The primary namespace for DAKOTA. More...

Classes
class	ActiveSet
	Container class for active set tracking information. Contains the active set request vector and the derivative variables vector. More...

class	ActiveSubspaceModel
	Active subspace model for input (variable space) reduction. More...

class	AdaptedBasisModel
	Adapted basis model for input (variable space) reduction. More...

class	AdapterModel
	Derived model class which wraps call-back functions for solving minimization sub-problems. More...

class	AddAttributeVisitor
	Objects of this class are called by boost::appy_visitor to add attributes to HDF5 objects. More...

class	Analyzer
	Base class for NonD, DACE, and ParamStudy branches of the iterator hierarchy. More...

class	ApplicationInterface
	Derived class within the interface class hierarchy for supporting interfaces to simulation codes. More...

class	Approximation
	Base class for the approximation class hierarchy. More...

class	ApproximationInterface
	Derived class within the interface class hierarchy for supporting approximations to simulation-based results. More...

class	APPSEvalMgr
	Evaluation manager class for APPSPACK. More...

class	APPSOptimizer
	Wrapper class for HOPSPACK. More...

class	AppsTraits
	HOPSPACK-specific traits class. More...

class	ApreproWriter
	Utility used in derived write_core to write in aprepro format. More...

class	AttachScaleVisitor
	Objects of this class are called by boost::appy_visitor to add dimension scales (RealScale or StringScale) to HDF5 datasets. More...

struct	BaseConstructor
	Dummy struct for overloading letter-envelope constructors. More...

class	BootstrapSampler
	Actual boostrap sampler implementation for common data types. More...

class	BootstrapSampler< Teuchos::SerialDenseMatrix< OrdinalType, ScalarType > >
	Bootstrap sampler that is specialized to allow for the bootstrapping of RealMatrix. More...

class	BootstrapSamplerBase
	Base class/interface for the bootstrap sampler. More...

class	BootstrapSamplerWithGS
	A derived sampler to allow for user specification of the accessor methods. More...

class	C3Approximation
	Derived approximation class for global basis polynomials. More...

class	C3FnTrainData
	Handle for reference-counted pointer to C3FnTrainDataRep body. More...

class	COLINApplication

class	COLINOptimizer
	Wrapper class for optimizers defined using COLIN. More...

class	COLINTraits
	A version of TraitsBase specialized for COLIN optimizers. More...

class	CollabHybridMetaIterator
	Meta-iterator for hybrid iteration using multiple collaborating optimization and nonlinear least squares methods. More...

class	CommandLineHandler
	Utility class for managing command line inputs to DAKOTA. More...

class	CommandShell
	Utility class which defines convenience operators for spawning processes with system calls. More...

class	ConcurrentMetaIterator
	Meta-iterator for multi-start iteration or pareto set optimization. More...

class	CONMINOptimizer
	Wrapper class for the CONMIN optimization library. More...

class	CONMINTraits
	A version of TraitsBase specialized for CONMIN optimizers. More...

class	ConsoleRedirector

class	Constraints
	Base class for the variable constraints class hierarchy. More...

class	DAGSolutionData
	Container class for numerical solutions for a given DAG. More...

class	DakotaROLEqConstraints

class	DakotaROLEqConstraintsGrad

class	DakotaROLEqConstraintsHess

class	DakotaROLIneqConstraints

class	DakotaROLIneqConstraintsGrad

class	DakotaROLIneqConstraintsHess

class	DakotaROLObjective

class	DakotaROLObjectiveGrad

class	DakotaROLObjectiveHess

class	DataEnvironment
	Handle class for environment specification data. More...

class	DataEnvironmentRep
	Body class for environment specification data. More...

class	DataFitSurrBasedLocalTraits
	Class for provably-convergent local surrogate-based optimization and nonlinear least squares. More...

class	DataFitSurrModel
	Derived model class within the surrogate model branch for managing data fit surrogates (global and local) More...

class	DataInterface
	Handle class for interface specification data. More...

class	DataMethod
	Handle class for method specification data. More...

class	DataMethodRep
	Body class for method specification data. More...

class	DataModel
	Handle class for model specification data. More...

class	DataModelRep
	Body class for model specification data. More...

class	DataResponses
	Handle class for responses specification data. More...

class	DataResponsesRep
	Body class for responses specification data. More...

class	DataTransformModel
	Data transformation specialization of RecastModel. More...

class	DataVariables
	Handle class for variables specification data. More...

class	DataVariablesRep
	Body class for variables specification data. More...

class	DDACEDesignCompExp
	Wrapper class for the DDACE design of experiments library. More...

class	DerivInformedPropCovLogitTK
	Dakota transition kernel that updates proposal covariance based on derivatives (for logit random walk case) More...

class	DerivInformedPropCovTK
	Dakota transition kernel that updates proposal covariance based on derivatives (for random walk case) More...

class	DirectApplicInterface
	Derived application interface class which spawns simulation codes and testers using direct procedure calls. More...

class	DiscrepancyCorrection
	Base class for discrepancy corrections. More...

class	DLSolverTraits
	A version of TraitsBase specialized for DLSolver. More...

class	DOTTraits
	Wrapper class for the DOT optimization library. More...

class	EffGlobalMinimizer

class	EffGlobalTraits
	Implementation of Efficient Global Optimization/Least Squares algorithms. More...

class	EmbedHybridMetaIterator
	Meta-iterator for closely-coupled hybrid iteration, typically involving the embedding of local search methods within global search methods. More...

class	EnsembleSurrModel
	Derived model class within the surrogate model branch for managing a truth model alongside approximation models of varying fidelity. More...

class	Environment
	Base class for the environment class hierarchy. More...

class	ExecutableEnvironment
	Environment corresponding to execution as a stand-alone application. More...

class	ExperimentData
	Interpolation method for interpolating between experimental and model data. I need to work on inputs/outputs to this method. For now, this assumes interpolation of functional data. More...

class	ExperimentResponse
	Container class for response functions and their derivatives. ExperimentResponse provides the body class. More...

class	FileReadException
	base class for Dakota file read exceptions (to allow catching both tabular and general file truncation issues) More...

class	ForkApplicInterface
	Derived application interface class which spawns simulation codes using fork/execvp/waitpid. More...

class	FSUDesignCompExp
	Wrapper class for the FSUDace QMC/CVT library. More...

class	FunctionEvalFailure
	exception class for function evaluation failures More...

class	GaussProcApproximation
	Derived approximation class for Gaussian Process implementation. More...

class	GeneralReader
	Utility used in derived read_core to read in generic format. More...

class	GeneralWriter
	Utility used in derived write_core to write in generic format. More...

class	GetLongOpt
	GetLongOpt is a general command line utility from S. Manoharan (Advanced Computer Research Institute, Lyon, France). More...

class	Graphics
	The Graphics class provides a single interface to 2D (motif) and 3D (PLPLOT) graphics; there is only one instance of this OutputManager::dakotaGraphics. More...

class	GridApplicInterface
	Derived application interface class which spawns simulation codes using grid services such as Condor or Globus. More...

class	HDF5IOHelper

class	HierarchSurrBasedLocalTraits
	Class for multilevel-multifidelity optimization algorithm. More...

struct	IntegerScale
	Data structure for storing int-valued dimension scale. More...

class	Interface
	Base class for the interface class hierarchy. More...

class	Iterator
	Base class for the iterator class hierarchy. More...

class	IteratorScheduler
	This class encapsulates scheduling operations for concurrent sub-iteration within an outer level context (e.g., meta-iteration, nested models). More...

class	JEGAOptimizer
	A version of Dakota::Optimizer for instantiation of John Eddy's Genetic Algorithms (JEGA). More...

class	JEGATraits
	A version of TraitsBase specialized for John Eddy's Genetic Algorithms (JEGA). More...

class	LabelsWriter
	Utility used in derived write_core to write labels in tabular format. More...

class	LeastSq
	Base class for the nonlinear least squares branch of the iterator hierarchy. More...

class	LibraryEnvironment
	Environment corresponding to execution as an embedded library. More...

struct	LightWtBaseConstructor
	Dummy struct for overloading constructors used in on-the-fly Model instantiations. More...

struct	MatchesWC
	Predicate that returns true when the passed path matches the wild_card with which it was configured. Currently supports * and ?. More...

class	MatlabInterface

class	MetaIterator
	Base class for meta-iterators. More...

class	Minimizer
	Base class for the optimizer and least squares branches of the iterator hierarchy. More...

class	MinimizerAdapterModel
	Derived model class which wraps call-back functions for solving minimization sub-problems. More...

class	MixedVarConstraints
	Derived class within the Constraints hierarchy which separates continuous and discrete variables (no domain type array merging). More...

class	MixedVariables
	Derived class within the Variables hierarchy which separates continuous and discrete variables (no domain type array merging). More...

class	Model
	Base class for the model class hierarchy. More...

class	MPIManager
	Class MPIManager to manage Dakota's MPI world, which may be a subset of MPI_COMM_WORLD. More...

class	MPIPackBuffer
	Class for packing MPI message buffers. More...

class	MPIUnpackBuffer
	Class for unpacking MPI message buffers. More...

class	NCSUOptimizer
	Wrapper class for the NCSU DIRECT optimization library. More...

class	NCSUTraits
	A version of TraitsBase specialized for NCSU optimizers. More...

class	NestedModel
	Derived model class which performs a complete sub-iterator execution within every evaluation of the model. More...

class	NIDRProblemDescDB
	The derived input file database utilizing the new IDR parser. More...

struct	NL2Res
	Auxiliary information passed to calcr and calcj via ur. More...

class	NL2SOLLeastSq
	Wrapper class for the NL2SOL nonlinear least squares library. More...

class	NL2SOLLeastSqTraits
	A version of TraitsBase specialized for NL2SOL nonlinear least squares library. More...

class	NLPQLPTraits
	Wrapper class for the NLPQLP optimization library, Version 2.0. More...

class	NLSSOLLeastSq
	Wrapper class for the NLSSOL nonlinear least squares library. More...

class	NLSSOLLeastSqTraits
	A version of TraitsBase specialized for NLSSOL nonlinear least squares library. More...

struct	NoDBBaseConstructor
	Dummy struct for overloading constructors used in on-the-fly instantiations without ProblemDescDB support. More...

class	NomadTraits
	Wrapper class for NOMAD Optimizer. More...

class	NonD
	Base class for all nondetermistic iterators (the DAKOTA/UQ branch). More...

class	NonDACVSampling
	Perform Approximate Control Variate Monte Carlo sampling for UQ. More...

class	NonDAdaptImpSampling
	Class for the Adaptive Importance Sampling methods within DAKOTA. More...

class	NonDAdaptiveSampling
	Class for testing various Adaptively sampling methods using geometric, statisctical, and topological information of the surrogate. More...

class	NonDBayesCalibration
	Base class for Bayesian inference: generates posterior distribution on model parameters given experimental data. More...

class	NonDC3FunctionTrain
	Nonintrusive uncertainty quantification with the C3 library ... More...

class	NonDCalibration

class	NonDCubature
	Derived nondeterministic class that generates N-dimensional numerical cubature points for evaluation of expectation integrals. More...

class	NonDDREAMBayesCalibration
	Bayesian inference using the DREAM approach. More...

class	NonDEnsembleSampling
	Base class for Monte Carlo sampling across Model ensembles. More...

class	NonDExpansion
	Base class for polynomial chaos expansions (PCE), stochastic collocation (SC) and functional tensor train (FT) More...

class	NonDGenACVSampling
	Perform Generalized Approximate Control Variate Monte Carlo sampling. More...

class	NonDGlobalEvidence
	Class for the Dempster-Shafer Evidence Theory methods within DAKOTA/UQ. More...

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

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

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

class	NonDGPImpSampling
	Class for the Gaussian Process-based Importance Sampling method. More...

class	NonDGPMSABayesCalibration
	Generates posterior distribution on model parameters given experiment data. More...

class	NonDHierarchSampling
	Performs Hierarch Monte Carlo sampling for uncertainty quantification. More...

class	NonDIntegration
	Derived nondeterministic class that generates N-dimensional numerical integration points for evaluation of expectation integrals. More...

class	NonDInterval
	Base class for interval-based methods within DAKOTA/UQ. More...

class	NonDLHSEvidence
	Class for the Dempster-Shafer Evidence Theory methods within DAKOTA/UQ. More...

class	NonDLHSInterval
	Class for the LHS-based interval methods within DAKOTA/UQ. More...

class	NonDLHSSampling
	Performs LHS and Monte Carlo sampling for uncertainty quantification. More...

class	NonDLHSSingleInterval
	Class for pure interval propagation using LHS. More...

class	NonDLocalEvidence
	Class for the Dempster-Shafer Evidence Theory methods within DAKOTA/UQ. More...

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

class	NonDLocalReliability
	Class for the reliability methods within DAKOTA/UQ. More...

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

class	NonDMultifidelitySampling
	Perform Approximate Control Variate Monte Carlo sampling for UQ. More...

class	NonDMultilevControlVarSampling
	Performs multilevel-multifidelity Monte Carlo sampling for uncertainty quantification. More...

class	NonDMultilevelFunctionTrain
	Nonintrusive polynomial chaos expansion approaches to uncertainty quantification. More...

class	NonDMultilevelPolynomialChaos
	Nonintrusive polynomial chaos expansion approaches to uncertainty quantification. More...

class	NonDMultilevelSampling
	Performs Multilevel Monte Carlo sampling for uncertainty quantification. More...

class	NonDMultilevelStochCollocation
	Nonintrusive stochastic collocation approaches to uncertainty quantification. More...

class	NonDMUQBayesCalibration
	Dakota interface to MUQ (MIT Uncertainty Quantification) library. More...

class	NonDNonHierarchSampling
	Base class for non-hierarchical ensemble-based Monte Carlo sampling. More...

class	NonDPOFDarts
	Base class for POF Dart methods within DAKOTA/UQ. More...

class	NonDPolynomialChaos
	Nonintrusive polynomial chaos expansion approaches to uncertainty quantification. More...

class	NonDQuadrature
	Derived nondeterministic class that generates N-dimensional numerical quadrature points for evaluation of expectation integrals over uncorrelated standard normals/uniforms/exponentials/betas/gammas. More...

class	NonDQUESOBayesCalibration
	Bayesian inference using the QUESO library from UT Austin. More...

class	NonDReliability
	Base class for the reliability methods within DAKOTA/UQ. More...

class	NonDRKDDarts
	Base class for the Recursive k-d Dart methods within DAKOTA/UQ. More...

class	NonDSampling
	Base class for common code between NonDLHSSampling, NonDAdaptImpSampling, and other specializations. More...

class	NonDSparseGrid
	Derived nondeterministic class that generates N-dimensional Smolyak sparse grids for numerical evaluation of expectation integrals over independent standard random variables. More...

class	NonDStochCollocation
	Nonintrusive stochastic collocation approaches to uncertainty quantification. More...

class	NonDSurrogateExpansion
	Generic uncertainty quantification with Model-based stochastic expansions. More...

class	NonDWASABIBayesCalibration
	WASABI - Weighted Adaptive Surrogate Approximations for Bayesian Inference. More...

class	NonlinearCGOptimizer

class	NonlinearCGTraits
	A version of TraitsBase specialized for NonlinearCG optimizers. More...

class	NOWPACBlackBoxEvaluator
	Derived class for plugging Dakota evaluations into NOWPAC solver. More...

class	NOWPACOptimizer
	Wrapper class for the (S)NOWPAC optimization algorithms from Florian Augustin (MIT) More...

class	NOWPACTraits
	A version of TraitsBase specialized for NOWPAC optimizers. More...

class	NPSOLTraits
	Wrapper class for the NPSOL optimization library. More...

class	OptDartsOptimizer
	Wrapper class for OptDarts Optimizer. More...

class	OptDartsTraits
	A version of TraitsBase specialized for OptDarts. More...

class	Optimizer
	Base class for the optimizer branch of the iterator hierarchy. More...

class	OutputManager
	Class to manage redirection of stdout/stderr, keep track of current redir state, and manage rank 0 output. Also manage tabular data output for post-processing with Matlab, Tecplot, etc. and delegate to Graphics for X Windows Graphics. More...

class	OutputWriter

class	ParallelConfiguration
	Container class for a set of ParallelLevel list iterators that collectively identify a particular multilevel parallel configuration. More...

class	ParallelLevel
	Container class for the data associated with a single level of communicator partitioning. More...

class	ParallelLibrary
	Class for partitioning multiple levels of parallelism and managing message passing within these levels. More...

class	ParamResponsePair
	Container class for a variables object, a response object, and an evaluation id. More...

class	ParamStudy
	Class for vector, list, centered, and multidimensional parameter studies. More...

struct	partial_prp_equality
	predicate for comparing ONLY the interfaceId and Vars attributes of PRPair More...

struct	partial_prp_hash
	wrapper to delegate to the ParamResponsePair hash_value function More...

class	PebbldBranching
	Main Branching class for the PEBBL-based Minimizer. More...

class	PebbldBranchSub
	Sub Branch class for the PEBBL-based Minimizer. More...

class	PebbldTraits
	Wrapper class for experimental PebbldMinimizer. More...

class	PecosApproximation
	Derived approximation class for global basis polynomials. More...

class	PrefixingLineFilter

class	ProbabilityTransformModel
	Probability transformation specialization of RecastModel. More...

class	ProblemDescDB
	The database containing information parsed from the DAKOTA input file. More...

class	ProcessApplicInterface
	Derived application interface class that spawns a simulation code using a separate process and communicates with it through files. More...

class	ProcessHandleApplicInterface
	Derived application interface class that spawns a simulation code using a separate process, receives a process identifier, and communicates with the spawned process through files. More...

class	ProgramOptions
	ProgramOptions stores options whether from the CLH or from library user; initially valid only on worldRank = 0, but then broadcast in ParallelLibrary::push_output_tag() More...

class	PStudyDACE
	Base class for managing common aspects of parameter studies and design of experiments methods. More...

class	PSUADEDesignCompExp
	Wrapper class for the PSUADE library. More...

class	Pybind11Interface

class	PythonInterface

class	QMEApproximation
	Derived approximation class for QMEA Quadratic Multipoint Exponential Approximation (a multipoint approximation). More...

class	QuesoJointPdf
	Dakota specialization of QUESO generic joint PDF. More...

class	QuesoVectorRV
	Dakota specialization of QUESO vector-valued random variable. More...

class	RandomFieldModel
	Random field model, capable of generating and then forward propagating. More...

struct	RealScale
	Data structure for storing real-valued dimension scale. More...

class	RecastModel
	Derived model class which provides a thin wrapper around a sub-model in order to recast the form of its inputs and/or outputs. More...

class	ReducedBasis

class	RelaxedVarConstraints
	Derived class within the Constraints hierarchy which employs relaxation of discrete variables. More...

class	RelaxedVariables
	Derived class within the Variables hierarchy which employs the relaxation of discrete variables. More...

class	Response
	Container class for response functions and their derivatives. Response provides the enveloper base class. More...

class	RestartWriter

struct	ResultAttribute
	Data structure for a single Real, String, or int valued attribute. More...

