTOPY.
bool	surrBasedGlobalReplacePts
	user-specified method for adding points to the set upon which the next surrogate is based in the surrogate_based_global method.
String	dlDetails
	string of options for a dynamically linked solver
void *	dlLib
	handle to dynamically loaded library
int	verifyLevel
	the verify_level specification in MethodNPSOLDC
Real	functionPrecision
	the function_precision specification in MethodNPSOLDC and the EPSILON specification in NOMAD
Real	lineSearchTolerance
	the linesearch_tolerance specification in MethodNPSOLDC
Real	absConvTol
	absolute function convergence tolerance
Real	xConvTol
	x-convergence tolerance
Real	singConvTol
	singular convergence tolerance
Real	singRadius
	radius for singular convergence test
Real	falseConvTol
	false-convergence tolerance
Real	initTRRadius
	initial trust radius
int	covarianceType
	kind of covariance required
bool	regressDiag
	whether to print the regression diagnostic vector
String	searchMethod
	the search_method specification for Newton and nonlinear interior-point methods in MethodOPTPPDC
Real	gradientTolerance
	the gradient_tolerance specification in MethodOPTPPDC
Real	maxStep
	the max_step specification in MethodOPTPPDC
short	meritFn
	the merit_function specification for nonlinear interior-point methods in MethodOPTPPDC
Real	stepLenToBoundary
	the steplength_to_boundary specification for nonlinear interior-point methods in MethodOPTPPDC
Real	centeringParam
	the centering_parameter specification for nonlinear interior-point methods in MethodOPTPPDC
int	searchSchemeSize
	the search_scheme_size specification for PDS methods in MethodOPTPPDC
Real	initStepLength
	the initStepLength choice for nonlinearly constrained APPS in MethodAPPSDC
Real	contractStepLength
	the contractStepLength choice for nonlinearly constrained APPS in MethodAPPSDC
Real	threshStepLength
	the threshStepLength choice for nonlinearly constrained APPS in MethodAPPSDC
String	meritFunction
	the meritFunction choice for nonlinearly constrained APPS in MethodAPPSDC
Real	constrPenalty
	the constrPenalty choice for nonlinearly constrained APPS in MethodAPPSDC
Real	smoothFactor
	the initial smoothFactor value for nonlinearly constrained APPS in MethodAPPSDC
Real	constraintPenalty
	the initial constraint_penalty for COLINY methods in MethodAPPS, MethodSCOLIBDIR, MethodSCOLIBPS, MethodSCOLIBSW and MethodSCOLIBEA
bool	constantPenalty
	the constant_penalty flag for COLINY methods in MethodSCOLIBPS and MethodSCOLIBSW
Real	globalBalanceParam
	the global_balance_parameter for the DIRECT method in MethodSCOLIBDIR
Real	localBalanceParam
	the local_balance_parameter for the DIRECT method in MethodSCOLIBDIR
Real	maxBoxSize
	the max_boxsize_limit for the DIRECT method in MethodSCOLIBDIR
Real	minBoxSize
	the min_boxsize_limit for the DIRECT method in MethodSCOLIBDIR and MethodNCSUDC
String	boxDivision
	the division setting (major_dimension or all_dimensions) for the DIRECT method in MethodSCOLIBDIR
bool	mutationAdaptive
	the non_adaptive specification for the coliny_ea method in MethodSCOLIBEA
bool	showMiscOptions
	the show_misc_options specification in MethodSCOLIBDC
StringArray	miscOptions
	the misc_options specification in MethodSCOLIBDC
Real	solnTarget
	the solution_target specification in MethodSCOLIBDC
Real	crossoverRate
	the crossover_rate specification for EA methods in MethodSCOLIBEA
Real	mutationRate
	the mutation_rate specification for EA methods in MethodSCOLIBEA
Real	mutationScale
	the mutation_scale specification for EA methods in MethodSCOLIBEA
Real	mutationMinScale
	the min_scale specification for mutation in EA methods in MethodSCOLIBEA