class	ResultsDBAny

class	ResultsDBBase

class	ResultsDBHDF5
	Manage interactions between ResultsManager and the low-level HDFIOHelper class. More...

class	ResultsEntry
	Class to manage in-core vs. file database lookups. More...

class	ResultsFileError
	exception throw for other results file read error More...

class	ResultsManager
	Results manager for iterator final data. More...

class	ResultsNames
	List of valid names for iterator results. More...

class	RichExtrapVerification
	Class for Richardson extrapolation for code and solution verification. More...

class	ROLOptimizer

class	ROLTraits

class	ScalingModel
	Scaling specialization of RecastModel. More...

class	ScalingOptions
	Simple container for user-provided scaling data, possibly expanded by replicates through the models. More...

class	ScilabInterface

class	SensAnalysisGlobal
	Class for a utility class containing correlation calculations and variance-based decomposition. More...

class	SeqHybridMetaIterator
	Method for sequential hybrid iteration using multiple optimization and nonlinear least squares methods on multiple models of varying fidelity. More...

class	SharedApproxData
	Base class for the shared approximation data class hierarchy. More...

class	SharedC3ApproxData
	Derived approximation class for global basis polynomials. More...

class	SharedPecosApproxData
	Derived approximation class for global basis polynomials. More...

class	SharedResponseData
	Container class encapsulating variables data that can be shared among a set of Response instances. More...

class	SharedResponseDataRep
	The representation of a SharedResponseData instance. This representation, or body, may be shared by multiple SharedResponseData handle instances. More...

class	SharedSurfpackApproxData
	Derived approximation class for Surfpack approximation classes. Interface between Surfpack and Dakota. More...

class	SharedVariablesData
	Container class encapsulating variables data that can be shared among a set of Variables instances. More...

class	SharedVariablesDataRep
	The representation of a SharedVariablesData instance. This representation, or body, may be shared by multiple SharedVariablesData handle instances. More...

class	SimulationModel
	Derived model class which utilizes a simulation-based application interface to map variables into responses. More...

class	SimulationResponse
	Container class for response functions and their derivatives. SimulationResponse provides the body class. More...

class	SNLLBase
	Base class for OPT++ optimization and least squares methods. More...

class	SNLLLeastSq
	Wrapper class for the OPT++ optimization library. More...

class	SNLLLeastSqTraits
	A version of TraitsBase specialized for SNLLLeastSq. More...

class	SNLLOptimizer
	Wrapper class for the OPT++ optimization library. More...

class	SNLLTraits
	A version of TraitsBase specialized for SNLL optimizers. More...

class	SOLBase
	Base class for Stanford SOL software. More...

class	SpawnApplicInterface
	Derived application interface class which spawns simulation codes using spawnvp. More...

struct	StringScale
	Data structure for storing string-valued dimension scale. More...

class	SubspaceModel
	Subspace model for input (variable space) reduction. More...

class	SurfpackApproximation
	Derived approximation class for Surfpack approximation classes. Interface between Surfpack and Dakota. More...

class	SurrBasedGlobalMinimizer

class	SurrBasedGlobalTraits
	The global surrogate-based minimizer which sequentially minimizes and updates a global surrogate model without trust region controls. More...

class	SurrBasedLocalMinimizer
	Class for provably-convergent local surrogate-based optimization and nonlinear least squares. More...

class	SurrBasedMinimizer
	Base class for local/global surrogate-based optimization/least squares. More...

class	SurrogateModel
	Base class for surrogate models (DataFitSurrModel and HierarchSurrModel). More...

class	SurrogatesBaseApprox
	Derived Approximation class for new Surrogates modules. More...

class	SurrogatesGPApprox
	Derived approximation class for Surrogates approximation classes. More...

class	SurrogatesPolyApprox
	Derived approximation class for Surrogates Polynomial approximation classes. More...

class	SysCallApplicInterface
	Derived application interface class which spawns simulation codes using system calls. More...

class	TabularDataTruncated
	exception thrown when data read truncated More...

class	TabularReader
	Utility used in derived read_core to read values in tabular format. More...

class	TabularWriter
	Utility used in derived write_core to write values in tabular format. More...

class	TANA3Approximation
	Derived approximation class for TANA-3 two-point exponential approximation (a multipoint approximation). More...

class	TaylorApproximation
	Derived approximation class for first- or second-order Taylor series (a local approximation). More...

class	TestDriverInterface

class	TKFactoryDIPC
	Custom RW TKfactory: passes Dakota QUESO instance pointer to the TK at build. More...

class	TKFactoryDIPCLogit
	Custom Logit RW TKfactory: passed Dakota QUESO instance pointer to the TK at build. More...

class	TPLDataTransfer

class	TrackerHTTP
	TrackerHTTP: a usage tracking module that uses HTTP/HTTPS via the curl library. More...

class	TraitsBase
	Base class for traits. More...

class	UsageTracker
	Lightweight class to manage conditionally active Curl-based HTTP tracker via PIMPL. More...

struct	Var_icheck
	structure for verifying bounds and initial point for string-valued vars More...

struct	Var_rcheck
	structure for verifying bounds and initial point for real-valued vars More...

class	Variables
	Base class for the variables class hierarchy. More...

class	Verification
	Base class for managing common aspects of verification studies. More...

struct	VLint
	structure for validating integer uncertain variable labels, bounds, values More...

struct	VLreal
	structure for validating real uncertain variable labels, bounds, values More...

struct	VLstr
	structure for validating string uncertain variable labels, bounds, values More...

class	VPSApproximation
	Derived approximation class for VPS implementation. More...

class	WeightingModel
	Weighting specialization of RecastModel. More...

class	WorkdirHelper

Typedefs
typedef boost::tuple< std::string, std::string, size_t, std::string >	ResultsKeyType
	Data type for results key (instance name / id, unique run, label), where data_key is a valid colon-delimited string from ResultsNames tuple<method_name, method_id, execution_number, data_key>

typedef std::string	MetaDataKeyType
	Data type for metadata key.

typedef std::vector< std::string >	MetaDataValueType
	Data type for metadata value.

typedef std::map< MetaDataKeyType, MetaDataValueType >	MetaDataType
	A single MetaData entry is map<string, vector<string> > Example: pair( "Column labels", ["Mean", "Std Dev", "Skewness", "Kurtosis"] )

typedef boost::tuple< std::string, std::string, size_t >	StrStrSizet
	Iterator unique ID: <method_name, method_id, exec_num>

typedef std::multimap< int, boost::variant< StringScale, RealScale, IntegerScale > >	DimScaleMap
	Datatype to communicate scales (stored in boost::variant) and their associated dimension (the int) to the ResultsManager instance.

typedef std::vector< boost::variant< ResultAttribute< int >, ResultAttribute< String >, ResultAttribute< Real > > >	AttributeArray
	Datatype to communcate metadata (attributes) to the ResultsManager instance.

typedef boost::bimap< unsigned short, std::string >	UShortStrBimap
	bimaps to convert from enums <--> strings

using	RespMetadataT = double

typedef void(*	dl_core_run_t) (void , Optimizer1 , char *)

typedef void(*	dl_destructor_t) (void **)

typedef Teuchos::SerialDenseSolver< int, Real >	RealSolver

typedef Teuchos::SerialSpdDenseSolver< int, Real >	RealSpdSolver

typedef int(*	start_grid_computing_t) (char analysis_driver_script, char params_file, char *results_file)
	definition of start grid computing type (function pointer)

typedef int(*	perform_analysis_t) (char *iteration_num)
	definition of perform analysis type (function pointer)

typedef int (	get_jobs_completed_t) ()
	definition of get completed jobs type (function pointer)

typedef int(*	stop_grid_computing_t) ()
	definition of stop grid computing type (function pointer)

typedef int	MPI_Comm

typedef void *	MPI_Request

typedef unsigned char	u_char

typedef unsigned short	u_short

typedef unsigned int	u_int

typedef unsigned long	u_long

typedef long long	long_long

typedef unsigned long	UL

typedef void(*	Calcrj) (int n, int p, Real x, int nf, Real r, int ui, void *ur, Vf vf)

typedef void(*	Vf) ()

typedef void(*	DbCallbackFunctionPtr) (Dakota::ProblemDescDB db, void data_ptr)

typedef boost::tuple< bfs::path, bfs::path, bfs::path >	PathTriple
	Triplet of filesystem paths: e.g., params, results, workdir.

typedef bmi::multi_index_container< Dakota::ParamResponsePair, bmi::indexed_by< bmi::ordered_non_unique< bmi::tag< ordered >, bmi::const_mem_fun< Dakota::ParamResponsePair, const IntStringPair &, &Dakota::ParamResponsePair::eval_interface_ids > >, bmi::hashed_non_unique< bmi::tag< hashed >, bmi::identity< Dakota::ParamResponsePair >, partial_prp_hash, partial_prp_equality > > >	PRPMultiIndexCache
	Boost Multi-Index Container for globally caching ParamResponsePairs. More...

typedef PRPMultiIndexCache	PRPCache

typedef PRPCache::index_iterator< ordered >::type	PRPCacheOIter

typedef PRPCache::index_const_iterator< ordered >::type	PRPCacheOCIter

typedef PRPCache::index_iterator< hashed >::type	PRPCacheHIter

typedef PRPCache::index_const_iterator< hashed >::type	PRPCacheHCIter

typedef PRPCacheOIter	PRPCacheIter
	default cache iterator <0>

typedef PRPCacheOCIter	PRPCacheCIter
	default cache const iterator <0> default cache const reverse iterator <0>

typedef boost::reverse_iterator< PRPCacheCIter >	PRPCacheCRevIter

typedef bmi::multi_index_container< Dakota::ParamResponsePair, bmi::indexed_by< bmi::ordered_unique< bmi::tag< ordered >, bmi::const_mem_fun< Dakota::ParamResponsePair, int, &Dakota::ParamResponsePair::eval_id > >, bmi::hashed_non_unique< bmi::tag< hashed >, bmi::identity< Dakota::ParamResponsePair >, partial_prp_hash, partial_prp_equality > > >	PRPMultiIndexQueue
	Boost Multi-Index Container for locally queueing ParamResponsePairs. More...

typedef PRPMultiIndexQueue	PRPQueue

typedef PRPQueue::index_iterator< ordered >::type	PRPQueueOIter

typedef PRPQueue::index_const_iterator< ordered >::type	PRPQueueOCIter

typedef PRPQueue::index_iterator< hashed >::type	PRPQueueHIter

typedef PRPQueue::index_const_iterator< hashed >::type	PRPQueueHCIter

typedef PRPQueueOIter	PRPQueueIter

typedef PRPQueueOCIter	PRPQueueCIter

typedef std::pair< boost::any, MetaDataType >	ResultsValueType
	Core data storage type: boost::any, with optional metadata (see other types in results_types.hpp)

typedef boost::function< bool(const bfs::path &src_path, const bfs::path &dest_path, bool overwrite)>	file_op_function
	define a function type that operates from src to dest, with option to overwrite

typedef boost::filter_iterator< MatchesWC, bfs::directory_iterator >	glob_iterator
	a glob_iterator filters a directory_iterator based on a wildcard predicate

Enumerations
enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }
	enum for Dakota abort reasons; using negative numbers to distinguish Dakota exit states from signals / uncaught signals. These need to be in range [-63, -1], so exit code (256+enum) is in [193, 255]. See RATIONALE in dakota_global_defs.cpp.

enum	{ MODEL_EVAL_STORE_TOP_METHOD = 0, MODEL_EVAL_STORE_NONE, MODEL_EVAL_STORE_ALL, MODEL_EVAL_STORE_ALL_METHODS, SYNCHRONOUS_INTERFACE, ASYNCHRONOUS_INTERFACE, SUBMETHOD_DEFAULT =0, SUBMETHOD_NONE, SUBMETHOD_COLLABORATIVE, SUBMETHOD_EMBEDDED, SUBMETHOD_SEQUENTIAL, SUBMETHOD_LHS, SUBMETHOD_RANDOM, SUBMETHOD_BOX_BEHNKEN, SUBMETHOD_CENTRAL_COMPOSITE, SUBMETHOD_GRID, SUBMETHOD_OA_LHS, SUBMETHOD_OAS, SUBMETHOD_MFMC, SUBMETHOD_ACV_IS, SUBMETHOD_ACV_MF, SUBMETHOD_ACV_RD, SUBMETHOD_DREAM, SUBMETHOD_GPMSA, SUBMETHOD_MUQ, SUBMETHOD_QUESO, SUBMETHOD_WASABI, SUBMETHOD_CONMIN, SUBMETHOD_DOT, SUBMETHOD_NLPQL, SUBMETHOD_NPSOL, SUBMETHOD_OPTPP, SUBMETHOD_NPSOL_OPTPP, SUBMETHOD_EA, SUBMETHOD_DIRECT, SUBMETHOD_EGO, SUBMETHOD_SBLO, SUBMETHOD_SBGO, SUBMETHOD_AMV_X, SUBMETHOD_AMV_U, SUBMETHOD_AMV_PLUS_X, SUBMETHOD_AMV_PLUS_U, SUBMETHOD_TANA_X, SUBMETHOD_TANA_U, SUBMETHOD_QMEA_X, SUBMETHOD_QMEA_U, SUBMETHOD_NO_APPROX, SUBMETHOD_EGRA_X, SUBMETHOD_EGRA_U, SUBMETHOD_CONVERGE_ORDER, SUBMETHOD_CONVERGE_QOI, SUBMETHOD_ESTIMATE_ORDER, NO_SURROGATE =0, UNCORRECTED_SURROGATE, AUTO_CORRECTED_SURROGATE, BYPASS_SURROGATE, MODEL_DISCREPANCY, AGGREGATED_MODEL_PAIR, AGGREGATED_MODELS, GENERIC_FNS = 0, OBJECTIVE_FNS, CALIB_TERMS, DEFAULT_VIEW =0, ALL_VIEW, DESIGN_VIEW, UNCERTAIN_VIEW, ALEATORY_UNCERTAIN_VIEW, EPISTEMIC_UNCERTAIN_VIEW, STATE_VIEW, DAUIVar_poisson = 0, DAUIVar_binomial = 1, DAUIVar_negative_binomial = 2, DAUIVar_geometric = 3, DAUIVar_hypergeometric = 4, DAUIVar_histogram_point_int = 5, DAUIVar_Nkinds = 6, TYPE_U =1, TYPE_B =2, TYPE_E =3, TYPE_EB =4, CORR_APPROX_RESPONSE =1, UNCORR_APPROX_RESPONSE, CORR_TRUTH_RESPONSE, UNCORR_TRUTH_RESPONSE, FILEOP_SILENT, FILEOP_WARN, FILEOP_ERROR }
	enum for selecting the models that store evaluations More...

enum	{ INTERF_EVAL_STORE_SIMULATION = 0, INTERF_EVAL_STORE_NONE, INTERF_EVAL_STORE_ALL, OBJECTIVE, INEQUALITY_CONSTRAINT, EQUALITY_CONSTRAINT, NO_GRAPH_RECURSION =0, KL_GRAPH_RECURSION, PARTIAL_GRAPH_RECURSION, FULL_GRAPH_RECURSION, NO_CORRECTION =0, ADDITIVE_CORRECTION, MULTIPLICATIVE_CORRECTION, COMBINED_CORRECTION, EMPTY_VIEW =0, RELAXED_ALL, MIXED_ALL, RELAXED_DESIGN, RELAXED_UNCERTAIN, RELAXED_ALEATORY_UNCERTAIN, RELAXED_EPISTEMIC_UNCERTAIN, RELAXED_STATE, MIXED_DESIGN, MIXED_UNCERTAIN, MIXED_ALEATORY_UNCERTAIN, MIXED_EPISTEMIC_UNCERTAIN, MIXED_STATE, DAUSVar_histogram_point_str = 0, DAUSVar_Nkinds = 1, NEW_CANDIDATE = 1, CANDIDATE_ACCEPTED = 2, CANDIDATE_STATE = (NEW_CANDIDATE \| CANDIDATE_ACCEPTED), NEW_CENTER = 8, CENTER_BUILT = 16, CENTER_STATE = (NEW_CENTER \| CENTER_BUILT), NEW_TR_FACTOR = 64, NEW_TRUST_REGION = (NEW_CENTER \| NEW_TR_FACTOR), HARD_CONVERGED = 128, SOFT_CONVERGED = 256, MIN_TR_CONVERGED = 512, MAX_ITER_CONVERGED = 1024, CONVERGED }
	enum for selecting the interfaces that store evaluations

enum	{ ABORT_EXITS, ABORT_THROWS, NO_MODEL_SELECTION =0, ALL_MODEL_COMBINATIONS, RF_KARHUNEN_LOEVE =0, RF_PCA_GP, RF_ICA, ALL_VARS =0, ACTIVE_VARS, INACTIVE_VARS, DAURVar_histogram_point_real = 0, DAURVar_Nkinds = 1 }
	enum for dakota abort behaviors

enum	{ CV_ID_DEFAULT = 0, MINIMUM_METRIC, RELATIVE_TOLERANCE, DECREASE_TOLERANCE, SILENT_OUTPUT, QUIET_OUTPUT, NORMAL_OUTPUT, VERBOSE_OUTPUT, DEBUG_OUTPUT, NOCOVAR =0, EXP_L2, EXP_L1, EMPTY_TYPE =0, CONTINUOUS_DESIGN, DISCRETE_DESIGN_RANGE, DISCRETE_DESIGN_SET_INT, DISCRETE_DESIGN_SET_STRING, DISCRETE_DESIGN_SET_REAL, NORMAL_UNCERTAIN, LOGNORMAL_UNCERTAIN, UNIFORM_UNCERTAIN, LOGUNIFORM_UNCERTAIN, TRIANGULAR_UNCERTAIN, EXPONENTIAL_UNCERTAIN, BETA_UNCERTAIN, GAMMA_UNCERTAIN, GUMBEL_UNCERTAIN, FRECHET_UNCERTAIN, WEIBULL_UNCERTAIN, HISTOGRAM_BIN_UNCERTAIN, POISSON_UNCERTAIN, BINOMIAL_UNCERTAIN, NEGATIVE_BINOMIAL_UNCERTAIN, GEOMETRIC_UNCERTAIN, HYPERGEOMETRIC_UNCERTAIN, HISTOGRAM_POINT_UNCERTAIN_INT, HISTOGRAM_POINT_UNCERTAIN_STRING, HISTOGRAM_POINT_UNCERTAIN_REAL, CONTINUOUS_INTERVAL_UNCERTAIN, DISCRETE_INTERVAL_UNCERTAIN, DISCRETE_UNCERTAIN_SET_INT, DISCRETE_UNCERTAIN_SET_STRING, DISCRETE_UNCERTAIN_SET_REAL, CONTINUOUS_STATE, DISCRETE_STATE_RANGE, DISCRETE_STATE_SET_INT, DISCRETE_STATE_SET_STRING, DISCRETE_STATE_SET_REAL, CEUVar_interval = 0, CEUVar_Nkinds = 1 }
	enum for active subspace cross validation identification

enum	{ TABULAR_NONE = 0, TABULAR_HEADER = 1, TABULAR_EVAL_ID = 2, TABULAR_IFACE_ID = 4, TABULAR_EXPER_ANNOT = TABULAR_HEADER \| TABULAR_EVAL_ID, TABULAR_ANNOTATED = TABULAR_HEADER \| TABULAR_EVAL_ID \| TABULAR_IFACE_ID, NO_RESULTS =0, REFINEMENT_RESULTS, INTERMEDIATE_RESULTS, FINAL_RESULTS, SUBSPACE_NORM_DEFAULT =0, SUBSPACE_NORM_MEAN_VALUE, SUBSPACE_NORM_MEAN_GRAD, SUBSPACE_NORM_LOCAL_GRAD, TOTAL_CDV =0, TOTAL_DDIV, TOTAL_DDSV, TOTAL_DDRV, TOTAL_CAUV, TOTAL_DAUIV, TOTAL_DAUSV, TOTAL_DAURV, TOTAL_CEUV, TOTAL_DEUIV, TOTAL_DEUSV, TOTAL_DEURV, TOTAL_CSV, TOTAL_DSIV, TOTAL_DSSV, TOTAL_DSRV, NUM_VC_TOTALS, DEUIVar_interval = 0, DEUIVar_set_int = 1, DEUIVar_Nkinds = 2 }
	options for tabular columns

enum	{ RESULTS_OUTPUT_TEXT = 1, RESULTS_OUTPUT_HDF5 = 2, DEFAULT_SYNCHRONIZATION =0, BLOCKING_SYNCHRONIZATION, NONBLOCKING_SYNCHRONIZATION, ROTATION_METHOD_UNRANKED, ROTATION_METHOD_RANKED, DEUSVar_set_str = 0, DEUSVar_Nkinds = 1 }
	Results output format.

enum	{ FLEXIBLE_RESULTS, LABELED_RESULTS, DEFAULT_SCHEDULING, MASTER_SCHEDULING, PEER_SCHEDULING, PEER_DYNAMIC_SCHEDULING, PEER_STATIC_SCHEDULING, DYNAMIC_SCHEDULING, STATIC_SCHEDULING, NO_PARALLEL_MODE =0, SURROGATE_MODEL_MODE, TRUTH_MODEL_MODE, SUB_MODEL_MODE, INTERFACE_MODE, DEURVar_set_real = 0, DEURVar_Nkinds = 1 }
	options for results file format

enum	{ NO_MODEL_FORMAT =0, TEXT_ARCHIVE =1, BINARY_ARCHIVE =2, ALGEBRAIC_FILE =4, ALGEBRAIC_CONSOLE =8, DEFAULT_CONFIG, PUSH_DOWN, PUSH_UP, NO_DERIVS =0, ALL_DERIVS, MIXED_DERIVS, DiscSetVar_design_set_int = 0, DiscSetVar_design_set_str = 1, DiscSetVar_design_set_real = 2, DiscSetVar_state_set_int = 3, DiscSetVar_state_set_str = 4, DiscSetVar_state_set_real = 5, DiscSetVar_Nkinds = 6 }
	define special values for surrogateExportFormats

enum	ScaleScope { SHARED, UNSHARED }
	Enum to specify whether a scale shared among responses.

enum	ResultsOutputType { REAL, INTEGER, UINTEGER, STRING }
	enum for setting type on allocted matrix for Results Output

enum	CONSTRAINT_TYPE { LINEAR, NONLINEAR }

enum	CONSTRAINT_EQUALITY_TYPE { EQUALITY, INEQUALITY }

enum	LINEAR_INEQUALITY_FORMAT { NONE, TWO_SIDED, ONE_SIDED_LOWER, ONE_SIDED_UPPER }

enum	NONLINEAR_EQUALITY_FORMAT { NONE, TRUE_EQUALITY, TWO_INEQUALITY }

enum	NONLINEAR_INEQUALITY_FORMAT { NONE, ONE_SIDED_UPPER, ONE_SIDED_LOWER, TWO_SIDED }

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }
	special values for interface type

enum	{ MODEL_EVAL_STORE_TOP_METHOD = 0, MODEL_EVAL_STORE_NONE, MODEL_EVAL_STORE_ALL, MODEL_EVAL_STORE_ALL_METHODS, SYNCHRONOUS_INTERFACE, ASYNCHRONOUS_INTERFACE, SUBMETHOD_DEFAULT =0, SUBMETHOD_NONE, SUBMETHOD_COLLABORATIVE, SUBMETHOD_EMBEDDED, SUBMETHOD_SEQUENTIAL, SUBMETHOD_LHS, SUBMETHOD_RANDOM, SUBMETHOD_BOX_BEHNKEN, SUBMETHOD_CENTRAL_COMPOSITE, SUBMETHOD_GRID, SUBMETHOD_OA_LHS, SUBMETHOD_OAS, SUBMETHOD_MFMC, SUBMETHOD_ACV_IS, SUBMETHOD_ACV_MF, SUBMETHOD_ACV_RD, SUBMETHOD_DREAM, SUBMETHOD_GPMSA, SUBMETHOD_MUQ, SUBMETHOD_QUESO, SUBMETHOD_WASABI, SUBMETHOD_CONMIN, SUBMETHOD_DOT, SUBMETHOD_NLPQL, SUBMETHOD_NPSOL, SUBMETHOD_OPTPP, SUBMETHOD_NPSOL_OPTPP, SUBMETHOD_EA, SUBMETHOD_DIRECT, SUBMETHOD_EGO, SUBMETHOD_SBLO, SUBMETHOD_SBGO, SUBMETHOD_AMV_X, SUBMETHOD_AMV_U, SUBMETHOD_AMV_PLUS_X, SUBMETHOD_AMV_PLUS_U, SUBMETHOD_TANA_X, SUBMETHOD_TANA_U, SUBMETHOD_QMEA_X, SUBMETHOD_QMEA_U, SUBMETHOD_NO_APPROX, SUBMETHOD_EGRA_X, SUBMETHOD_EGRA_U, SUBMETHOD_CONVERGE_ORDER, SUBMETHOD_CONVERGE_QOI, SUBMETHOD_ESTIMATE_ORDER, NO_SURROGATE =0, UNCORRECTED_SURROGATE, AUTO_CORRECTED_SURROGATE, BYPASS_SURROGATE, MODEL_DISCREPANCY, AGGREGATED_MODEL_PAIR, AGGREGATED_MODELS, GENERIC_FNS = 0, OBJECTIVE_FNS, CALIB_TERMS, DEFAULT_VIEW =0, ALL_VIEW, DESIGN_VIEW, UNCERTAIN_VIEW, ALEATORY_UNCERTAIN_VIEW, EPISTEMIC_UNCERTAIN_VIEW, STATE_VIEW, DAUIVar_poisson = 0, DAUIVar_binomial = 1, DAUIVar_negative_binomial = 2, DAUIVar_geometric = 3, DAUIVar_hypergeometric = 4, DAUIVar_histogram_point_int = 5, DAUIVar_Nkinds = 6, TYPE_U =1, TYPE_B =2, TYPE_E =3, TYPE_EB =4, CORR_APPROX_RESPONSE =1, UNCORR_APPROX_RESPONSE, CORR_TRUTH_RESPONSE, UNCORR_TRUTH_RESPONSE, FILEOP_SILENT, FILEOP_WARN, FILEOP_ERROR }
	interface synchronization types More...

enum	{ INTERF_EVAL_STORE_SIMULATION = 0, INTERF_EVAL_STORE_NONE, INTERF_EVAL_STORE_ALL, OBJECTIVE, INEQUALITY_CONSTRAINT, EQUALITY_CONSTRAINT, NO_GRAPH_RECURSION =0, KL_GRAPH_RECURSION, PARTIAL_GRAPH_RECURSION, FULL_GRAPH_RECURSION, NO_CORRECTION =0, ADDITIVE_CORRECTION, MULTIPLICATIVE_CORRECTION, COMBINED_CORRECTION, EMPTY_VIEW =0, RELAXED_ALL, MIXED_ALL, RELAXED_DESIGN, RELAXED_UNCERTAIN, RELAXED_ALEATORY_UNCERTAIN, RELAXED_EPISTEMIC_UNCERTAIN, RELAXED_STATE, MIXED_DESIGN, MIXED_UNCERTAIN, MIXED_ALEATORY_UNCERTAIN, MIXED_EPISTEMIC_UNCERTAIN, MIXED_STATE, DAUSVar_histogram_point_str = 0, DAUSVar_Nkinds = 1, NEW_CANDIDATE = 1, CANDIDATE_ACCEPTED = 2, CANDIDATE_STATE = (NEW_CANDIDATE \| CANDIDATE_ACCEPTED), NEW_CENTER = 8, CENTER_BUILT = 16, CENTER_STATE = (NEW_CENTER \| CENTER_BUILT), NEW_TR_FACTOR = 64, NEW_TRUST_REGION = (NEW_CENTER \| NEW_TR_FACTOR), HARD_CONVERGED = 128, SOFT_CONVERGED = 256, MIN_TR_CONVERGED = 512, MAX_ITER_CONVERGED = 1024, CONVERGED }
	define algebraic function types

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	{ MODEL_EVAL_STORE_TOP_METHOD = 0, MODEL_EVAL_STORE_NONE, MODEL_EVAL_STORE_ALL, MODEL_EVAL_STORE_ALL_METHODS, SYNCHRONOUS_INTERFACE, ASYNCHRONOUS_INTERFACE, SUBMETHOD_DEFAULT =0, SUBMETHOD_NONE, SUBMETHOD_COLLABORATIVE, SUBMETHOD_EMBEDDED, SUBMETHOD_SEQUENTIAL, SUBMETHOD_LHS, SUBMETHOD_RANDOM, SUBMETHOD_BOX_BEHNKEN, SUBMETHOD_CENTRAL_COMPOSITE, SUBMETHOD_GRID, SUBMETHOD_OA_LHS, SUBMETHOD_OAS, SUBMETHOD_MFMC, SUBMETHOD_ACV_IS, SUBMETHOD_ACV_MF, SUBMETHOD_ACV_RD, SUBMETHOD_DREAM, SUBMETHOD_GPMSA, SUBMETHOD_MUQ, SUBMETHOD_QUESO, SUBMETHOD_WASABI, SUBMETHOD_CONMIN, SUBMETHOD_DOT, SUBMETHOD_NLPQL, SUBMETHOD_NPSOL, SUBMETHOD_OPTPP, SUBMETHOD_NPSOL_OPTPP, SUBMETHOD_EA, SUBMETHOD_DIRECT, SUBMETHOD_EGO, SUBMETHOD_SBLO, SUBMETHOD_SBGO, SUBMETHOD_AMV_X, SUBMETHOD_AMV_U, SUBMETHOD_AMV_PLUS_X, SUBMETHOD_AMV_PLUS_U, SUBMETHOD_TANA_X, SUBMETHOD_TANA_U, SUBMETHOD_QMEA_X, SUBMETHOD_QMEA_U, SUBMETHOD_NO_APPROX, SUBMETHOD_EGRA_X, SUBMETHOD_EGRA_U, SUBMETHOD_CONVERGE_ORDER, SUBMETHOD_CONVERGE_QOI, SUBMETHOD_ESTIMATE_ORDER, NO_SURROGATE =0, UNCORRECTED_SURROGATE, AUTO_CORRECTED_SURROGATE, BYPASS_SURROGATE, MODEL_DISCREPANCY, AGGREGATED_MODEL_PAIR, AGGREGATED_MODELS, GENERIC_FNS = 0, OBJECTIVE_FNS, CALIB_TERMS, DEFAULT_VIEW =0, ALL_VIEW, DESIGN_VIEW, UNCERTAIN_VIEW, ALEATORY_UNCERTAIN_VIEW, EPISTEMIC_UNCERTAIN_VIEW, STATE_VIEW, DAUIVar_poisson = 0, DAUIVar_binomial = 1, DAUIVar_negative_binomial = 2, DAUIVar_geometric = 3, DAUIVar_hypergeometric = 4, DAUIVar_histogram_point_int = 5, DAUIVar_Nkinds = 6, TYPE_U =1, TYPE_B =2, TYPE_E =3, TYPE_EB =4, CORR_APPROX_RESPONSE =1, UNCORR_APPROX_RESPONSE, CORR_TRUTH_RESPONSE, UNCORR_TRUTH_RESPONSE, FILEOP_SILENT, FILEOP_WARN, FILEOP_ERROR }
	Sub-methods, including sampling, inference algorithm, opt algorithm types. More...

enum	{ INTERF_EVAL_STORE_SIMULATION = 0, INTERF_EVAL_STORE_NONE, INTERF_EVAL_STORE_ALL, OBJECTIVE, INEQUALITY_CONSTRAINT, EQUALITY_CONSTRAINT, NO_GRAPH_RECURSION =0, KL_GRAPH_RECURSION, PARTIAL_GRAPH_RECURSION, FULL_GRAPH_RECURSION, NO_CORRECTION =0, ADDITIVE_CORRECTION, MULTIPLICATIVE_CORRECTION, COMBINED_CORRECTION, EMPTY_VIEW =0, RELAXED_ALL, MIXED_ALL, RELAXED_DESIGN, RELAXED_UNCERTAIN, RELAXED_ALEATORY_UNCERTAIN, RELAXED_EPISTEMIC_UNCERTAIN, RELAXED_STATE, MIXED_DESIGN, MIXED_UNCERTAIN, MIXED_ALEATORY_UNCERTAIN, MIXED_EPISTEMIC_UNCERTAIN, MIXED_STATE, DAUSVar_histogram_point_str = 0, DAUSVar_Nkinds = 1, NEW_CANDIDATE = 1, CANDIDATE_ACCEPTED = 2, CANDIDATE_STATE = (NEW_CANDIDATE \| CANDIDATE_ACCEPTED), NEW_CENTER = 8, CENTER_BUILT = 16, CENTER_STATE = (NEW_CENTER \| CENTER_BUILT), NEW_TR_FACTOR = 64, NEW_TRUST_REGION = (NEW_CENTER \| NEW_TR_FACTOR), HARD_CONVERGED = 128, SOFT_CONVERGED = 256, MIN_TR_CONVERGED = 512, MAX_ITER_CONVERGED = 1024, CONVERGED }
	Graph recursion options for generalized ACV.

enum	{ ABORT_EXITS, ABORT_THROWS, NO_MODEL_SELECTION =0, ALL_MODEL_COMBINATIONS, RF_KARHUNEN_LOEVE =0, RF_PCA_GP, RF_ICA, ALL_VARS =0, ACTIVE_VARS, INACTIVE_VARS, DAURVar_histogram_point_real = 0, DAURVar_Nkinds = 1 }
	Model selection options for generalized ACV.

enum	{ CV_ID_DEFAULT = 0, MINIMUM_METRIC, RELATIVE_TOLERANCE, DECREASE_TOLERANCE, SILENT_OUTPUT, QUIET_OUTPUT, NORMAL_OUTPUT, VERBOSE_OUTPUT, DEBUG_OUTPUT, NOCOVAR =0, EXP_L2, EXP_L1, EMPTY_TYPE =0, CONTINUOUS_DESIGN, DISCRETE_DESIGN_RANGE, DISCRETE_DESIGN_SET_INT, DISCRETE_DESIGN_SET_STRING, DISCRETE_DESIGN_SET_REAL, NORMAL_UNCERTAIN, LOGNORMAL_UNCERTAIN, UNIFORM_UNCERTAIN, LOGUNIFORM_UNCERTAIN, TRIANGULAR_UNCERTAIN, EXPONENTIAL_UNCERTAIN, BETA_UNCERTAIN, GAMMA_UNCERTAIN, GUMBEL_UNCERTAIN, FRECHET_UNCERTAIN, WEIBULL_UNCERTAIN, HISTOGRAM_BIN_UNCERTAIN, POISSON_UNCERTAIN, BINOMIAL_UNCERTAIN, NEGATIVE_BINOMIAL_UNCERTAIN, GEOMETRIC_UNCERTAIN, HYPERGEOMETRIC_UNCERTAIN, HISTOGRAM_POINT_UNCERTAIN_INT, HISTOGRAM_POINT_UNCERTAIN_STRING, HISTOGRAM_POINT_UNCERTAIN_REAL, CONTINUOUS_INTERVAL_UNCERTAIN, DISCRETE_INTERVAL_UNCERTAIN, DISCRETE_UNCERTAIN_SET_INT, DISCRETE_UNCERTAIN_SET_STRING, DISCRETE_UNCERTAIN_SET_REAL, CONTINUOUS_STATE, DISCRETE_STATE_RANGE, DISCRETE_STATE_SET_INT, DISCRETE_STATE_SET_STRING, DISCRETE_STATE_SET_REAL, CEUVar_interval = 0, CEUVar_Nkinds = 1 }

enum	{ TABULAR_NONE = 0, TABULAR_HEADER = 1, TABULAR_EVAL_ID = 2, TABULAR_IFACE_ID = 4, TABULAR_EXPER_ANNOT = TABULAR_HEADER \| TABULAR_EVAL_ID, TABULAR_ANNOTATED = TABULAR_HEADER \| TABULAR_EVAL_ID \| TABULAR_IFACE_ID, NO_RESULTS =0, REFINEMENT_RESULTS, INTERMEDIATE_RESULTS, FINAL_RESULTS, SUBSPACE_NORM_DEFAULT =0, SUBSPACE_NORM_MEAN_VALUE, SUBSPACE_NORM_MEAN_GRAD, SUBSPACE_NORM_LOCAL_GRAD, TOTAL_CDV =0, TOTAL_DDIV, TOTAL_DDSV, TOTAL_DDRV, TOTAL_CAUV, TOTAL_DAUIV, TOTAL_DAUSV, TOTAL_DAURV, TOTAL_CEUV, TOTAL_DEUIV, TOTAL_DEUSV, TOTAL_DEURV, TOTAL_CSV, TOTAL_DSIV, TOTAL_DSSV, TOTAL_DSRV, NUM_VC_TOTALS, DEUIVar_interval = 0, DEUIVar_set_int = 1, DEUIVar_Nkinds = 2 }

enum	{ RESULTS_OUTPUT_TEXT = 1, RESULTS_OUTPUT_HDF5 = 2, DEFAULT_SYNCHRONIZATION =0, BLOCKING_SYNCHRONIZATION, NONBLOCKING_SYNCHRONIZATION, ROTATION_METHOD_UNRANKED, ROTATION_METHOD_RANKED, DEUSVar_set_str = 0, DEUSVar_Nkinds = 1 }

enum	{ FLEXIBLE_RESULTS, LABELED_RESULTS, DEFAULT_SCHEDULING, MASTER_SCHEDULING, PEER_SCHEDULING, PEER_DYNAMIC_SCHEDULING, PEER_STATIC_SCHEDULING, DYNAMIC_SCHEDULING, STATIC_SCHEDULING, NO_PARALLEL_MODE =0, SURROGATE_MODEL_MODE, TRUTH_MODEL_MODE, SUB_MODEL_MODE, INTERFACE_MODE, DEURVar_set_real = 0, DEURVar_Nkinds = 1 }