Real	initDelta
	the initial_delta specification for APPS/COBYLA/PS/SW methods in MethodAPPS, MethodSCOLIBCOB, MethodSCOLIBPS, and MethodSCOLIBSW
Real	threshDelta
	the variable_tolerance specification for APPS/COBYLA/PS/SW methods in MethodAPPS, MethodSCOLIBCOB, MethodSCOLIBPS, and MethodSCOLIBSW
Real	contractFactor
	the contraction_factor specification for APPS/PS/SW methods in MethodAPPS, MethodSCOLIBPS, and MethodSCOLIBSW
int	newSolnsGenerated
	the new_solutions_generated specification for GA/EPSA methods in MethodSCOLIBEA
int	numberRetained
	the integer assignment to random, chc, or elitist in the replacement_type specification for GA/EPSA methods in MethodSCOLIBEA
bool	expansionFlag
	the no_expansion specification for APPS/PS/SW methods in MethodAPPS, MethodSCOLIBPS, and MethodSCOLIBSW
int	expandAfterSuccess
	the expand_after_success specification for PS/SW methods in MethodSCOLIBPS and MethodSCOLIBSW
int	contractAfterFail
	the contract_after_failure specification for the SW method in MethodSCOLIBSW
int	mutationRange
	the mutation_range specification for the pga_int method in MethodSCOLIBEA
int	totalPatternSize
	the total_pattern_size specification for PS methods in MethodSCOLIBPS
bool	randomizeOrderFlag
	the stochastic specification for the PS method in MethodSCOLIBPS
String	selectionPressure
	the fitness_type specification for EA methods in MethodSCOLIBEA
String	replacementType
	the replacement_type specification for EA methods in MethodSCOLIBEA
String	crossoverType
	the crossover_type specification for EA methods in MethodSCOLIBEA
String	mutationType
	the mutation_type specification for EA methods in MethodSCOLIBEA
String	exploratoryMoves
	the exploratory_moves specification for the PS method in MethodSCOLIBPS
String	patternBasis
	the pattern_basis specification for APPS/PS methods in MethodAPPS and MethodSCOLIBPS
String	betaSolverName
	beta solvers don't need documentation
short	evalSynchronize
	the synchronization setting for parallel pattern search methods in MethodSCOLIBPS and MethodAPPS
size_t	numCrossPoints
	The number of crossover points or multi-point schemes.
size_t	numParents
	The number of parents to use in a crossover operation.
size_t	numOffspring
	The number of children to produce in a crossover operation.
String	fitnessType
	the fitness assessment operator to use.
String	convergenceType
	The means by which this JEGA should converge.
Real	percentChange
	The minimum percent change before convergence for a fitness tracker converger.
size_t	numGenerations
	The number of generations over which a fitness tracker converger should track.
Real	fitnessLimit
	The cutoff value for survival in fitness limiting selectors (e.g., below_limit selector).
Real	shrinkagePercent
	The minimum percentage of the requested number of selections that must take place on each call to the selector (0, 1).
String	nichingType
	The niching type.
RealVector	nicheVector
	The discretization percentage along each objective.
size_t	numDesigns
	The maximum number of designs to keep when using the max_designs nicher.
String	postProcessorType
	The post processor type.
RealVector	distanceVector
	The discretization percentage along each objective.
String	initializationType
	The means by which the JEGA should initialize the population.
String	flatFile
	The filename to use for initialization.
String	logFile
	The filename to use for logging.
int	populationSize
	the population_size specification for GA methods in MethodSCOLIBEA
bool	printPopFlag
	The print_each_pop flag to set the printing of the population at each generation.