enum	{ NO_MODEL_FORMAT =0, TEXT_ARCHIVE =1, BINARY_ARCHIVE =2, ALGEBRAIC_FILE =4, ALGEBRAIC_CONSOLE =8, DEFAULT_CONFIG, PUSH_DOWN, PUSH_UP, NO_DERIVS =0, ALL_DERIVS, MIXED_DERIVS, DiscSetVar_design_set_int = 0, DiscSetVar_design_set_str = 1, DiscSetVar_design_set_real = 2, DiscSetVar_state_set_int = 3, DiscSetVar_state_set_str = 4, DiscSetVar_state_set_real = 5, DiscSetVar_Nkinds = 6 }

enum	{ STD_NORMAL_U, STD_UNIFORM_U, PARTIAL_ASKEY_U, ASKEY_U, EXTENDED_U, FT_LS, FT_RLS2, NUM_UNC_REAL_CONT = 4 }

enum	{ DEFAULT_COVARIANCE, NO_COVARIANCE, DIAGONAL_COVARIANCE, FULL_COVARIANCE, NO_C3_ADVANCEMENT =0, START_RANK_ADVANCEMENT, START_ORDER_ADVANCEMENT, MAX_RANK_ADVANCEMENT, MAX_ORDER_ADVANCEMENT, MAX_RANK_ORDER_ADVANCEMENT, NUM_UNC_INT_CONT = 2 }

enum	{ NO_INT_REFINE =0, IS, AIS, MMAIS, NUM_UNC_STR_CONT = 2 }

enum	{ PROBABILITIES, RELIABILITIES, GEN_RELIABILITIES }

enum	{ COMPONENT =0, SYSTEM_SERIES, SYSTEM_PARALLEL }

enum	{ CUMULATIVE, COMPLEMENTARY }

enum	{ DEFAULT_LS =0, SVD_LS, EQ_CON_LS }

enum	{ DEFAULT_MLMF_CONTROL =0, ESTIMATOR_VARIANCE, RIP_SAMPLING, RANK_SAMPLING, GREEDY_REFINEMENT }

enum	{ DEFAULT_EMULATION, DISTINCT_EMULATION, RECURSIVE_EMULATION }

enum	{ NO_EMULATOR, PCE_EMULATOR, ML_PCE_EMULATOR, MF_PCE_EMULATOR, SC_EMULATOR, MF_SC_EMULATOR, GP_EMULATOR, KRIGING_EMULATOR, EXPGP_EMULATOR, VPS_EMULATOR }

enum	{ CALIBRATE_NONE = 0, CALIBRATE_ONE, CALIBRATE_PER_EXPER, CALIBRATE_PER_RESP, CALIBRATE_BOTH }

enum	{ IGNORE_RANKS, SET_RANKS, GET_RANKS, SET_GET_RANKS }

enum	{ DESIGN, UNCERTAIN, UNCERTAIN_UNIFORM, ALEATORY_UNCERTAIN, ALEATORY_UNCERTAIN_UNIFORM, EPISTEMIC_UNCERTAIN, EPISTEMIC_UNCERTAIN_UNIFORM, STATE, ACTIVE, ACTIVE_UNIFORM, ALL, ALL_UNIFORM }

enum	{ ONE_SIDED_LOWER, ONE_SIDED_UPPER, NONE, TWO_SIDED, ONE_SIDED_LOWER, ONE_SIDED_UPPER, TWO_SIDED, NONE, ONE_SIDED_UPPER, ONE_SIDED_LOWER, TWO_SIDED }

enum	{ NO_FINAL_STATS =0, QOI_STATISTICS, ESTIMATOR_PERFORMANCE }

enum	{ QOI_AGGREGATION_MAX, QOI_AGGREGATION_SUM }

enum	{ TARGET_MEAN, TARGET_VARIANCE, TARGET_SIGMA, TARGET_SCALARIZATION }

enum	{ CONVERGENCE_TOLERANCE_TYPE_RELATIVE, CONVERGENCE_TOLERANCE_TYPE_ABSOLUTE }

enum	{ CONVERGENCE_TOLERANCE_TARGET_VARIANCE_CONSTRAINT, CONVERGENCE_TOLERANCE_TARGET_COST_CONSTRAINT }

enum	{ ONLINE_PILOT, OFFLINE_PILOT, PILOT_PROJECTION }

enum	{ REORDERED_FALLBACK, NUMERICAL_FALLBACK, NUMERICAL_OVERRIDE }

enum	{ BREITUNG, HOHENRACK, HONG }

enum	{ ORIGINAL_PRIMARY, SINGLE_OBJECTIVE, LAGRANGIAN_OBJECTIVE, AUGMENTED_LAGRANGIAN_OBJECTIVE }

enum	{ NO_CONSTRAINTS, LINEARIZED_CONSTRAINTS, ORIGINAL_CONSTRAINTS }

enum	{ NO_RELAX, HOMOTOPY, COMPOSITE_STEP }

enum	{ PENALTY_MERIT, ADAPTIVE_PENALTY_MERIT, LAGRANGIAN_MERIT, AUGMENTED_LAGRANGIAN_MERIT }

enum	{ FILTER, TR_RATIO }

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }
	define special values for pointsManagement

enum	{ MODEL_EVAL_STORE_TOP_METHOD = 0, MODEL_EVAL_STORE_NONE, MODEL_EVAL_STORE_ALL, MODEL_EVAL_STORE_ALL_METHODS, SYNCHRONOUS_INTERFACE, ASYNCHRONOUS_INTERFACE, SUBMETHOD_DEFAULT =0, SUBMETHOD_NONE, SUBMETHOD_COLLABORATIVE, SUBMETHOD_EMBEDDED, SUBMETHOD_SEQUENTIAL, SUBMETHOD_LHS, SUBMETHOD_RANDOM, SUBMETHOD_BOX_BEHNKEN, SUBMETHOD_CENTRAL_COMPOSITE, SUBMETHOD_GRID, SUBMETHOD_OA_LHS, SUBMETHOD_OAS, SUBMETHOD_MFMC, SUBMETHOD_ACV_IS, SUBMETHOD_ACV_MF, SUBMETHOD_ACV_RD, SUBMETHOD_DREAM, SUBMETHOD_GPMSA, SUBMETHOD_MUQ, SUBMETHOD_QUESO, SUBMETHOD_WASABI, SUBMETHOD_CONMIN, SUBMETHOD_DOT, SUBMETHOD_NLPQL, SUBMETHOD_NPSOL, SUBMETHOD_OPTPP, SUBMETHOD_NPSOL_OPTPP, SUBMETHOD_EA, SUBMETHOD_DIRECT, SUBMETHOD_EGO, SUBMETHOD_SBLO, SUBMETHOD_SBGO, SUBMETHOD_AMV_X, SUBMETHOD_AMV_U, SUBMETHOD_AMV_PLUS_X, SUBMETHOD_AMV_PLUS_U, SUBMETHOD_TANA_X, SUBMETHOD_TANA_U, SUBMETHOD_QMEA_X, SUBMETHOD_QMEA_U, SUBMETHOD_NO_APPROX, SUBMETHOD_EGRA_X, SUBMETHOD_EGRA_U, SUBMETHOD_CONVERGE_ORDER, SUBMETHOD_CONVERGE_QOI, SUBMETHOD_ESTIMATE_ORDER, NO_SURROGATE =0, UNCORRECTED_SURROGATE, AUTO_CORRECTED_SURROGATE, BYPASS_SURROGATE, MODEL_DISCREPANCY, AGGREGATED_MODEL_PAIR, AGGREGATED_MODELS, GENERIC_FNS = 0, OBJECTIVE_FNS, CALIB_TERMS, DEFAULT_VIEW =0, ALL_VIEW, DESIGN_VIEW, UNCERTAIN_VIEW, ALEATORY_UNCERTAIN_VIEW, EPISTEMIC_UNCERTAIN_VIEW, STATE_VIEW, DAUIVar_poisson = 0, DAUIVar_binomial = 1, DAUIVar_negative_binomial = 2, DAUIVar_geometric = 3, DAUIVar_hypergeometric = 4, DAUIVar_histogram_point_int = 5, DAUIVar_Nkinds = 6, TYPE_U =1, TYPE_B =2, TYPE_E =3, TYPE_EB =4, CORR_APPROX_RESPONSE =1, UNCORR_APPROX_RESPONSE, CORR_TRUTH_RESPONSE, UNCORR_TRUTH_RESPONSE, FILEOP_SILENT, FILEOP_WARN, FILEOP_ERROR }
	define special values for SurrogateModel::responseMode More...

enum	{ INTERF_EVAL_STORE_SIMULATION = 0, INTERF_EVAL_STORE_NONE, INTERF_EVAL_STORE_ALL, OBJECTIVE, INEQUALITY_CONSTRAINT, EQUALITY_CONSTRAINT, NO_GRAPH_RECURSION =0, KL_GRAPH_RECURSION, PARTIAL_GRAPH_RECURSION, FULL_GRAPH_RECURSION, NO_CORRECTION =0, ADDITIVE_CORRECTION, MULTIPLICATIVE_CORRECTION, COMBINED_CORRECTION, EMPTY_VIEW =0, RELAXED_ALL, MIXED_ALL, RELAXED_DESIGN, RELAXED_UNCERTAIN, RELAXED_ALEATORY_UNCERTAIN, RELAXED_EPISTEMIC_UNCERTAIN, RELAXED_STATE, MIXED_DESIGN, MIXED_UNCERTAIN, MIXED_ALEATORY_UNCERTAIN, MIXED_EPISTEMIC_UNCERTAIN, MIXED_STATE, DAUSVar_histogram_point_str = 0, DAUSVar_Nkinds = 1, NEW_CANDIDATE = 1, CANDIDATE_ACCEPTED = 2, CANDIDATE_STATE = (NEW_CANDIDATE \| CANDIDATE_ACCEPTED), NEW_CENTER = 8, CENTER_BUILT = 16, CENTER_STATE = (NEW_CENTER \| CENTER_BUILT), NEW_TR_FACTOR = 64, NEW_TRUST_REGION = (NEW_CENTER \| NEW_TR_FACTOR), HARD_CONVERGED = 128, SOFT_CONVERGED = 256, MIN_TR_CONVERGED = 512, MAX_ITER_CONVERGED = 1024, CONVERGED }
	define special values for approxCorrectionType

enum	{ ABORT_EXITS, ABORT_THROWS, NO_MODEL_SELECTION =0, ALL_MODEL_COMBINATIONS, RF_KARHUNEN_LOEVE =0, RF_PCA_GP, RF_ICA, ALL_VARS =0, ACTIVE_VARS, INACTIVE_VARS, DAURVar_histogram_point_real = 0, DAURVar_Nkinds = 1 }
	define types of random field approximations

enum	{ CV_ID_DEFAULT = 0, MINIMUM_METRIC, RELATIVE_TOLERANCE, DECREASE_TOLERANCE, SILENT_OUTPUT, QUIET_OUTPUT, NORMAL_OUTPUT, VERBOSE_OUTPUT, DEBUG_OUTPUT, NOCOVAR =0, EXP_L2, EXP_L1, EMPTY_TYPE =0, CONTINUOUS_DESIGN, DISCRETE_DESIGN_RANGE, DISCRETE_DESIGN_SET_INT, DISCRETE_DESIGN_SET_STRING, DISCRETE_DESIGN_SET_REAL, NORMAL_UNCERTAIN, LOGNORMAL_UNCERTAIN, UNIFORM_UNCERTAIN, LOGUNIFORM_UNCERTAIN, TRIANGULAR_UNCERTAIN, EXPONENTIAL_UNCERTAIN, BETA_UNCERTAIN, GAMMA_UNCERTAIN, GUMBEL_UNCERTAIN, FRECHET_UNCERTAIN, WEIBULL_UNCERTAIN, HISTOGRAM_BIN_UNCERTAIN, POISSON_UNCERTAIN, BINOMIAL_UNCERTAIN, NEGATIVE_BINOMIAL_UNCERTAIN, GEOMETRIC_UNCERTAIN, HYPERGEOMETRIC_UNCERTAIN, HISTOGRAM_POINT_UNCERTAIN_INT, HISTOGRAM_POINT_UNCERTAIN_STRING, HISTOGRAM_POINT_UNCERTAIN_REAL, CONTINUOUS_INTERVAL_UNCERTAIN, DISCRETE_INTERVAL_UNCERTAIN, DISCRETE_UNCERTAIN_SET_INT, DISCRETE_UNCERTAIN_SET_STRING, DISCRETE_UNCERTAIN_SET_REAL, CONTINUOUS_STATE, DISCRETE_STATE_RANGE, DISCRETE_STATE_SET_INT, DISCRETE_STATE_SET_STRING, DISCRETE_STATE_SET_REAL, CEUVar_interval = 0, CEUVar_Nkinds = 1 }
	define types of analytic covariance functions

enum	{ TABULAR_NONE = 0, TABULAR_HEADER = 1, TABULAR_EVAL_ID = 2, TABULAR_IFACE_ID = 4, TABULAR_EXPER_ANNOT = TABULAR_HEADER \| TABULAR_EVAL_ID, TABULAR_ANNOTATED = TABULAR_HEADER \| TABULAR_EVAL_ID \| TABULAR_IFACE_ID, NO_RESULTS =0, REFINEMENT_RESULTS, INTERMEDIATE_RESULTS, FINAL_RESULTS, SUBSPACE_NORM_DEFAULT =0, SUBSPACE_NORM_MEAN_VALUE, SUBSPACE_NORM_MEAN_GRAD, SUBSPACE_NORM_LOCAL_GRAD, TOTAL_CDV =0, TOTAL_DDIV, TOTAL_DDSV, TOTAL_DDRV, TOTAL_CAUV, TOTAL_DAUIV, TOTAL_DAUSV, TOTAL_DAURV, TOTAL_CEUV, TOTAL_DEUIV, TOTAL_DEUSV, TOTAL_DEURV, TOTAL_CSV, TOTAL_DSIV, TOTAL_DSSV, TOTAL_DSRV, NUM_VC_TOTALS, DEUIVar_interval = 0, DEUIVar_set_int = 1, DEUIVar_Nkinds = 2 }
	define special values for active subspace normalizations

enum	{ RESULTS_OUTPUT_TEXT = 1, RESULTS_OUTPUT_HDF5 = 2, DEFAULT_SYNCHRONIZATION =0, BLOCKING_SYNCHRONIZATION, NONBLOCKING_SYNCHRONIZATION, ROTATION_METHOD_UNRANKED, ROTATION_METHOD_RANKED, DEUSVar_set_str = 0, DEUSVar_Nkinds = 1 }

enum	{ FLEXIBLE_RESULTS, LABELED_RESULTS, DEFAULT_SCHEDULING, MASTER_SCHEDULING, PEER_SCHEDULING, PEER_DYNAMIC_SCHEDULING, PEER_STATIC_SCHEDULING, DYNAMIC_SCHEDULING, STATIC_SCHEDULING, NO_PARALLEL_MODE =0, SURROGATE_MODEL_MODE, TRUTH_MODEL_MODE, SUB_MODEL_MODE, INTERFACE_MODE, DEURVar_set_real = 0, DEURVar_Nkinds = 1 }
	define special values for componentParallelMode (active model for parallel scheduling)

enum	{ NO_MODEL_FORMAT =0, TEXT_ARCHIVE =1, BINARY_ARCHIVE =2, ALGEBRAIC_FILE =4, ALGEBRAIC_CONSOLE =8, DEFAULT_CONFIG, PUSH_DOWN, PUSH_UP, NO_DERIVS =0, ALL_DERIVS, MIXED_DERIVS, DiscSetVar_design_set_int = 0, DiscSetVar_design_set_str = 1, DiscSetVar_design_set_real = 2, DiscSetVar_state_set_int = 3, DiscSetVar_state_set_str = 4, DiscSetVar_state_set_real = 5, DiscSetVar_Nkinds = 6 }
	define special values for distParamDerivs

enum	{ STD_NORMAL_U, STD_UNIFORM_U, PARTIAL_ASKEY_U, ASKEY_U, EXTENDED_U, FT_LS, FT_RLS2, NUM_UNC_REAL_CONT = 4 }

enum	{ DEFAULT_COVARIANCE, NO_COVARIANCE, DIAGONAL_COVARIANCE, FULL_COVARIANCE, NO_C3_ADVANCEMENT =0, START_RANK_ADVANCEMENT, START_ORDER_ADVANCEMENT, MAX_RANK_ADVANCEMENT, MAX_ORDER_ADVANCEMENT, MAX_RANK_ORDER_ADVANCEMENT, NUM_UNC_INT_CONT = 2 }

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }
	special values for derived Response type

enum	{ MODEL_EVAL_STORE_TOP_METHOD = 0, MODEL_EVAL_STORE_NONE, MODEL_EVAL_STORE_ALL, MODEL_EVAL_STORE_ALL_METHODS, SYNCHRONOUS_INTERFACE, ASYNCHRONOUS_INTERFACE, SUBMETHOD_DEFAULT =0, SUBMETHOD_NONE, SUBMETHOD_COLLABORATIVE, SUBMETHOD_EMBEDDED, SUBMETHOD_SEQUENTIAL, SUBMETHOD_LHS, SUBMETHOD_RANDOM, SUBMETHOD_BOX_BEHNKEN, SUBMETHOD_CENTRAL_COMPOSITE, SUBMETHOD_GRID, SUBMETHOD_OA_LHS, SUBMETHOD_OAS, SUBMETHOD_MFMC, SUBMETHOD_ACV_IS, SUBMETHOD_ACV_MF, SUBMETHOD_ACV_RD, SUBMETHOD_DREAM, SUBMETHOD_GPMSA, SUBMETHOD_MUQ, SUBMETHOD_QUESO, SUBMETHOD_WASABI, SUBMETHOD_CONMIN, SUBMETHOD_DOT, SUBMETHOD_NLPQL, SUBMETHOD_NPSOL, SUBMETHOD_OPTPP, SUBMETHOD_NPSOL_OPTPP, SUBMETHOD_EA, SUBMETHOD_DIRECT, SUBMETHOD_EGO, SUBMETHOD_SBLO, SUBMETHOD_SBGO, SUBMETHOD_AMV_X, SUBMETHOD_AMV_U, SUBMETHOD_AMV_PLUS_X, SUBMETHOD_AMV_PLUS_U, SUBMETHOD_TANA_X, SUBMETHOD_TANA_U, SUBMETHOD_QMEA_X, SUBMETHOD_QMEA_U, SUBMETHOD_NO_APPROX, SUBMETHOD_EGRA_X, SUBMETHOD_EGRA_U, SUBMETHOD_CONVERGE_ORDER, SUBMETHOD_CONVERGE_QOI, SUBMETHOD_ESTIMATE_ORDER, NO_SURROGATE =0, UNCORRECTED_SURROGATE, AUTO_CORRECTED_SURROGATE, BYPASS_SURROGATE, MODEL_DISCREPANCY, AGGREGATED_MODEL_PAIR, AGGREGATED_MODELS, GENERIC_FNS = 0, OBJECTIVE_FNS, CALIB_TERMS, DEFAULT_VIEW =0, ALL_VIEW, DESIGN_VIEW, UNCERTAIN_VIEW, ALEATORY_UNCERTAIN_VIEW, EPISTEMIC_UNCERTAIN_VIEW, STATE_VIEW, DAUIVar_poisson = 0, DAUIVar_binomial = 1, DAUIVar_negative_binomial = 2, DAUIVar_geometric = 3, DAUIVar_hypergeometric = 4, DAUIVar_histogram_point_int = 5, DAUIVar_Nkinds = 6, TYPE_U =1, TYPE_B =2, TYPE_E =3, TYPE_EB =4, CORR_APPROX_RESPONSE =1, UNCORR_APPROX_RESPONSE, CORR_TRUTH_RESPONSE, UNCORR_TRUTH_RESPONSE, FILEOP_SILENT, FILEOP_WARN, FILEOP_ERROR }
	values for primary response types More...