Real	volBoxSize
	the volume_boxsize_limit for the DIRECT method in MethodNCSUDC
int	numSymbols
	the symbols specification for DACE methods
bool	mainEffectsFlag
	the main_effects specification for sampling methods in MethodDDACE)
bool	latinizeFlag
	the latinize specification for FSU QMC and CVT methods in MethodFSUDACE
bool	volQualityFlag
	the quality_metrics specification for sampling methods (FSU QMC and CVT methods in MethodFSUDACE)
IntVector	sequenceStart
	the sequenceStart specification in MethodFSUDACE
IntVector	sequenceLeap
	the sequenceLeap specification in MethodFSUDACE
IntVector	primeBase
	the primeBase specification in MethodFSUDACE
int	numTrials
	the numTrials specification in MethodFSUDACE
String	trialType
	the trial_type specification in MethodFSUDACE
int	randomSeed
	the seed specification for COLINY, NonD, & DACE methods
SizetArray	randomSeedSeq
	the seed_sequence specification for multilevel UQ methods
Real	initMeshSize
	the initMeshSize choice for NOMAD in MethodNOMADDC
Real	minMeshSize
	the minMeshSize choice for NOMAD in MethodNOMADDC
String	historyFile
	the HISTORY_FILE specification for NOMAD
String	displayFormat
	the DISPLAY_STATS specification for NOMAD
Real	vns
	the VNS specification for NOMAD
int	neighborOrder
	the NEIGHBOR_ORDER specification for NOMAD
bool	showAllEval
	the DISPLAY_ALL_EVAL specification for NOMAD
String	useSurrogate
	the HAS_SGTE specification for NOMAD
int	maxCrossIterations
	maximum number of cross iterations
Real	solverTol
	optimization tolerance for FT regression
Real	solverRoundingTol
	Rounding tolerance for FT regression.
Real	statsRoundingTol
	arithmetic (rounding) tolerance for FT sums and products
unsigned short	startOrder
	starting polynomial order
unsigned short	kickOrder
	polynomial order increment when adapting
unsigned short	maxOrder
	maximum order of basis polynomials
bool	adaptOrder
	whether or not to adapt order by cross validation
size_t	startRank
	starting rank
size_t	kickRank
	rank increment when adapting
size_t	maxRank
	maximum rank
bool	adaptRank
	whether or not to adapt rank
size_t	maxCVRankCandidates
	maximum number of cross-validation candidates for adaptRank
unsigned short	maxCVOrderCandidates
	maximum number of cross-validation candidates for adaptOrder
short	c3AdvanceType
	quantity to increment (start rank, start order, max rank, max order, max rank + max order) for FT (uniform) p-refinement
UShortArray	startOrderSeq
	starting polynomial order
SizetArray	startRankSeq
	starting rank
int	numSamples
	the samples specification for NonD & DACE methods
bool	fixedSeedFlag
	flag for fixing the value of the seed among different NonD/DACE sample sets. This results in the use of the same sampling stencil/pattern throughout an execution with repeated sampling.
bool	fixedSequenceFlag
	flag for fixing the sequence for Halton or Hammersley QMC sample sets. This results in the use of the same sampling stencil/pattern throughout an execution with repeated sampling.
bool	vbdFlag
	the var_based_decomp specification for computing Sobol' indices via either PCE or sampling
Real	vbdDropTolerance
	the var_based_decomp tolerance for omitting Sobol' indices computed via either PCE or sampling
unsigned short	vbdViaSamplingMethod
	Sampling method for computing Sobol indices: Mahadevan (default) or Saltelli.
int	vbdViaSamplingNumBins
	Number of bins to use in case the Mahadevan method is selected (default is the square root of the number of samples)
bool	backfillFlag
	the backfill option allows one to augment in LHS sample by enforcing the addition of unique discrete variables to the sample
bool	pcaFlag
	Flag to specify the calculation of principal components when using LHS.
Real	percentVarianceExplained
	The percentage of variance explained by using a truncated number of principal components in PCA.
bool	wilksFlag
	Flag to specify use of Wilks formula to calculate num samples.
unsigned short	wilksOrder
	Wilks order parameter.
Real	wilksConfidenceLevel
	Wilks confidence interval parameter.
short	wilksSidedInterval
	Wilks sided interval type.
bool	rank1LatticeFlag
	Flag to indicate rank-1 lattice sampling.
bool	noRandomShiftFlag
	Flag to indicate randomization of rank-1 lattice rule.
int	log2MaxPoints
	(log2 of) maximum number of points of low-discrepancy generator
IntVector	generatingVector
	Inline generating vector of the rank-1 lattice rule.