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	{ MODEL_EVAL_STORE_TOP_METHOD = 0, MODEL_EVAL_STORE_NONE, MODEL_EVAL_STORE_ALL, MODEL_EVAL_STORE_ALL_METHODS, SYNCHRONOUS_INTERFACE, ASYNCHRONOUS_INTERFACE, SUBMETHOD_DEFAULT =0, SUBMETHOD_NONE, SUBMETHOD_COLLABORATIVE, SUBMETHOD_EMBEDDED, SUBMETHOD_SEQUENTIAL, SUBMETHOD_LHS, SUBMETHOD_RANDOM, SUBMETHOD_BOX_BEHNKEN, SUBMETHOD_CENTRAL_COMPOSITE, SUBMETHOD_GRID, SUBMETHOD_OA_LHS, SUBMETHOD_OAS, SUBMETHOD_MFMC, SUBMETHOD_ACV_IS, SUBMETHOD_ACV_MF, SUBMETHOD_ACV_RD, SUBMETHOD_DREAM, SUBMETHOD_GPMSA, SUBMETHOD_MUQ, SUBMETHOD_QUESO, SUBMETHOD_WASABI, SUBMETHOD_CONMIN, SUBMETHOD_DOT, SUBMETHOD_NLPQL, SUBMETHOD_NPSOL, SUBMETHOD_OPTPP, SUBMETHOD_NPSOL_OPTPP, SUBMETHOD_EA, SUBMETHOD_DIRECT, SUBMETHOD_EGO, SUBMETHOD_SBLO, SUBMETHOD_SBGO, SUBMETHOD_AMV_X, SUBMETHOD_AMV_U, SUBMETHOD_AMV_PLUS_X, SUBMETHOD_AMV_PLUS_U, SUBMETHOD_TANA_X, SUBMETHOD_TANA_U, SUBMETHOD_QMEA_X, SUBMETHOD_QMEA_U, SUBMETHOD_NO_APPROX, SUBMETHOD_EGRA_X, SUBMETHOD_EGRA_U, SUBMETHOD_CONVERGE_ORDER, SUBMETHOD_CONVERGE_QOI, SUBMETHOD_ESTIMATE_ORDER, NO_SURROGATE =0, UNCORRECTED_SURROGATE, AUTO_CORRECTED_SURROGATE, BYPASS_SURROGATE, MODEL_DISCREPANCY, AGGREGATED_MODEL_PAIR, AGGREGATED_MODELS, GENERIC_FNS = 0, OBJECTIVE_FNS, CALIB_TERMS, DEFAULT_VIEW =0, ALL_VIEW, DESIGN_VIEW, UNCERTAIN_VIEW, ALEATORY_UNCERTAIN_VIEW, EPISTEMIC_UNCERTAIN_VIEW, STATE_VIEW, DAUIVar_poisson = 0, DAUIVar_binomial = 1, DAUIVar_negative_binomial = 2, DAUIVar_geometric = 3, DAUIVar_hypergeometric = 4, DAUIVar_histogram_point_int = 5, DAUIVar_Nkinds = 6, TYPE_U =1, TYPE_B =2, TYPE_E =3, TYPE_EB =4, CORR_APPROX_RESPONSE =1, UNCORR_APPROX_RESPONSE, CORR_TRUTH_RESPONSE, UNCORR_TRUTH_RESPONSE, FILEOP_SILENT, FILEOP_WARN, FILEOP_ERROR }

enum	{ INTERF_EVAL_STORE_SIMULATION = 0, INTERF_EVAL_STORE_NONE, INTERF_EVAL_STORE_ALL, OBJECTIVE, INEQUALITY_CONSTRAINT, EQUALITY_CONSTRAINT, NO_GRAPH_RECURSION =0, KL_GRAPH_RECURSION, PARTIAL_GRAPH_RECURSION, FULL_GRAPH_RECURSION, NO_CORRECTION =0, ADDITIVE_CORRECTION, MULTIPLICATIVE_CORRECTION, COMBINED_CORRECTION, EMPTY_VIEW =0, RELAXED_ALL, MIXED_ALL, RELAXED_DESIGN, RELAXED_UNCERTAIN, RELAXED_ALEATORY_UNCERTAIN, RELAXED_EPISTEMIC_UNCERTAIN, RELAXED_STATE, MIXED_DESIGN, MIXED_UNCERTAIN, MIXED_ALEATORY_UNCERTAIN, MIXED_EPISTEMIC_UNCERTAIN, MIXED_STATE, DAUSVar_histogram_point_str = 0, DAUSVar_Nkinds = 1, NEW_CANDIDATE = 1, CANDIDATE_ACCEPTED = 2, CANDIDATE_STATE = (NEW_CANDIDATE \| CANDIDATE_ACCEPTED), NEW_CENTER = 8, CENTER_BUILT = 16, CENTER_STATE = (NEW_CENTER \| CENTER_BUILT), NEW_TR_FACTOR = 64, NEW_TRUST_REGION = (NEW_CENTER \| NEW_TR_FACTOR), HARD_CONVERGED = 128, SOFT_CONVERGED = 256, MIN_TR_CONVERGED = 512, MAX_ITER_CONVERGED = 1024, CONVERGED }

enum	{ ABORT_EXITS, ABORT_THROWS, NO_MODEL_SELECTION =0, ALL_MODEL_COMBINATIONS, RF_KARHUNEN_LOEVE =0, RF_PCA_GP, RF_ICA, ALL_VARS =0, ACTIVE_VARS, INACTIVE_VARS, DAURVar_histogram_point_real = 0, DAURVar_Nkinds = 1 }
	values differentiating subsets of variables for I/O

enum	{ CV_ID_DEFAULT = 0, MINIMUM_METRIC, RELATIVE_TOLERANCE, DECREASE_TOLERANCE, SILENT_OUTPUT, QUIET_OUTPUT, NORMAL_OUTPUT, VERBOSE_OUTPUT, DEBUG_OUTPUT, NOCOVAR =0, EXP_L2, EXP_L1, EMPTY_TYPE =0, CONTINUOUS_DESIGN, DISCRETE_DESIGN_RANGE, DISCRETE_DESIGN_SET_INT, DISCRETE_DESIGN_SET_STRING, DISCRETE_DESIGN_SET_REAL, NORMAL_UNCERTAIN, LOGNORMAL_UNCERTAIN, UNIFORM_UNCERTAIN, LOGUNIFORM_UNCERTAIN, TRIANGULAR_UNCERTAIN, EXPONENTIAL_UNCERTAIN, BETA_UNCERTAIN, GAMMA_UNCERTAIN, GUMBEL_UNCERTAIN, FRECHET_UNCERTAIN, WEIBULL_UNCERTAIN, HISTOGRAM_BIN_UNCERTAIN, POISSON_UNCERTAIN, BINOMIAL_UNCERTAIN, NEGATIVE_BINOMIAL_UNCERTAIN, GEOMETRIC_UNCERTAIN, HYPERGEOMETRIC_UNCERTAIN, HISTOGRAM_POINT_UNCERTAIN_INT, HISTOGRAM_POINT_UNCERTAIN_STRING, HISTOGRAM_POINT_UNCERTAIN_REAL, CONTINUOUS_INTERVAL_UNCERTAIN, DISCRETE_INTERVAL_UNCERTAIN, DISCRETE_UNCERTAIN_SET_INT, DISCRETE_UNCERTAIN_SET_STRING, DISCRETE_UNCERTAIN_SET_REAL, CONTINUOUS_STATE, DISCRETE_STATE_RANGE, DISCRETE_STATE_SET_INT, DISCRETE_STATE_SET_STRING, DISCRETE_STATE_SET_REAL, CEUVar_interval = 0, CEUVar_Nkinds = 1 }

enum	{ TABULAR_NONE = 0, TABULAR_HEADER = 1, TABULAR_EVAL_ID = 2, TABULAR_IFACE_ID = 4, TABULAR_EXPER_ANNOT = TABULAR_HEADER \| TABULAR_EVAL_ID, TABULAR_ANNOTATED = TABULAR_HEADER \| TABULAR_EVAL_ID \| TABULAR_IFACE_ID, NO_RESULTS =0, REFINEMENT_RESULTS, INTERMEDIATE_RESULTS, FINAL_RESULTS, SUBSPACE_NORM_DEFAULT =0, SUBSPACE_NORM_MEAN_VALUE, SUBSPACE_NORM_MEAN_GRAD, SUBSPACE_NORM_LOCAL_GRAD, TOTAL_CDV =0, TOTAL_DDIV, TOTAL_DDSV, TOTAL_DDRV, TOTAL_CAUV, TOTAL_DAUIV, TOTAL_DAUSV, TOTAL_DAURV, TOTAL_CEUV, TOTAL_DEUIV, TOTAL_DEUSV, TOTAL_DEURV, TOTAL_CSV, TOTAL_DSIV, TOTAL_DSSV, TOTAL_DSRV, NUM_VC_TOTALS, DEUIVar_interval = 0, DEUIVar_set_int = 1, DEUIVar_Nkinds = 2 }

enum	var_t { VAR_x1, VAR_x2, VAR_x3, VAR_b, VAR_h, VAR_P, VAR_M, VAR_Y, VAR_w, VAR_t, VAR_R, VAR_E, VAR_X, VAR_area_type, VAR_Fs, VAR_P1, VAR_P2, VAR_P3, VAR_B, VAR_D, VAR_H, VAR_F0, VAR_d, VAR_MForm, VAR_x, VAR_xi, VAR_Af, VAR_Ac, VAR_y, VAR_theta, VAR_theta1, VAR_theta2, VAR_delta, VAR_gamma }
	enumeration of possible variable types (to index to names)

enum	driver_t { NO_DRIVER =0, CANTILEVER_BEAM, MOD_CANTILEVER_BEAM, CANTILEVER_BEAM_ML, CYLINDER_HEAD, EXTENDED_ROSENBROCK, GENERALIZED_ROSENBROCK, LF_ROSENBROCK, EXTRA_LF_ROSENBROCK, MF_ROSENBROCK, MODIFIED_ROSENBROCK, ROSENBROCK, LF_POLY_PROD, POLY_PROD, GERSTNER, SCALABLE_GERSTNER, LOGNORMAL_RATIO, MULTIMODAL, PLUGIN_ROSENBROCK, PLUGIN_TEXT_BOOK, SHORT_COLUMN, LF_SHORT_COLUMN, MF_SHORT_COLUMN, SIDE_IMPACT_COST, SIDE_IMPACT_PERFORMANCE, SOBOL_RATIONAL, SOBOL_G_FUNCTION, SOBOL_ISHIGAMI, STEEL_COLUMN_COST, STEEL_COLUMN_PERFORMANCE, TEXT_BOOK, TEXT_BOOK1, TEXT_BOOK2, TEXT_BOOK3, TEXT_BOOK_OUU, SCALABLE_TEXT_BOOK, SCALABLE_MONOMIALS, MOGATEST1, MOGATEST2, MOGATEST3, ILLUMINATION, BARNES, BARNES_LF, HERBIE, SMOOTH_HERBIE, SHUBERT, SALINAS, MODELCENTER, GENZ, DAMPED_OSCILLATOR, ANISOTROPIC_QUADRATIC_FORM, BAYES_LINEAR, STEADY_STATE_DIFFUSION_1D, SS_DIFFUSION_DISCREPANCY, TRANSIENT_DIFFUSION_1D, PREDATOR_PREY, PROBLEM18, TUNABLE_MODEL }
	enumeration of possible direct driver types (to index to names)

enum	local_data_t { VARIABLES_MAP =1, VARIABLES_VECTOR =2 }
	enumeration for how local variables are stored (values must employ a bit representation)

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	sigtype { NO_SIGMA, SCALAR_SIGMA, DIAGONAL_SIGMA, MATRIX_SIGMA }
	special values for sigmaType

enum	edtype { SCALAR_DATA, FUNCTIONAL_DATA }
	special values for experimental data type

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	{ MODEL_EVAL_STORE_TOP_METHOD = 0, MODEL_EVAL_STORE_NONE, MODEL_EVAL_STORE_ALL, MODEL_EVAL_STORE_ALL_METHODS, SYNCHRONOUS_INTERFACE, ASYNCHRONOUS_INTERFACE, SUBMETHOD_DEFAULT =0, SUBMETHOD_NONE, SUBMETHOD_COLLABORATIVE, SUBMETHOD_EMBEDDED, SUBMETHOD_SEQUENTIAL, SUBMETHOD_LHS, SUBMETHOD_RANDOM, SUBMETHOD_BOX_BEHNKEN, SUBMETHOD_CENTRAL_COMPOSITE, SUBMETHOD_GRID, SUBMETHOD_OA_LHS, SUBMETHOD_OAS, SUBMETHOD_MFMC, SUBMETHOD_ACV_IS, SUBMETHOD_ACV_MF, SUBMETHOD_ACV_RD, SUBMETHOD_DREAM, SUBMETHOD_GPMSA, SUBMETHOD_MUQ, SUBMETHOD_QUESO, SUBMETHOD_WASABI, SUBMETHOD_CONMIN, SUBMETHOD_DOT, SUBMETHOD_NLPQL, SUBMETHOD_NPSOL, SUBMETHOD_OPTPP, SUBMETHOD_NPSOL_OPTPP, SUBMETHOD_EA, SUBMETHOD_DIRECT, SUBMETHOD_EGO, SUBMETHOD_SBLO, SUBMETHOD_SBGO, SUBMETHOD_AMV_X, SUBMETHOD_AMV_U, SUBMETHOD_AMV_PLUS_X, SUBMETHOD_AMV_PLUS_U, SUBMETHOD_TANA_X, SUBMETHOD_TANA_U, SUBMETHOD_QMEA_X, SUBMETHOD_QMEA_U, SUBMETHOD_NO_APPROX, SUBMETHOD_EGRA_X, SUBMETHOD_EGRA_U, SUBMETHOD_CONVERGE_ORDER, SUBMETHOD_CONVERGE_QOI, SUBMETHOD_ESTIMATE_ORDER, NO_SURROGATE =0, UNCORRECTED_SURROGATE, AUTO_CORRECTED_SURROGATE, BYPASS_SURROGATE, MODEL_DISCREPANCY, AGGREGATED_MODEL_PAIR, AGGREGATED_MODELS, GENERIC_FNS = 0, OBJECTIVE_FNS, CALIB_TERMS, DEFAULT_VIEW =0, ALL_VIEW, DESIGN_VIEW, UNCERTAIN_VIEW, ALEATORY_UNCERTAIN_VIEW, EPISTEMIC_UNCERTAIN_VIEW, STATE_VIEW, DAUIVar_poisson = 0, DAUIVar_binomial = 1, DAUIVar_negative_binomial = 2, DAUIVar_geometric = 3, DAUIVar_hypergeometric = 4, DAUIVar_histogram_point_int = 5, DAUIVar_Nkinds = 6, TYPE_U =1, TYPE_B =2, TYPE_E =3, TYPE_EB =4, CORR_APPROX_RESPONSE =1, UNCORR_APPROX_RESPONSE, CORR_TRUTH_RESPONSE, UNCORR_TRUTH_RESPONSE, FILEOP_SILENT, FILEOP_WARN, FILEOP_ERROR }

enum	{ INTERF_EVAL_STORE_SIMULATION = 0, INTERF_EVAL_STORE_NONE, INTERF_EVAL_STORE_ALL, OBJECTIVE, INEQUALITY_CONSTRAINT, EQUALITY_CONSTRAINT, NO_GRAPH_RECURSION =0, KL_GRAPH_RECURSION, PARTIAL_GRAPH_RECURSION, FULL_GRAPH_RECURSION, NO_CORRECTION =0, ADDITIVE_CORRECTION, MULTIPLICATIVE_CORRECTION, COMBINED_CORRECTION, EMPTY_VIEW =0, RELAXED_ALL, MIXED_ALL, RELAXED_DESIGN, RELAXED_UNCERTAIN, RELAXED_ALEATORY_UNCERTAIN, RELAXED_EPISTEMIC_UNCERTAIN, RELAXED_STATE, MIXED_DESIGN, MIXED_UNCERTAIN, MIXED_ALEATORY_UNCERTAIN, MIXED_EPISTEMIC_UNCERTAIN, MIXED_STATE, DAUSVar_histogram_point_str = 0, DAUSVar_Nkinds = 1, NEW_CANDIDATE = 1, CANDIDATE_ACCEPTED = 2, CANDIDATE_STATE = (NEW_CANDIDATE \| CANDIDATE_ACCEPTED), NEW_CENTER = 8, CENTER_BUILT = 16, CENTER_STATE = (NEW_CENTER \| CENTER_BUILT), NEW_TR_FACTOR = 64, NEW_TRUST_REGION = (NEW_CENTER \| NEW_TR_FACTOR), HARD_CONVERGED = 128, SOFT_CONVERGED = 256, MIN_TR_CONVERGED = 512, MAX_ITER_CONVERGED = 1024, CONVERGED }

enum	{ ABORT_EXITS, ABORT_THROWS, NO_MODEL_SELECTION =0, ALL_MODEL_COMBINATIONS, RF_KARHUNEN_LOEVE =0, RF_PCA_GP, RF_ICA, ALL_VARS =0, ACTIVE_VARS, INACTIVE_VARS, DAURVar_histogram_point_real = 0, DAURVar_Nkinds = 1 }

enum	{ CV_ID_DEFAULT = 0, MINIMUM_METRIC, RELATIVE_TOLERANCE, DECREASE_TOLERANCE, SILENT_OUTPUT, QUIET_OUTPUT, NORMAL_OUTPUT, VERBOSE_OUTPUT, DEBUG_OUTPUT, NOCOVAR =0, EXP_L2, EXP_L1, EMPTY_TYPE =0, CONTINUOUS_DESIGN, DISCRETE_DESIGN_RANGE, DISCRETE_DESIGN_SET_INT, DISCRETE_DESIGN_SET_STRING, DISCRETE_DESIGN_SET_REAL, NORMAL_UNCERTAIN, LOGNORMAL_UNCERTAIN, UNIFORM_UNCERTAIN, LOGUNIFORM_UNCERTAIN, TRIANGULAR_UNCERTAIN, EXPONENTIAL_UNCERTAIN, BETA_UNCERTAIN, GAMMA_UNCERTAIN, GUMBEL_UNCERTAIN, FRECHET_UNCERTAIN, WEIBULL_UNCERTAIN, HISTOGRAM_BIN_UNCERTAIN, POISSON_UNCERTAIN, BINOMIAL_UNCERTAIN, NEGATIVE_BINOMIAL_UNCERTAIN, GEOMETRIC_UNCERTAIN, HYPERGEOMETRIC_UNCERTAIN, HISTOGRAM_POINT_UNCERTAIN_INT, HISTOGRAM_POINT_UNCERTAIN_STRING, HISTOGRAM_POINT_UNCERTAIN_REAL, CONTINUOUS_INTERVAL_UNCERTAIN, DISCRETE_INTERVAL_UNCERTAIN, DISCRETE_UNCERTAIN_SET_INT, DISCRETE_UNCERTAIN_SET_STRING, DISCRETE_UNCERTAIN_SET_REAL, CONTINUOUS_STATE, DISCRETE_STATE_RANGE, DISCRETE_STATE_SET_INT, DISCRETE_STATE_SET_STRING, DISCRETE_STATE_SET_REAL, CEUVar_interval = 0, CEUVar_Nkinds = 1 }