String	generatingVectorFileName
	Name of file with generating vector.
bool	kuo
	Predefined generating vectors.
bool	cools_kuo_nuyens
bool	naturalOrdering
	Ordering of the lattice points.
bool	radicalInverseOrdering
bool	digitalNetFlag
	Flag to indicate digital net sampling.
bool	noDigitalShiftFlag
	Flag to indicate randomization of digital net.
bool	noScramblingFlag
	Flag to indicate scrambling of the digital net.
bool	mostSignificantBitFirst
	Flag to indicate integers in generating matrices are stored with most significant bit first.
bool	leastSignificantBitFirst
	Flag to indicate integers in generating matrices are stored with least significant bit first.
int	numberOfBits
	Number of bits in each integer in the generating matrices.
int	scrambleSize
	Number of rows in the linear scramble matrix.
IntVector	generatingMatrices
	Inline generating matrices of the digital net.
String	generatingMatricesFileName
	Name of file with generating matrices.
bool	joe_kuo
	Predefined generating matrices.
bool	sobol_order_2
bool	grayCodeOrdering
	Ordering of the digital net points.
bool	stdRegressionCoeffs
	flag indicating the calculation/output of standardized regression coefficients
bool	toleranceIntervalsFlag
	Flag to specify use of double sided tolerance interval equivalent normal.
Real	tiCoverage
	Coverage parameter for the calculation of double sided tolerance interval equivalent normal.
Real	tiConfidenceLevel
	Confidence level parameter for the calculation of double sided tolerance interval equivalent normal.
bool	respScalingFlag
	flag to indicate bounds-based scaling of current response data set prior to build in surrogate-based methods; important for ML/MF data fits of decaying discrepancy data using regression with absolute tolerances
unsigned short	vbdOrder
	a sub-specification of vbdFlag: interaction order limit for calculation/output of component VBD indices
short	covarianceControl
	restrict the calculation of a full response covariance matrix for high dimensional outputs: {DEFAULT,DIAGONAL,FULL}_COVARIANCE
String	rngName
	the basic random-number generator for NonD
short	refinementType
	refinement type for stochastic expansions from dimension refinement keyword group
short	refinementControl
	refinement control for stochastic expansions from dimension refinement keyword group
short	nestingOverride
	override for default point nesting policy: NO_NESTING_OVERRIDE, NESTED, or NON_NESTED
short	growthOverride
	override for default point growth restriction policy: NO_GROWTH_OVERRIDE, RESTRICTED, or UNRESTRICTED
short	expansionType
	enumeration for u-space type that defines u-space variable targets for probability space transformations: EXTENDED_U (default), ASKEY_U, PARTIAL_ASKEY_U, STD_NORMAL_U, or STD_UNIFORM_U
bool	piecewiseBasis
	boolean indicating presence of piecewise keyword
short	expansionBasisType
	enumeration for type of basis in sparse grid interpolation (Pecos::{NODAL,HIERARCHICAL}_INTERPOLANT) or regression (Pecos::{TENSOR_PRODUCT,TOTAL_ORDER,ADAPTED}_BASIS).