enum	{ TABULAR_NONE = 0, TABULAR_HEADER = 1, TABULAR_EVAL_ID = 2, TABULAR_IFACE_ID = 4, TABULAR_EXPER_ANNOT = TABULAR_HEADER \| TABULAR_EVAL_ID, TABULAR_ANNOTATED = TABULAR_HEADER \| TABULAR_EVAL_ID \| TABULAR_IFACE_ID, NO_RESULTS =0, REFINEMENT_RESULTS, INTERMEDIATE_RESULTS, FINAL_RESULTS, SUBSPACE_NORM_DEFAULT =0, SUBSPACE_NORM_MEAN_VALUE, SUBSPACE_NORM_MEAN_GRAD, SUBSPACE_NORM_LOCAL_GRAD, TOTAL_CDV =0, TOTAL_DDIV, TOTAL_DDSV, TOTAL_DDRV, TOTAL_CAUV, TOTAL_DAUIV, TOTAL_DAUSV, TOTAL_DAURV, TOTAL_CEUV, TOTAL_DEUIV, TOTAL_DEUSV, TOTAL_DEURV, TOTAL_CSV, TOTAL_DSIV, TOTAL_DSSV, TOTAL_DSRV, NUM_VC_TOTALS, DEUIVar_interval = 0, DEUIVar_set_int = 1, DEUIVar_Nkinds = 2 }

enum	{ RESULTS_OUTPUT_TEXT = 1, RESULTS_OUTPUT_HDF5 = 2, DEFAULT_SYNCHRONIZATION =0, BLOCKING_SYNCHRONIZATION, NONBLOCKING_SYNCHRONIZATION, ROTATION_METHOD_UNRANKED, ROTATION_METHOD_RANKED, DEUSVar_set_str = 0, DEUSVar_Nkinds = 1 }

enum	{ FLEXIBLE_RESULTS, LABELED_RESULTS, DEFAULT_SCHEDULING, MASTER_SCHEDULING, PEER_SCHEDULING, PEER_DYNAMIC_SCHEDULING, PEER_STATIC_SCHEDULING, DYNAMIC_SCHEDULING, STATIC_SCHEDULING, NO_PARALLEL_MODE =0, SURROGATE_MODEL_MODE, TRUTH_MODEL_MODE, SUB_MODEL_MODE, INTERFACE_MODE, DEURVar_set_real = 0, DEURVar_Nkinds = 1 }

enum	{ NO_MODEL_FORMAT =0, TEXT_ARCHIVE =1, BINARY_ARCHIVE =2, ALGEBRAIC_FILE =4, ALGEBRAIC_CONSOLE =8, DEFAULT_CONFIG, PUSH_DOWN, PUSH_UP, NO_DERIVS =0, ALL_DERIVS, MIXED_DERIVS, DiscSetVar_design_set_int = 0, DiscSetVar_design_set_str = 1, DiscSetVar_design_set_real = 2, DiscSetVar_state_set_int = 3, DiscSetVar_state_set_str = 4, DiscSetVar_state_set_real = 5, DiscSetVar_Nkinds = 6 }

enum	{ STD_NORMAL_U, STD_UNIFORM_U, PARTIAL_ASKEY_U, ASKEY_U, EXTENDED_U, FT_LS, FT_RLS2, NUM_UNC_REAL_CONT = 4 }
	number of real-valued uncertain contiguous containers

enum	{ DEFAULT_COVARIANCE, NO_COVARIANCE, DIAGONAL_COVARIANCE, FULL_COVARIANCE, NO_C3_ADVANCEMENT =0, START_RANK_ADVANCEMENT, START_ORDER_ADVANCEMENT, MAX_RANK_ADVANCEMENT, MAX_ORDER_ADVANCEMENT, MAX_RANK_ORDER_ADVANCEMENT, NUM_UNC_INT_CONT = 2 }
	number of int-valued uncertain contiguous containers

enum	{ NO_INT_REFINE =0, IS, AIS, MMAIS, NUM_UNC_STR_CONT = 2 }
	number of string-valued uncertain contiguous containers

enum	miAlg : unsigned short { MI_ALG_KSG1 = 0, MI_ALG_KSG2 = 1 }

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	CG_UPDATETYPE { CG_STEEPEST, CG_FLETCHER_REEVES, CG_POLAK_RIBIERE, CG_POLAK_RIBIERE_PLUS, CG_HESTENES_STIEFEL }
	NonlinearCG update options.

enum	CG_LINESEARCHTYPE { CG_FIXED_STEP, CG_LS_SIMPLE, CG_LS_BRENT, CG_LS_WOLFE }
	NonlinearCG linesearch options.

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	{ MODEL_EVAL_STORE_TOP_METHOD = 0, MODEL_EVAL_STORE_NONE, MODEL_EVAL_STORE_ALL, MODEL_EVAL_STORE_ALL_METHODS, SYNCHRONOUS_INTERFACE, ASYNCHRONOUS_INTERFACE, SUBMETHOD_DEFAULT =0, SUBMETHOD_NONE, SUBMETHOD_COLLABORATIVE, SUBMETHOD_EMBEDDED, SUBMETHOD_SEQUENTIAL, SUBMETHOD_LHS, SUBMETHOD_RANDOM, SUBMETHOD_BOX_BEHNKEN, SUBMETHOD_CENTRAL_COMPOSITE, SUBMETHOD_GRID, SUBMETHOD_OA_LHS, SUBMETHOD_OAS, SUBMETHOD_MFMC, SUBMETHOD_ACV_IS, SUBMETHOD_ACV_MF, SUBMETHOD_ACV_RD, SUBMETHOD_DREAM, SUBMETHOD_GPMSA, SUBMETHOD_MUQ, SUBMETHOD_QUESO, SUBMETHOD_WASABI, SUBMETHOD_CONMIN, SUBMETHOD_DOT, SUBMETHOD_NLPQL, SUBMETHOD_NPSOL, SUBMETHOD_OPTPP, SUBMETHOD_NPSOL_OPTPP, SUBMETHOD_EA, SUBMETHOD_DIRECT, SUBMETHOD_EGO, SUBMETHOD_SBLO, SUBMETHOD_SBGO, SUBMETHOD_AMV_X, SUBMETHOD_AMV_U, SUBMETHOD_AMV_PLUS_X, SUBMETHOD_AMV_PLUS_U, SUBMETHOD_TANA_X, SUBMETHOD_TANA_U, SUBMETHOD_QMEA_X, SUBMETHOD_QMEA_U, SUBMETHOD_NO_APPROX, SUBMETHOD_EGRA_X, SUBMETHOD_EGRA_U, SUBMETHOD_CONVERGE_ORDER, SUBMETHOD_CONVERGE_QOI, SUBMETHOD_ESTIMATE_ORDER, NO_SURROGATE =0, UNCORRECTED_SURROGATE, AUTO_CORRECTED_SURROGATE, BYPASS_SURROGATE, MODEL_DISCREPANCY, AGGREGATED_MODEL_PAIR, AGGREGATED_MODELS, GENERIC_FNS = 0, OBJECTIVE_FNS, CALIB_TERMS, DEFAULT_VIEW =0, ALL_VIEW, DESIGN_VIEW, UNCERTAIN_VIEW, ALEATORY_UNCERTAIN_VIEW, EPISTEMIC_UNCERTAIN_VIEW, STATE_VIEW, DAUIVar_poisson = 0, DAUIVar_binomial = 1, DAUIVar_negative_binomial = 2, DAUIVar_geometric = 3, DAUIVar_hypergeometric = 4, DAUIVar_histogram_point_int = 5, DAUIVar_Nkinds = 6, TYPE_U =1, TYPE_B =2, TYPE_E =3, TYPE_EB =4, CORR_APPROX_RESPONSE =1, UNCORR_APPROX_RESPONSE, CORR_TRUTH_RESPONSE, UNCORR_TRUTH_RESPONSE, FILEOP_SILENT, FILEOP_WARN, FILEOP_ERROR }

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }
	to restrict type of auto scaling allowed

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }
	indicate type of scaling active for a component (bitwise)

enum	EvalType { NO_EVALUATOR, NLF_EVALUATOR, CON_EVALUATOR }
	enumeration for the type of evaluator function

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	{ MODEL_EVAL_STORE_TOP_METHOD = 0, MODEL_EVAL_STORE_NONE, MODEL_EVAL_STORE_ALL, MODEL_EVAL_STORE_ALL_METHODS, SYNCHRONOUS_INTERFACE, ASYNCHRONOUS_INTERFACE, SUBMETHOD_DEFAULT =0, SUBMETHOD_NONE, SUBMETHOD_COLLABORATIVE, SUBMETHOD_EMBEDDED, SUBMETHOD_SEQUENTIAL, SUBMETHOD_LHS, SUBMETHOD_RANDOM, SUBMETHOD_BOX_BEHNKEN, SUBMETHOD_CENTRAL_COMPOSITE, SUBMETHOD_GRID, SUBMETHOD_OA_LHS, SUBMETHOD_OAS, SUBMETHOD_MFMC, SUBMETHOD_ACV_IS, SUBMETHOD_ACV_MF, SUBMETHOD_ACV_RD, SUBMETHOD_DREAM, SUBMETHOD_GPMSA, SUBMETHOD_MUQ, SUBMETHOD_QUESO, SUBMETHOD_WASABI, SUBMETHOD_CONMIN, SUBMETHOD_DOT, SUBMETHOD_NLPQL, SUBMETHOD_NPSOL, SUBMETHOD_OPTPP, SUBMETHOD_NPSOL_OPTPP, SUBMETHOD_EA, SUBMETHOD_DIRECT, SUBMETHOD_EGO, SUBMETHOD_SBLO, SUBMETHOD_SBGO, SUBMETHOD_AMV_X, SUBMETHOD_AMV_U, SUBMETHOD_AMV_PLUS_X, SUBMETHOD_AMV_PLUS_U, SUBMETHOD_TANA_X, SUBMETHOD_TANA_U, SUBMETHOD_QMEA_X, SUBMETHOD_QMEA_U, SUBMETHOD_NO_APPROX, SUBMETHOD_EGRA_X, SUBMETHOD_EGRA_U, SUBMETHOD_CONVERGE_ORDER, SUBMETHOD_CONVERGE_QOI, SUBMETHOD_ESTIMATE_ORDER, NO_SURROGATE =0, UNCORRECTED_SURROGATE, AUTO_CORRECTED_SURROGATE, BYPASS_SURROGATE, MODEL_DISCREPANCY, AGGREGATED_MODEL_PAIR, AGGREGATED_MODELS, GENERIC_FNS = 0, OBJECTIVE_FNS, CALIB_TERMS, DEFAULT_VIEW =0, ALL_VIEW, DESIGN_VIEW, UNCERTAIN_VIEW, ALEATORY_UNCERTAIN_VIEW, EPISTEMIC_UNCERTAIN_VIEW, STATE_VIEW, DAUIVar_poisson = 0, DAUIVar_binomial = 1, DAUIVar_negative_binomial = 2, DAUIVar_geometric = 3, DAUIVar_hypergeometric = 4, DAUIVar_histogram_point_int = 5, DAUIVar_Nkinds = 6, TYPE_U =1, TYPE_B =2, TYPE_E =3, TYPE_EB =4, CORR_APPROX_RESPONSE =1, UNCORR_APPROX_RESPONSE, CORR_TRUTH_RESPONSE, UNCORR_TRUTH_RESPONSE, FILEOP_SILENT, FILEOP_WARN, FILEOP_ERROR }

enum	{ INTERF_EVAL_STORE_SIMULATION = 0, INTERF_EVAL_STORE_NONE, INTERF_EVAL_STORE_ALL, OBJECTIVE, INEQUALITY_CONSTRAINT, EQUALITY_CONSTRAINT, NO_GRAPH_RECURSION =0, KL_GRAPH_RECURSION, PARTIAL_GRAPH_RECURSION, FULL_GRAPH_RECURSION, NO_CORRECTION =0, ADDITIVE_CORRECTION, MULTIPLICATIVE_CORRECTION, COMBINED_CORRECTION, EMPTY_VIEW =0, RELAXED_ALL, MIXED_ALL, RELAXED_DESIGN, RELAXED_UNCERTAIN, RELAXED_ALEATORY_UNCERTAIN, RELAXED_EPISTEMIC_UNCERTAIN, RELAXED_STATE, MIXED_DESIGN, MIXED_UNCERTAIN, MIXED_ALEATORY_UNCERTAIN, MIXED_EPISTEMIC_UNCERTAIN, MIXED_STATE, DAUSVar_histogram_point_str = 0, DAUSVar_Nkinds = 1, NEW_CANDIDATE = 1, CANDIDATE_ACCEPTED = 2, CANDIDATE_STATE = (NEW_CANDIDATE \| CANDIDATE_ACCEPTED), NEW_CENTER = 8, CENTER_BUILT = 16, CENTER_STATE = (NEW_CENTER \| CENTER_BUILT), NEW_TR_FACTOR = 64, NEW_TRUST_REGION = (NEW_CENTER \| NEW_TR_FACTOR), HARD_CONVERGED = 128, SOFT_CONVERGED = 256, MIN_TR_CONVERGED = 512, MAX_ITER_CONVERGED = 1024, CONVERGED }

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }

enum	{ COBYLA, DIRECT, EA, MS, PS, SW, BETA, VARS_ERROR = -11, CONS_ERROR = -10, RESP_ERROR = -9, APPROX_ERROR = -8, METHOD_ERROR = -7, MODEL_ERROR = -6, IO_ERROR = -5, INTERFACE_ERROR = -4, CONSTRUCT_ERROR = -3, PARSE_ERROR = -2, OTHER_ERROR = -1, DEFAULT_INTERFACE =0, APPROX_INTERFACE, FORK_INTERFACE =PROCESS_INTERFACE_BIT, SYSTEM_INTERFACE, GRID_INTERFACE, TEST_INTERFACE =DIRECT_INTERFACE_BIT, PLUGIN_INTERFACE, MATLAB_INTERFACE, LEGACY_PYTHON_INTERFACE, PYTHON_INTERFACE, SCILAB_INTERFACE, DEFAULT_METHOD =0, HYBRID =(META_BIT \| PARALLEL_BIT), PARETO_SET, MULTI_START, RICHARDSON_EXTRAP =(ANALYZER_BIT \| VERIF_BIT), CENTERED_PARAMETER_STUDY =(ANALYZER_BIT \| PSTUDYDACE_BIT), LIST_PARAMETER_STUDY, MULTIDIM_PARAMETER_STUDY, VECTOR_PARAMETER_STUDY, DACE, FSU_CVT, FSU_HALTON, FSU_HAMMERSLEY, PSUADE_MOAT, LOCAL_RELIABILITY =(ANALYZER_BIT \| NOND_BIT), GLOBAL_RELIABILITY, SURROGATE_BASED_UQ, POLYNOMIAL_CHAOS, MULTILEVEL_POLYNOMIAL_CHAOS, MULTIFIDELITY_POLYNOMIAL_CHAOS, STOCH_COLLOCATION, MULTIFIDELITY_STOCH_COLLOCATION, C3_FUNCTION_TRAIN, MULTILEVEL_FUNCTION_TRAIN, MULTIFIDELITY_FUNCTION_TRAIN, CUBATURE_INTEGRATION, SPARSE_GRID_INTEGRATION, QUADRATURE_INTEGRATION, BAYES_CALIBRATION, GPAIS, POF_DARTS, RKD_DARTS, IMPORTANCE_SAMPLING, ADAPTIVE_SAMPLING, MULTILEVEL_SAMPLING, MULTIFIDELITY_SAMPLING, MULTILEVEL_MULTIFIDELITY_SAMPLING, APPROXIMATE_CONTROL_VARIATE, LIST_SAMPLING, RANDOM_SAMPLING, LOCAL_INTERVAL_EST, LOCAL_EVIDENCE, GLOBAL_INTERVAL_EST, GLOBAL_EVIDENCE, SURROGATE_BASED_LOCAL =(MINIMIZER_BIT \| SURRBASED_BIT), DATA_FIT_SURROGATE_BASED_LOCAL, HIERARCH_SURROGATE_BASED_LOCAL, SURROGATE_BASED_GLOBAL, EFFICIENT_GLOBAL, NL2SOL =(MINIMIZER_BIT \| LEASTSQ_BIT), NLSSOL_SQP, OPTPP_G_NEWTON, ASYNCH_PATTERN_SEARCH =(MINIMIZER_BIT \| OPTIMIZER_BIT), OPTPP_PDS, COLINY_BETA, COLINY_COBYLA, COLINY_DIRECT, COLINY_MULTI_START, COLINY_EA, COLINY_PATTERN_SEARCH, COLINY_SOLIS_WETS, MOGA, SOGA, NCSU_DIRECT, MESH_ADAPTIVE_SEARCH, MIT_NOWPAC, MIT_SNOWPAC, GENIE_OPT_DARTS, GENIE_DIRECT, DEMO_TPL, NONLINEAR_CG, OPTPP_CG, OPTPP_Q_NEWTON, OPTPP_FD_NEWTON, OPTPP_NEWTON, NPSOL_SQP, NLPQL_SQP, DOT_BFGS, DOT_FRCG, DOT_MMFD, DOT_SLP, DOT_SQP, CONMIN_FRCG, CONMIN_MFD, ROL, DL_SOLVER, BRANCH_AND_BOUND =(MINIMIZER_BIT \| OPTIMIZER_BIT \| LEASTSQ_BIT), DEFAULT_POINTS, MINIMUM_POINTS, RECOMMENDED_POINTS, TOTAL_POINTS, BASE_RESPONSE =0, SIMULATION_RESPONSE, EXPERIMENT_RESPONSE, DEFAULT_DOMAIN =0, RELAXED_DOMAIN, MIXED_DOMAIN, DEFAULT_CORRECTION = 0, SINGLE_CORRECTION, FULL_MODEL_FORM_CORRECTION, FULL_SOLUTION_LEVEL_CORRECTION, SEQUENCE_CORRECTION, SETUP_MODEL, SETUP_USERFUNC, CAUVar_normal = 0, CAUVar_lognormal = 1, CAUVar_uniform = 2, CAUVar_loguniform = 3, CAUVar_triangular = 4, CAUVar_exponential = 5, CAUVar_beta = 6, CAUVar_gamma = 7, CAUVar_gumbel = 8, CAUVar_frechet = 9, CAUVar_weibull = 10, CAUVar_histogram_bin = 11, CAUVar_Nkinds = 12, ANALYTIC_SOLUTION = 1, REORDERED_ANALYTIC_SOLUTION, R_ONLY_LINEAR_CONSTRAINT, N_VECTOR_LINEAR_CONSTRAINT, R_AND_N_NONLINEAR_CONSTRAINT, N_VECTOR_LINEAR_OBJECTIVE, FULL_TENSOR, FILTERED_TENSOR, RANDOM_TENSOR, AS_FUNC =1, AS_GRAD =2, AS_HESS =4, DISALLOW, TARGET, BOUNDS, SCALE_NONE = 0, SCALE_VALUE = 1, SCALE_LOG = 2, SCALE_AUTO = 4, APPROX_RESPONSE =1, TRUTH_RESPONSE, TH_SILENT_OUTPUT, TH_QUIET_OUTPUT, TH_NORMAL_OUTPUT, TH_VERBOSE_OUTPUT, TH_DEBUG_OUTPUT, DIR_CLEAN, DIR_PERSIST, DIR_ERROR }
	define directory creation options