UShortArray	quadratureOrderSeq
	the quadrature_order_sequence specification in MethodNonDPCE and MethodNonDSC
UShortArray	sparseGridLevelSeq
	the sparse_grid_level_sequence specification in MethodNonDPCE and MethodNonDSC
UShortArray	expansionOrderSeq
	the expansion_order_sequence specification in MethodNonDPCE
SizetArray	collocationPointsSeq
	the collocation_points_sequence specification in MethodNonDPCE
SizetArray	expansionSamplesSeq
	the expansion_samples_sequence specification in MethodNonDPCE
unsigned short	quadratureOrder
	the quadrature_order specification in MethodNonDPCE and MethodNonDSC
unsigned short	sparseGridLevel
	the sparse_grid_level specification in MethodNonDPCE and MethodNonDSC
unsigned short	expansionOrder
	the expansion_order specification in MethodNonDPCE
size_t	collocationPoints
	the collocation_points specification in MethodNonDPCE
size_t	expansionSamples
	the expansion_samples specification in MethodNonDPCE
RealVector	anisoDimPref
	the dimension_preference specification for tensor and sparse grids and expansion orders in MethodNonDPCE and MethodNonDSC
unsigned short	cubIntOrder
	the cubature_integrand specification in MethodNonDPCE
Real	collocationRatio
	the collocation_ratio specification in MethodNonDPCE
Real	collocRatioTermsOrder
	order applied to the number of expansion terms when applying or computing the collocation ratio within regression PCE; based on the ratio_order specification in MethodNonDPCE
short	regressionType
	type of regression: LS, OMP, BP, BPDN, LARS, or LASSO
short	lsRegressionType
	type of least squares regression: SVD or EQ_CON_QR
RealVector	regressionNoiseTol
	noise tolerance(s) for OMP, BPDN, LARS, and LASSO
Real	regressionL2Penalty
	L2 regression penalty for a variant of LASSO known as the elastic net method (default of 0 gives standard LASSO)
bool	crossValidation
	flag indicating the use of cross-validation across expansion orders (given a prescribed maximum order) and, for some methods, noise tolerances
bool	crossValidNoiseOnly
	flag indicating the restriction of cross-validation to estimate only the most effective noise tolerance; used to reduce cost from performing CV over both noise tolerances and expansion orders
unsigned short	adaptedBasisAdvancements
	initial grid level for the ADAPTED_BASIS_EXPANDING_FRONT approach to defining the candidate basis for sparse recovery (compressed sensing)
bool	normalizedCoeffs
	flag indicating the output of PCE coefficients corresponding to normalized basis polynomials
String	pointReuse
	allows PCE construction to reuse points from previous sample sets or data import using the reuse_points specification in MethodNonDPCE
bool	tensorGridFlag
	flag for usage of a sub-sampled set of tensor-product grid points within regression PCE; based on the tensor_grid specification in MethodNonDPCE
UShortArray	tensorGridOrder
	order of tensor-product grid points that are sub-sampled within orthogonal least interpolation PCE; based on the tensor_grid specification in MethodNonDPCE
String	importExpansionFile
	the import_expansion_file specification in MethodNonDPCE
String	exportExpansionFile
	the export_expansion_file specification in MethodNonDPCE
unsigned short	sampleType
	the sample_type specification in MethodNonDMC, MethodNonDPCE, and MethodNonDSC
bool	dOptimal
	whether to generate D-optimal designs
size_t	numCandidateDesigns
	number of candidate designss in D-optimal design selection
String	reliabilityIntegration
	the first_order or second_order integration selection in MethodNonDLocalRel
unsigned short	integrationRefine
	the import, adapt_import, or mm_adapt_import integration refinement selection in MethodNonDLocalRel, MethodNonDPCE, and MethodNonDSC
IntVector	refineSamples
	Sequence of refinement samples, e.g., the size of the batch (e.g. number of supplemental points added) to be added to be added to the build points for an emulator at each iteration.