enum	{ MODEL_EVAL_STORE_TOP_METHOD = 0, MODEL_EVAL_STORE_NONE, MODEL_EVAL_STORE_ALL, MODEL_EVAL_STORE_ALL_METHODS, SYNCHRONOUS_INTERFACE, ASYNCHRONOUS_INTERFACE, SUBMETHOD_DEFAULT =0, SUBMETHOD_NONE, SUBMETHOD_COLLABORATIVE, SUBMETHOD_EMBEDDED, SUBMETHOD_SEQUENTIAL, SUBMETHOD_LHS, SUBMETHOD_RANDOM, SUBMETHOD_BOX_BEHNKEN, SUBMETHOD_CENTRAL_COMPOSITE, SUBMETHOD_GRID, SUBMETHOD_OA_LHS, SUBMETHOD_OAS, SUBMETHOD_MFMC, SUBMETHOD_ACV_IS, SUBMETHOD_ACV_MF, SUBMETHOD_ACV_RD, SUBMETHOD_DREAM, SUBMETHOD_GPMSA, SUBMETHOD_MUQ, SUBMETHOD_QUESO, SUBMETHOD_WASABI, SUBMETHOD_CONMIN, SUBMETHOD_DOT, SUBMETHOD_NLPQL, SUBMETHOD_NPSOL, SUBMETHOD_OPTPP, SUBMETHOD_NPSOL_OPTPP, SUBMETHOD_EA, SUBMETHOD_DIRECT, SUBMETHOD_EGO, SUBMETHOD_SBLO, SUBMETHOD_SBGO, SUBMETHOD_AMV_X, SUBMETHOD_AMV_U, SUBMETHOD_AMV_PLUS_X, SUBMETHOD_AMV_PLUS_U, SUBMETHOD_TANA_X, SUBMETHOD_TANA_U, SUBMETHOD_QMEA_X, SUBMETHOD_QMEA_U, SUBMETHOD_NO_APPROX, SUBMETHOD_EGRA_X, SUBMETHOD_EGRA_U, SUBMETHOD_CONVERGE_ORDER, SUBMETHOD_CONVERGE_QOI, SUBMETHOD_ESTIMATE_ORDER, NO_SURROGATE =0, UNCORRECTED_SURROGATE, AUTO_CORRECTED_SURROGATE, BYPASS_SURROGATE, MODEL_DISCREPANCY, AGGREGATED_MODEL_PAIR, AGGREGATED_MODELS, GENERIC_FNS = 0, OBJECTIVE_FNS, CALIB_TERMS, DEFAULT_VIEW =0, ALL_VIEW, DESIGN_VIEW, UNCERTAIN_VIEW, ALEATORY_UNCERTAIN_VIEW, EPISTEMIC_UNCERTAIN_VIEW, STATE_VIEW, DAUIVar_poisson = 0, DAUIVar_binomial = 1, DAUIVar_negative_binomial = 2, DAUIVar_geometric = 3, DAUIVar_hypergeometric = 4, DAUIVar_histogram_point_int = 5, DAUIVar_Nkinds = 6, TYPE_U =1, TYPE_B =2, TYPE_E =3, TYPE_EB =4, CORR_APPROX_RESPONSE =1, UNCORR_APPROX_RESPONSE, CORR_TRUTH_RESPONSE, UNCORR_TRUTH_RESPONSE, FILEOP_SILENT, FILEOP_WARN, FILEOP_ERROR }
	enum indicating action on failed file operation More...

Functions
void	batch_means_interval (RealMatrix &mcmc_matrix, RealMatrix &interval_matrix, RealMatrix &means_matrix, int moment, Real alpha)

void	batch_means_percentile (RealMatrix &mcmc_matrix, RealMatrix &interval_matrix, RealMatrix &means_matrix, Real percentile, Real alpha)

CommandShell &	flush (CommandShell &shell)
	convenient shell manipulator function to "flush" the shell More...

void	read_sized_data (std::istream &s, RealVectorArray &va, size_t num_rows, int num_cols)

void	read_fixed_rowsize_data (std::istream &s, RealVectorArray &va, int num_cols, bool row_major)

void	read_unsized_data (std::istream &s, RealVectorArray &va, bool row_major)

void	read_config_vars_multifile (const std::string &basename, int num_expts, int ncv, std::vector< Variables > &config_vars)

void	read_config_vars_singlefile (const std::string &basename, int num_expts, int ncv, std::vector< Variables > &config_vars)

void	read_field_values (const std::string &basename, int expt_num, RealVectorArray &field_vars)

void	read_field_values (const std::string &basename, int expt_num, RealVector &field_vars)

void	read_coord_values (const std::string &basename, int expt_num, RealMatrix &coords)

void	read_coord_values (const std::string &basename, RealMatrix &coords)

void	read_covariance (const std::string &basename, int expt_num, RealMatrix &cov_vals)

void	read_covariance (const std::string &basename, int expt_num, Dakota::CovarianceMatrix::FORMAT format, int num_vals, RealMatrix &cov_vals)

bool	nearby (const RealVector &rv1, const RealVector &rv2, Real rel_tol)
	tolerance-based equality operator for RealVector

bool	operator== (const ShortArray &dsa1, const ShortArray &dsa2)
	equality operator for ShortArray

bool	operator== (const StringArray &dsa1, const StringArray &dsa2)
	equality operator for StringArray

Real	rel_change_L2 (const RealVector &curr_rv, const RealVector &prev_rv)
	Computes relative change between RealVectors using Euclidean L2 norm.

Real	rel_change_L2 (const RealVector &curr_rv1, const RealVector &prev_rv1, const IntVector &curr_iv, const IntVector &prev_iv, const RealVector &curr_rv2, const RealVector &prev_rv2)
	Computes relative change between Real/int/Real vector triples using Euclidean L2 norm.

void	compute_col_means (RealMatrix &matrix, RealVector &avg_vals)
	Computes means of columns of matrix.

void	compute_col_stdevs (RealMatrix &matrix, RealVector &avg_vals, RealVector &std_devs)
	Computes standard deviations of columns of matrix.

void	sort_vector (const RealVector &vec, RealVector &sort_vec, IntVector &indices)
	Sort incoming vector with result and corresponding indices returned in passed arguments.

void	sort_matrix_columns (const RealMatrix &mat, RealMatrix &sort_mat, IntMatrix &indices)
	Sort incoming matrix columns with result and corresponding indices returned in passed arguments.

void	center_matrix_rows (RealMatrix &mat)
	center the incoming matrix rows by their means, in-place

void	center_matrix_cols (RealMatrix &mat)
	center the incoming matrix columns by their means, in-place

bool	is_matrix_symmetric (const RealMatrix &matrix)
	Test if incoming matrix is symmetric.

void	remove_column (RealMatrix &matrix, int index)
	Removes column from matrix.

void	copy_data (const MatrixXd &src_mat, RealMatrix &dst_mat)
	Copy data from Eigen::MatrixXd to RealMatrix.

void	copy_data (const RealMatrix &src_mat, MatrixXd &dst_mat)
	Copy data from RealMatrix to Eigen::MatrixXd.

void	view_data (const RealMatrix &src_mat, Eigen::Map< MatrixXd > &dst_mat)
	Create a view of data in RealMatrix as an Eigen::MatrixXd.

std::vector< std::string >	strsplit (const std::string &input)
	Trim then split a string on {space, tab} and return as vector of strings.

std::string::size_type	longest_strlen (const std::vector< std::string > &vecstr)
	Return the length of the longest string in the passed vector.

void	iround (const RealVector &input_vec, IntVector &rounded_vec)
	round entries of a RealVector yielding an IntVector

bool	operator== (const IntArray &dia1, const IntArray &dia2)
	equality operator for IntArray

template<typename T >
bool	operator== (const std::vector< T > &vec, typename boost::multi_array< T, 1 >::template const_array_view< 1 >::type mav)
	equality operator for std::vector and boost::multi_array::const_array_view

template<typename T >
bool	operator== (typename boost::multi_array< T, 1 >::template const_array_view< 1 >::type mav, const std::vector< T > &vec)
	equality operator for boost::multi_array::const_array_view and std::vector

template<typename T >
bool	operator== (const boost::multi_array< T, 1 > &ma, typename boost::multi_array< T, 1 >::template const_array_view< 1 >::type mav)
	equality operator for boost::multi_array and boost::multi_array::const_array_view

template<typename T >
bool	operator== (typename boost::multi_array< T, 1 >::template const_array_view< 1 >::type mav, const boost::multi_array< T, 1 > &ma)
	equality operator for boost::multi_array::const_array_view and boost::multi_array

bool	operator!= (const IntArray &dia1, const IntArray &dia2)
	inequality operator for IntArray

bool	operator!= (const ShortArray &dsa1, const ShortArray &dsa2)
	inequality operator for ShortArray

bool	operator!= (const StringArray &dsa1, const StringArray &dsa2)
	inequality operator for StringArray

template<typename T >
bool	operator!= (const std::vector< T > &vec, typename boost::multi_array< T, 1 >::template const_array_view< 1 >::type mav)
	inequality operator for std::vector and boost::multi_array::const_array_view

template<typename T >
bool	operator!= (typename boost::multi_array< T, 1 >::template const_array_view< 1 >::type mav, const std::vector< T > &vec)
	inequality operator for boost::multi_array::const_array_view and std::vector

template<typename T >
bool	operator!= (const boost::multi_array< T, 1 > &ma, typename boost::multi_array< T, 1 >::template const_array_view< 1 >::type mav)
	inequality operator for boost::multi_array and boost::multi_array::const_array_view

template<typename T >
bool	operator!= (typename boost::multi_array< T, 1 >::template const_array_view< 1 >::type mav, const boost::multi_array< T, 1 > &ma)
	inequality operator for boost::multi_array::const_array_view and boost::multi_array

template<typename OrdinalType >
bool	non_zero (const std::vector< OrdinalType > &vec)
	checks for any non-zero value in std::vector(); useful for determining whether an array of request codes (e.g., an ASV) has any actionable content

template<typename VectorType >
bool	is_equal_vec (const RealVector &vec1, const VectorType &vec2)
	equality function for RealVector and a vector of arbitrary type

template<typename OrdinalType1 , typename OrdinalType2 , typename ScalarType >
bool	is_equal_partial (const Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &vec1, const Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &vec2, OrdinalType2 start_index2)
	partial equality operator for navigating different views

bool	is_equal_partial (const StringMultiArray &ma1, const StringMultiArray &ma2, size_t start_index2)
	partial equality operator for navigating different views

template<typename MatrixType , typename VectorType >
void	apply_matrix_partial (const MatrixType &M, const VectorType &v1, VectorType &v2)
	Applies a RealMatrix to a vector (or subset of vector) v1. More...

template<typename VectorType >
void	apply_matrix_transpose_partial (const RealMatrix &M, const VectorType &v1, VectorType &v2)
	Applies transpose of a RealMatrix to a vector (or subset of vector) v1. More...

std::string	strtolower (const std::string &s)
	Return lowercase copy of string s.

bool	strbegins (const std::string &input, const std::string &test)
	Return true if input string begins with string test.

bool	strends (const std::string &input, const std::string &test)
	Return true if input string ends with string test.

bool	strcontains (const std::string &input, const std::string &test)
	Return true if input string contains string test.

void	build_label (String &label, const String &root_label, size_t tag, const String &separator="")
	create a label by appending a numerical tag to the root_label, o

void	build_labels (StringArray &label_array, const String &root_label)
	create an array of labels by tagging root_label for each entry in label_array. Uses build_label().

void	build_labels (StringMultiArray &label_array, const String &root_label)
	create an array of labels by tagging root_label for each entry in label_array. Uses build_label().

void	build_labels_partial (StringArray &label_array, const String &root_label, size_t start_index, size_t num_items)
	create a partial array of labels by tagging root_label for a subset of entries in label_array. Uses build_label().

template<typename vecType , typename valueType >
void	assign_value (vecType &target, valueType val)
	assign a value to an arbitrary vector

template<typename vecType , typename valueType >
void	assign_value (vecType &target, valueType val, size_t start, size_t len)
	assign a value to a portion of an arbitrary vector

template<typename OrdinalType , typename ScalarType >
void	copy_data (const std::vector< Teuchos::SerialDenseVector< OrdinalType, ScalarType > > &sdva, Teuchos::SerialDenseMatrix< OrdinalType, ScalarType > &sdm)
	copy Array<Teuchos::SerialDenseVector<OT,ST> > to Teuchos::SerialDenseMatrix<OT,ST> - used by read_data_tabular - RWH

template<typename OrdinalType , typename ScalarType >
void	copy_data_transpose (const std::vector< Teuchos::SerialDenseVector< OrdinalType, ScalarType > > &sdva, Teuchos::SerialDenseMatrix< OrdinalType, ScalarType > &sdm)
	copy Array<Teuchos::SerialDenseVector<OT,ST> > to transposed Teuchos::SerialDenseMatrix<OT,ST> - used by read_data_tabular - RWH

template<typename OrdinalType1 , typename OrdinalType2 , typename ScalarType >
void	copy_data (const Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv, Teuchos::SerialDenseMatrix< OrdinalType1, ScalarType > &sdm, OrdinalType2 nr, OrdinalType2 nc)
	copy Teuchos::SerialDenseVector<OT,ST> to Teuchos::SerialDenseMatrix<OT,ST> - used by NestedModel::update_sub_iterator - RWH

template<typename T >
void	copy_data (const std::vector< std::vector< T > > &d2a, std::vector< T > &da)
	copy std::vector<vector<T> > to std::vector<T>(unroll vecOfvecs into vector) - used by ProcessApplicInterface::write_parameters_files - RWH

template<typename T >
void	copy_data (const std::map< int, T > &im, std::vector< T > &da)
	copy map<int, T> to std::vector<T> (discard integer keys) - used by SurrBasedGlobalMinimizer::core_run - RWH

template<typename OrdinalType , typename ScalarType >
void	copy_data (const Teuchos::SerialDenseVector< OrdinalType, ScalarType > &sdv1, Teuchos::SerialDenseVector< OrdinalType, ScalarType > &sdv2)
	copy Teuchos::SerialDenseVector<OrdinalType, ScalarType> to same (used in place of operator= when a deep copy is required) - used by Response - MSE

template<typename OrdinalType , typename ScalarType >
void	copy_data (const Teuchos::SerialDenseMatrix< OrdinalType, ScalarType > &sdm1, Teuchos::SerialDenseMatrix< OrdinalType, ScalarType > &sdm2)
	copy Teuchos::SerialDenseMatrix<OrdinalType, ScalarType> to same (used in place of operator= when a deep copy is required) - used by Response - MSE

template<typename OrdinalType , typename ScalarType >
void	copy_data (const Teuchos::SerialSymDenseMatrix< OrdinalType, ScalarType > &ssdm1, Teuchos::SerialSymDenseMatrix< OrdinalType, ScalarType > &ssdm2)
	copy Teuchos::SerialSymDenseMatrix<OrdinalType, ScalarType> to same (used in place of operator= when a deep copy is required) - used by Response - MSE

void	copy_data (const RealMatrix &source, RealMatrix &dest, int num_rows, int num_cols, int start_row=0, int start_col=0)
	Taken from pecos/src/MathTools.hpp, BUT not templated because the implementation is specific to RealMatrix.