unsigned short	optSubProbSolver
	the method used for solving an optimization sub-problem (e.g., pre-solve for the MAP point)
unsigned short	numericalSolveMode
	approach for overriding an analytic solution based on simplifying assumptions that might be violated, suggesting a fallback approach, or lacking robustness, suggesting an optional override replacement
SizetArray	pilotSamples
	the pilot_samples selection in ML/MF methods
short	ensemblePilotSolnMode
	the solution_mode selection for ML/MF sampling methods
short	pilotGroupSampling
	the group sampling approach for pilot sampling in ML BLUE: independent or shared
short	groupThrottleType
	approach to restricting the total number of groups in group estimators
unsigned short	groupSizeThrottle
	restricting the number of group combinations in group estimators by enforcing a maximum size in terms of the number of models per group
bool	truthPilotConstraint
	the truth_fixed_by_pilot flag for ACV methods
short	dagRecursionType
	option specified for extent of DAG enumeration within search_model_graphs for generalized ACV methods
unsigned short	dagDepthLimit
	option specified for depth_limit in generalized ACV methods with partial graph recursion
short	modelSelectType
	option specified for model_selection within search_model_graphs for generalized ACV methods
RealVector	relaxFactorSequence
	sequence of (under-)relaxation factors that are applied to sample increments computed in the latest ML/MF allocation solution: f_i = f_seq[i] for iter <= len, f_seq[last_i] for iter > len
Real	relaxFixedFactor
	fixed (under-)relaxation factor applied to sample increments computed in the latest ML/MF allocation solution: f_i = f_fixed for all iter
Real	relaxRecursiveFactor
	(under-)relaxation factor that is applied to sample increments computed in the latest ML/MF allocation solution. The relaxation factor for each iteration (f_i) is defined from recursive application of the user specification (f_recur) to the remaining partition of unity: f_{i+1} = f_i + f_recur (1 - f_i), where f_0 = 0 gives f_1 = f_recur. E.g., f_recur = 0.5 gives f_i = 0.5, .75, .875, .9375, ...
short	allocationTarget
	the allocationTarget selection in MethodMultilevelMC
bool	useTargetVarianceOptimizationFlag
	the allocation_target selection in MethodMultilevelMC
short	qoiAggregation
	the qoi_aggregation_norm selection in MethodMultilevelMC
RealVector	scalarizationRespCoeffs
	the scalarization_response_mapping for defining the statistical goal in multilevel UQ methods
short	convergenceToleranceType
	the convergence_tolerance_type selection in MethodMultilevelMC
short	convergenceToleranceTarget
	the convergence_tolerance_target selection in MethodMultilevelMC
short	multilevAllocControl
	the allocation_control selection in MethodMultilevelPCE
Real	multilevEstimatorRate
	the estimator_rate selection in MethodMultilevelPCE
short	multilevDiscrepEmulation
	type of discrepancy emulation in multilevel methods: distinct or recursive
short	finalStatsType
	specification of the type of final statistics in MethodNonD
short	finalMomentsType
	the final_moments specification in MethodNonD, subordinate to the type of final statistics
short	distributionType
	the distribution cumulative or complementary specification in MethodNonD
short	responseLevelTarget
	the compute probabilities, reliabilities, or gen_reliabilities specification in MethodNonD
short	responseLevelTargetReduce
	the system series or parallel specification in MethodNonD
RealVectorArray	responseLevels
	the response_levels specification in MethodNonD
RealVectorArray	probabilityLevels
	the probability_levels specification in MethodNonD
RealVectorArray	reliabilityLevels
	the reliability_levels specification in MethodNonD
RealVectorArray	genReliabilityLevels
	the gen_reliability_levels specification in MethodNonD
int	chainSamples
	the number of MCMC chain samples
int	buildSamples
	the number of samples to construct an emulator, e.g., for Bayesian calibration methods
int	samplesOnEmulator
	number of samples to perform on emulator
int	emulatorOrder
	The total order to be used in construction of a VPS surrogate.
short	emulatorType
	the emulator specification in MethodNonDBayesCalib
String	mcmcType
	the mcmc type specification in MethodNonDBayesCalib
bool	standardizedSpace
	use of standardized probability spaces for MCMC within Bayesian inference
bool	adaptPosteriorRefine
	flag indicating adaptive refinement of the emulator in regions of high posterior probability
bool	logitTransform
	flag indicating user activation of logit transform option within QUESO
bool	gpmsaNormalize
	whether to apply GPMSA-internal normalization
bool	posteriorStatsKL
	flag indicating the calculation of KL divergence between prior and posterior in Bayesian methods
bool	posteriorStatsMutual
	flag indicating the calculation of mutual information between prior and posterior in Bayesian methods
bool	posteriorStatsKDE
	flag indicating calculation of kernel density estimate of posterior distributions
bool	chainDiagnostics
	flag indicating calculation of chain diagnostics
bool	chainDiagnosticsCI
	flag indicating calculation of confidence intervals as a chain diagnositc
bool	modelEvidence
	flag indicating calculation of the evidence of the model
bool	modelEvidMC
	flag indicating use of Monte Carlo approximation for evidence calc.
int	evidenceSamples
	number of prior samples to use in model evidence calculation
bool	modelEvidLaplace
	flag indicating use of Laplace approximation for evidence calc.
String	proposalCovType
	the type of proposal covariance: user, derivatives, or prior
Real	priorPropCovMult
	optional multiplier for prior-based proposal covariance
int	proposalCovUpdatePeriod
	number of samples after which to update the proposal covariance from misfit Hessian (using residual values and derivatives)
String	proposalCovInputType
	the format of proposal covariance input: diagonal or matrix
RealVector	proposalCovData
	raw list of real data for the proposal covariance
String	proposalCovFile
	file from which to read proposal covariance in diagonal or matrix format
String	advancedOptionsFilename
	file containing advanced ROL option overrides
String	quesoOptionsFilename
	file containing advanced QUESO option overrides
String	fitnessMetricType
	the fitness metric type specification in MethodNonDAdaptive
String	batchSelectionType
	the batch selection type specification in MethodNonDAdaptive
String	lipschitzType
	the Lipschitz type specification in MethodNonDPOFDarts (e.g. either local or global estimation)
unsigned short	calibrateErrorMode
	calibration mode for observation error multipliers (CALIBRATE_*)
RealVector	hyperPriorAlphas
	hyperparameters inverse gamma prior alphas
RealVector	hyperPriorBetas
	hyperparameters inverse gamma prior alphas
int	burnInSamples
	number of MCMC samples to discard from acceptance chain
int	subSamplingPeriod
	period or skip in post-processing the acceptance chain
bool	calModelDiscrepancy
	flag to calculate model discrepancy
size_t	numPredConfigs
	number of prediction configurations at which to calculate model discrepancy
RealVector	predictionConfigList
	list of prediction configurations at which to calculate model discrepancy
String	importPredConfigs
	whether to import prediction configurations at which to calculate model discrepancy
unsigned short	importPredConfigFormat
	tabular format for prediction configurations import file
String	modelDiscrepancyType
	type of model discrepancy emulation
short	polynomialOrder
	polynomial order for model discrepancy calculations: either gaussian process trend order or polynomial basis order
String	exportCorrModelFile
	specify the name of file to which corrected model (model+discrepancy) calculations are output
unsigned short	exportCorrModelFormat
	tabular format for corrected model (model+discrepancy) export file
String	exportCorrVarFile
	specify the name of file to which corrected model variance calculations are output
unsigned short	exportCorrVarFormat
	tabular format for corrected model variance export file
String	exportDiscrepFile
	specify the name of file to which discrepancy calculations are output
unsigned short	exportDiscrepFormat
	tabular format for model discrepancy export file
bool	adaptExpDesign
	whether to perform adaptive Bayesian design of experiments
String	importCandPtsFile
	whether to import candidate design points for adaptive Bayesian experimental design
unsigned short	importCandFormat
	tabular format for the candidate design points import file
size_t	numCandidates
	number of candidate designs for adaptive Bayesian experimental design
int	maxHifiEvals
	maximum number of highfidelity model runs to be used for adaptive Bayesian experimental design
int	batchSize
	number of optimal designs selected per iteration of experimental design algorithm; also number of concurrent GP refinement points for EGO
int	batchSizeExplore
	portion of batchSize earmarked for exploration rather than acquisition
bool	mutualInfoKSG2
	indicate that the KSG2 algorithm is to be employed in the calculation of the mutual information
int	numChains
	number of concurrent chains
int	numCR
	number of CR-factors
int	crossoverChainPairs
	number of crossover chain pairs
Real	grThreshold
	threshold for the Gelmin-Rubin statistic
int	jumpStep
	how often to perform a long jump in generations
int	numPushforwardSamples
	Number of samples from the prior that is pushed forward through the model to obtain the initial set of pushforward samples.
String	dataDistType
	the type of data distribution: kde, or gaussian
String	dataDistCovInputType
	the format of data distribution gaussian covariance input: diagonal or matrix
RealVector	dataDistMeans
	raw list of real data for the data distribution gaussian means
RealVector	dataDistCovariance
	raw list of real data for the data distribution gaussian covariance
String	dataDistFile
	file from which to read data distribution data (covariance or samples )
String	posteriorDensityExportFilename
	The filename of the export file containing an arbitrary set of samples and their corresponding density values.
String	posteriorSamplesExportFilename
	The filename of the export file containing samples from the posterior and their corresponding density values.
String	posteriorSamplesImportFilename
	The filename of the import file containing samples at which the posterior will be evaluated.
bool	generatePosteriorSamples
	Flag specifying whether to generate random samples from the posterior.
bool	evaluatePosteriorDensity
	Flag specifying whether to evaluate the posterior density at a set of samples.
int	drNumStages
	DR num stages.
String	drScaleType
	DR scale type.
Real	drScale
	DR scale.
int	amPeriodNumSteps
	AM period num steps.
int	amStartingStep
	AM staring step.
Real	amScale
	AM scale.
Real	malaStepSize
	MALA step size.
RealVector	finalPoint
	the final_point specification in MethodPSVPS
RealVector	stepVector
	the step_vector specification in MethodPSVPS and MethodPSCPS
int	numSteps
	the num_steps specification in MethodPSVPS
IntVector	stepsPerVariable
	the deltas_per_variable specification in MethodPSCPS
RealVector	listOfPoints
	the list_of_points specification in MethodPSLPS
String	pstudyFilename
	the import_points_file spec for a file-based parameter study
unsigned short	pstudyFileFormat
	tabular format for the parameter study points file
bool	pstudyFileActive
	whether to import active variables only
UShortArray	varPartitions
	the partitions specification for PStudy method in MethodPSMPS
Real	refinementRate
	rate of mesh refinement in Richardson extrapolation
String	importBuildPtsFile
	the file name from the import_build_points_file specification
unsigned short	importBuildFormat
	tabular format for the build point import file
bool	importBuildActive
	whether to import active variables only
String	importApproxPtsFile
	the file name from the import_approx_points_file specification
unsigned short	importApproxFormat
	tabular format for the approx point import file
bool	importApproxActive
	whether to import active variables only
String	exportApproxPtsFile
	the file name from the export_approx_points_file specification
unsigned short	exportApproxFormat
	tabular format for the approx point export file
String	exportMCMCPtsFile
	the file name from the export_mcmc_points_file specification
bool	exportSampleSeqFlag
	flag for exporting the sequence of sample increments within multilevel sampling from the export_sample_sequence specification
unsigned short	exportSamplesFormat
	tabular format for the MCMC chain and MLMC sample sequence exports
bool	exportSurrogate
	Option to turn on surrogate model export (export_model)
String	modelExportPrefix
	the filename prefix for export_model
unsigned short	modelExportFormat
	Format selection for export_model.

Private Member Functions
	DataMethodRep ()
	constructor
void	write (std::ostream &s) const
	write a DataInterfaceRep object to an std::ostream
void	read (MPIUnpackBuffer &s)
	read a DataInterfaceRep object from a packed MPI buffer
void	write (MPIPackBuffer &s) const
	write a DataInterfaceRep object to a packed MPI buffer

Friends
class	DataMethod
	the handle class can access attributes of the body class directly

Detailed Description

Body class for method specification data.

The DataMethodRep class is used to contain the data from a method keyword specification. Default values are managed in the DataMethodRep constructor. Data is public to avoid maintaining set/get functions, but is still encapsulated within ProblemDescDB since ProblemDescDB::dataMethodList is private.

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

• DataMethod.hpp
• DataMethod.cpp