template<typename OrdinalType , typename ScalarType , typename VecType >
void	copy_data (const Teuchos::SerialDenseVector< OrdinalType, ScalarType > &sdv, VecType &vec)
	copy Teuchos::SerialDenseVector<OrdinalType, ScalarType> to VecType - used by APPS for HOPS vector types

template<typename OrdinalType , typename ScalarType1 , typename ScalarType2 >
void	copy_data (const Teuchos::SerialDenseVector< OrdinalType, ScalarType1 > &sdv, std::vector< ScalarType2 > &vec)
	copy Teuchos::SerialDenseVector<OrdinalType, ScalarType> to std::vector<ScalarType> - used by DakotaModel

template<typename OrdinalType , typename ScalarType >
void	copy_data (const std::vector< ScalarType > &da, Teuchos::SerialDenseVector< OrdinalType, ScalarType > &sdv)
	copy Array<ScalarType> to Teuchos::SerialDenseVector<OrdinalType, ScalarType> - used by NOWPACOptimizer - MSE

template<typename OrdinalType1 , typename OrdinalType2 , typename ScalarType >
void	copy_data (const ScalarType *ptr, const OrdinalType2 ptr_len, Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv)
	copy ScalarType* to Teuchos::SerialDenseVector<OrdinalType, ScalarType> - used by ScalingModel::response_modify_n2s - RWH

template<typename OrdinalType1 , typename OrdinalType2 , typename ScalarType >
void	copy_data (const Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv, ScalarType *ptr, const OrdinalType2 ptr_len)
	copy ScalarType* to Teuchos::SerialDenseVector<OrdinalType, ScalarType> - used by NL2SOLLeastSq::core_run - RWH

template<typename OrdinalType , typename ScalarType >
void	copy_data (const ScalarType ptr1, ScalarType ptr2, const OrdinalType ptr_len)
	copy ScalarType* to ScalarType*

template<typename OrdinalType1 , typename OrdinalType2 , typename ScalarType >
void	copy_data (const Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv, std::vector< Teuchos::SerialDenseVector< OrdinalType1, ScalarType > > &sdva, OrdinalType2 num_vec, OrdinalType2 vec_len)
	copy SerialDenseVector<> to Array<SerialDenseVector<> > - used by ConcurrentMetaIterator constructor - RWH

template<typename vecType1 , typename vecType2 >
void	copy_data_partial (const vecType1 &source, size_t source_start_idx, vecType2 &target, size_t target_start_idx, size_t len)
	copy a portion arbitrary vector to all of another arbitrary vector

template<typename OrdinalType1 , typename OrdinalType2 , typename ScalarType >
void	copy_data_partial (const Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv1, OrdinalType2 start_index1, OrdinalType2 num_items, Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv2)
	copy portion of first SerialDenseVector to all of second SerialDenseVector - used by DataTransformModel::vars_mapping - RWH

template<typename OrdinalType1 , typename OrdinalType2 , typename ScalarType >
void	copy_data_partial (const Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv1, Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv2, OrdinalType2 start_index2)
	copy all of first SerialDenseVector to portion of second SerialDenseVector - used by MixedVariables - RWH, NLSSOLLeastSq - BMA

template<typename OrdinalType1 , typename OrdinalType2 , typename ScalarType >
void	copy_data_partial (const Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv1, OrdinalType2 start_index1, OrdinalType2 num_items, Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv2, OrdinalType2 start_index2)
	copy portion of first SerialDenseVector to portion of second SerialDenseVector - used by ScalingModel::secondary_resp_scaled2native - RWH

template<typename OrdinalType1 , typename OrdinalType2 , typename ScalarType >
void	copy_data_partial (const Teuchos::SerialDenseVector< OrdinalType1, ScalarType > &sdv1, std::vector< ScalarType > &da2, OrdinalType2 start_index2)
	copy all of first SerialDenseVector to portion of second SerialDenseVector - used by SharedSurfpackApproxData::merge_variable_arrays - RWH

template<typename T >
void	copy_data_partial (const std::vector< T > &da1, size_t start_index1, size_t num_items, std::vector< T > &da2)
	copy portion of first Array<T> to all of second Array<T> - used by SharedResponseDataRep constructor - RWH

template<typename T >
void	copy_data_partial (const std::vector< T > &da1, std::vector< T > &da2, size_t start_index2)
	copy all of first Array<T> to portion of second Array<T> - used by ParamStudy::multidim_loop - RWH

template<typename T >
void	copy_data_partial (const std::vector< T > &da, boost::multi_array< T, 1 > &bma, size_t start_index_bma)
	copy all of first Array<T> to portion of boost::multi_array<T, 1> - used by RelaxedVariables - RWH

template<typename VectorType >
void	copy_column_vector (const RealMatrix &m, RealMatrix::ordinalType j, VectorType &col)
	Copies a column of a Teuchos_SerialDenseMatrix<int,Real> to std::vector<Real>

template<typename VectorType >
void	copy_row_vector (const RealMatrix &m, RealMatrix::ordinalType i, VectorType &row)
	Copies a row of a Teuchos_SerialDenseMatrix<int,Real> to std::vector<Real>

template<typename ScalarType >
void	insert_row_vector (const std::vector< ScalarType > &row, RealMatrix::ordinalType i, RealMatrix &m)
	Inserts a std::vector<Real> into a row of a Teuchos_SerialDenseMatrix<int,Real>

void	merge_data_partial (const IntVector &d_vec, RealVector &m_vec, size_t start_index_ma)
	merge a discrete integer vector into a single continuous vector

void	merge_data_partial (const IntVector &d_vec, RealArray &m_array, size_t start_index_ma)
	merge a discrete integer vector into a single continuous array

template<typename OrdinalType , typename ScalarType >
const ScalarType &	set_index_to_value (OrdinalType index, const std::set< ScalarType > &values)
	retrieve the set value corresponding to the passed index

template<typename ScalarType >
size_t	set_value_to_index (const ScalarType &value, const std::set< ScalarType > &values)
	calculate the set index corresponding to the passed value

template<typename OrdinalType , typename KeyType , typename ValueType >
const KeyType &	map_index_to_key (OrdinalType index, const std::map< KeyType, ValueType > &pairs)
	retrieve the set value corresponding to the passed index

template<typename OrdinalType , typename KeyType , typename ValueType >
const ValueType &	map_index_to_value (OrdinalType index, const std::map< KeyType, ValueType > &pairs)
	retrieve the set value corresponding to the passed index

template<typename KeyType , typename ValueType >
void	map_keys_to_set (const std::map< KeyType, ValueType > &source_map, std::set< KeyType > &target_set)
	calculate the map index corresponding to the passed key

template<typename KeyType , typename ValueType >
size_t	map_key_to_index (const KeyType &key, const std::map< KeyType, ValueType > &pairs)
	calculate the map index corresponding to the passed key

template<typename KeyType , typename ValueType >
size_t	map_value_to_index (const ValueType &value, const std::map< KeyType, ValueType > &pairs)
	calculate the map index corresponding to the passed value (not the key)

template<typename KeyType , typename ValueType >
size_t	map_value_to_index (const ValueType &value, const std::multimap< KeyType, ValueType > &pairs)
	calculate the map index corresponding to the passed value (not the key)

template<typename OrdinalType , typename ScalarType >
void	x_y_pairs_to_x_set (const Teuchos::SerialDenseVector< OrdinalType, ScalarType > &xy_pairs, std::set< ScalarType > &x_set)
	convert a SerialDenseVector of head-to-tail (x,y) pairs into a std::set of (x), discarding the y values

template<typename ScalarType >
ScalarType	find_min (const std::vector< ScalarType > &vec)

template<typename OrdinalType , typename ScalarType >
ScalarType	find_min (const std::vector< ScalarType > &vec, OrdinalType start, OrdinalType end)

template<typename OrdinalType , typename ScalarType >
ScalarType	find_min (const Teuchos::SerialDenseVector< OrdinalType, ScalarType > &vec)

template<typename OrdinalType , typename ScalarType >
ScalarType	find_min (const ScalarType *vec, OrdinalType len)

template<typename ScalarType >
ScalarType	find_max (const std::vector< ScalarType > &vec)

template<typename OrdinalType , typename ScalarType >
ScalarType	find_max (const Teuchos::SerialDenseVector< OrdinalType, ScalarType > &vec)

template<typename OrdinalType , typename ScalarType >
ScalarType	find_max (const ScalarType *vec, const OrdinalType len)

template<typename ContainerType >
size_t	find_index (const ContainerType &c, const typename ContainerType::value_type &search_data)
	generic find_index (inactive)

template<typename T >
size_t	find_index (const boost::multi_array< T, 1 > &bma, const T &search_data)
	compute the index of an entry within a boost::multi_array

size_t	find_index (SizetMultiArrayConstView bmacv, size_t search_data)
	compute the index of an entry within a boost::multi_array view

size_t	find_index (StringMultiArrayConstView bmacv, const String &search_data)
	compute the index of an entry within a boost::multi_array view

template<typename ListT >
size_t	find_index (const ListT &l, const typename ListT::value_type &val)
	compute the index of an entry within a std::list

template<typename ListT >
ListT::const_iterator	find_if (const ListT &c, bool(*test_fn)(const typename ListT::value_type &, const std::string &), const std::string &test_fn_data)
	return an iterator to the first list element satisfying the predicate test_fn w.r.t. the passed test_fn_data; end if not found

template<typename VectorType , typename ScalarType >
void	copy_data (const std::vector< VectorType > &va, ScalarType *ptr, int ptr_len)

void	copy_data (SizetMultiArrayConstView ma, SizetArray &da)
	copy boost::multi_array view to Array - used by ActiveSet::derivative_vector - RWH

void	copy_data (StringMultiArrayConstView ma, StringArray &da)
	copy boost::multi_array view to Array - used by Pecos::copy_data - RWH

template<typename DakContainerType >
bool	contains (const DakContainerType &v, const typename DakContainerType::value_type &val)
	return true if the item val appears in container v

template<class RandomIt , class URBG >
void	rand_shuffle (RandomIt first, RandomIt last, URBG &&g)
	Random shuffle with C++17 shuffle API, but using Boost for portability.

void	abort_handler (int code)
	global function which handles serial or parallel aborts

void	abort_throw_or_exit (int dakota_code)
	throw or exit depending on abort_mode More...

void	register_signal_handlers ()
	Tie various signal handlers to Dakota's abort_handler function. More...

void	mpi_debug_hold ()
	Global function to hold Dakota processes to help with MPI debugging. More...

template<typename T >
T	abort_handler_t (int code)

void	svd (RealMatrix &matrix, RealVector &singular_vals, RealMatrix &v_trans, bool compute_vectors=true)
	Compute the SVD of an arbitrary matrix A = USV^T. More...

void	singular_values (RealMatrix &matrix, RealVector &singular_values)
	compute the singular values without storing any singular vectors (A will be destroyed)

int	qr (RealMatrix &A)
	Compute an in-place QR factorization A = QR. More...

int	qr_rsolve (const RealMatrix &q_r, bool transpose, RealMatrix &rhs)
	Perform a multiple right-hand sides Rinv * rhs solve using the R from a qr factorization. More...

double	det_AtransA (RealMatrix &A)
	Use SVD to compute det(A'*A), destroying A with the SVD.

std::string	string_to_tmpfile (const std::string &dump_string)
	utility to write an input string to a tmpfile in PWD

std::string	pyprepro_input (const std::string &template_file, const std::string &preproc_cmd="pyprepro.py")
	run pyprepro on the user-provided input file and return generated tmp output

ResultsKeyType	make_key (const StrStrSizet &iterator_id, const std::string &data_name)
	Make a full ResultsKeyType from the passed iterator_id and data_name.

MetaDataValueType	make_metadatavalue (StringMultiArrayConstView labels)
	create MetaDataValueType from the passed strings

MetaDataValueType	make_metadatavalue (StringMultiArrayConstView cv_labels, StringMultiArrayConstView div_labels, StringMultiArrayConstView drv_labels, const StringArray &resp_labels)
	create MetaDataValueType from the passed strings

MetaDataValueType	make_metadatavalue (const StringArray &resp_labels)
	create MetaDataValueType from the passed strings

MetaDataValueType	make_metadatavalue (const std::string &)
	create MetaDataValueType from the passed strings

MetaDataValueType	make_metadatavalue (const std::string &, const std::string &)
	create MetaDataValueType from the passed strings

MetaDataValueType	make_metadatavalue (const std::string &, const std::string &, const std::string &)
	create MetaDataValueType from the passed strings

MetaDataValueType	make_metadatavalue (const std::string &, const std::string &, const std::string &, const std::string &)
	create MetaDataValueType from the passed strings

MetaDataValueType	make_metadatavalue (StringMultiArrayConstView cv_labels, StringMultiArrayConstView div_labels, StringMultiArrayConstView dsv_labels, StringMultiArrayConstView drv_labels, const StringArray &resp_labels)

int	generate_system_seed ()
	clock microseconds-based random seed in [1, 1000000] More...

void	compute_regression_coeffs (const RealMatrix &samples, const RealMatrix &resps, RealMatrix &coeffs, RealVector &cods)
	Compute (non-standardized) linear regression coefficients and return R^2.

void	compute_std_regression_coeffs (const RealMatrix &samples, const RealMatrix &resps, RealMatrix &coeffs, RealVector &cods)
	Compute standardized linear regression coefficients.

std::istream &	operator>> (std::istream &s, ActiveSet &set)
	std::istream extraction operator for ActiveSet. Calls read(std::istream&).

std::ostream &	operator<< (std::ostream &s, const ActiveSet &set)
	std::ostream insertion operator for ActiveSet. Calls write(std::istream&).

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, ActiveSet &set)
	MPIUnpackBuffer extraction operator for ActiveSet. Calls read(MPIUnpackBuffer&).

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const ActiveSet &set)
	MPIPackBuffer insertion operator for ActiveSet. Calls write(MPIPackBuffer&).

bool	operator!= (const ActiveSet &set1, const ActiveSet &set2)
	inequality operator for ActiveSet More...

std::istream &	operator>> (std::istream &s, Constraints &con)
	std::istream extraction operator for Constraints

std::ostream &	operator<< (std::ostream &s, const Constraints &con)
	std::ostream insertion operator for Constraints

std::string	re_match (const std::string &token, const boost::regex &re)
	Global utility function to ease migration from CtelRegExp to Boost.Regex.

bool	interface_id_compare (const Interface &interface_in, const void *id)
	global comparison function for Interface

bool	method_id_compare (const Iterator &iterator, const void *id)
	global comparison function for Iterator

bool	model_id_compare (const Model &model, const void *id)
	global comparison function for Model

bool	operator== (const Model &m1, const Model &m2)
	equality operator for Envelope is true if same letter instance More...

bool	operator!= (const Model &m1, const Model &m2)
	inequality operator for Envelope is true if different letter instance More...

template<typename VecT >
void	get_initial_values (const Model &model, VecT &values)

template<typename VecT >
bool	get_bounds (const RealVector &lower_source, const RealVector &upper_source, VecT &lower_target, VecT &upper_target, Real big_real_bound_size, Real no_value)

template<typename VecT >
void	get_bounds (const Model &model, VecT &lower_target, VecT &upper_target)

template<typename SetT , typename VecT >
void	get_bounds (const SetT &source_set, VecT &lower_target, VecT &upper_target, int target_offset)

template<typename OrdinalType , typename ScalarType , typename VectorType2 , typename MaskType , typename SetArray >
bool	get_mixed_bounds (const MaskType &mask_set, const SetArray &source_set, const Teuchos::SerialDenseVector< OrdinalType, ScalarType > &lower_source, const Teuchos::SerialDenseVector< OrdinalType, ScalarType > &upper_source, VectorType2 &lower_target, VectorType2 &upper_target, ScalarType bigBoundSize, ScalarType no_value, int target_offset=0)

template<typename AdapterT >
bool	get_variable_bounds (Model &model, Real big_real_bound_size, int big_int_bound_size, typename AdapterT::VecT &lower, typename AdapterT::VecT &upper)

template<typename RVecT , typename IVecT >
int	configure_inequality_constraint_maps (const Model &model, Real big_real_bound_size, CONSTRAINT_TYPE ctype, IVecT &map_indices, RVecT &map_multipliers, RVecT &map_offsets, Real scaling=1.0)

template<typename RVecT , typename IVecT >
void	configure_equality_constraint_maps (Model &model, CONSTRAINT_TYPE ctype, IVecT &indices, size_t index_offset, RVecT &multipliers, RVecT &values, bool make_one_sided)

template<typename AdapterT >
void	get_linear_constraints (Model &model, Real big_real_bound_size, typename AdapterT::VecT &lin_ineq_lower_bnds, typename AdapterT::VecT &lin_ineq_upper_bnds, typename AdapterT::VecT &lin_eq_targets, typename AdapterT::MatT &lin_ineq_coeffs, typename AdapterT::MatT &lin_eq_coeffs)

template<typename VecT >
void	apply_linear_constraints (const Model &model, CONSTRAINT_EQUALITY_TYPE etype, const VecT &in_vals, VecT &values, bool adjoint=false)

template<typename VecT >
void	apply_nonlinear_constraints (const Model &model, CONSTRAINT_EQUALITY_TYPE etype, const VecT &in_vals, VecT &values, bool adjoint=false)

template<typename VectorType1 , typename VectorType2 , typename SetArray >
void	copy_variables (const VectorType1 &source, const BitArray &set_bits, const SetArray &set_vars, VectorType2 &dest, size_t offset, size_t len)

template<typename VectorType1 , typename VectorType2 , typename SetArray >
void	copy_variables (const VectorType1 &source, const SetArray &set_vars, VectorType2 &dest, size_t offset, size_t len)

template<typename VectorType1 , typename VectorType2 >
void	copy_variables (const VectorType1 &source, VectorType2 &dest, const BitArray &int_set_bits, const IntSetArray &set_int_vars, size_t offset, size_t len)

template<typename AdapterT >
void	set_best_responses (typename AdapterT::OptT &optimizer, const Model &model, bool set_objectives, size_t num_user_primary_fns, const std::vector< int > constraint_map_indices, const std::vector< double > constraint_map_multipliers, const std::vector< double > constraint_map_offsets, ResponseArray &response_array)

template<typename VectorType >
void	set_variables (const VectorType &source, Model &model, Variables &vars)

template<typename VectorType >
void	get_variables (Model &model, VectorType &vec)

template<typename vectorType >
void	get_responses (const Model &model, const RealVector &dak_fn_vals, const std::vector< int > constraint_map_indices, const std::vector< double > constraint_map_multipliers, const std::vector< double > constraint_map_offsets, vectorType &f_vec, vectorType &cEqs_vec, vectorType &cIneqs_vec)

template<typename VecT >
void	get_nonlinear_eq_constraints (const Model &model, VecT &values, Real scale, int offset=-1)

template<typename VecT >
void	get_nonlinear_eq_constraints (Model &model, const RealVector &curr_resp_vals, VecT &values, Real scale, int offset=0)

template<typename VecT >
void	get_nonlinear_ineq_constraints (const Model &model, VecT &values)

template<typename VecT >
void	get_nonlinear_bounds (Model &model, VecT &nonlin_ineq_lower, VecT &nonlin_ineq_upper, VecT &nonlin_eq_targets)
	Would like to combine the previous adapter with this one (based on APPSOptimizer and COLINOptimizer) and then see how much more generalization is needed to support other TPLs like JEGA. More...

bool	responses_id_compare (const Response &resp, const void *id)
	global comparison function for Response

std::istream &	operator>> (std::istream &s, Response &response)
	std::istream extraction operator for Response. Calls read(std::istream&).

std::ostream &	operator<< (std::ostream &s, const Response &response)
	std::ostream insertion operator for Response. Calls write(std::ostream&).

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, Response &response)
	MPIUnpackBuffer extraction operator for Response. Calls read(MPIUnpackBuffer&).

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const Response &response)
	MPIPackBuffer insertion operator for Response. Calls write(MPIPackBuffer&).

bool	operator!= (const Response &resp1, const Response &resp2)
	inequality operator for Response More...

void	set_model_gp_options (Model &model, const String &options_file)

bool	variables_id_compare (const Variables &vars, const void *id)
	global comparison function for Variables

std::istream &	operator>> (std::istream &s, Variables &vars)
	std::istream extraction operator for Variables.

std::ostream &	operator<< (std::ostream &s, const Variables &vars)
	std::ostream insertion operator for Variables.

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, Variables &vars)
	MPIUnpackBuffer extraction operator for Variables.

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const Variables &vars)
	MPIPackBuffer insertion operator for Variables.

bool	operator!= (const Variables &vars1, const Variables &vars2)
	inequality operator for Variables More...

template<typename OrdinalType , typename ScalarType1 , typename ScalarType2 , typename ScalarType3 , typename ScalarType4 >
void	write_ordered (std::ostream &s, const SizetArray &comp_totals, const Teuchos::SerialDenseVector< OrdinalType, ScalarType1 > &c_vector, const Teuchos::SerialDenseVector< OrdinalType, ScalarType2 > &di_vector, const Teuchos::SerialDenseVector< OrdinalType, ScalarType3 > &ds_vector, const Teuchos::SerialDenseVector< OrdinalType, ScalarType4 > &dr_vector)
	free function to write Variables data vectors in input spec ordering More...

template<typename OrdinalType , typename ScalarType1 , typename ScalarType2 , typename ScalarType3 , typename ScalarType4 >
void	write_ordered (std::ostream &s, const SizetArray &comp_totals, const Teuchos::SerialDenseVector< OrdinalType, ScalarType1 > &c_vector, const Teuchos::SerialDenseVector< OrdinalType, ScalarType2 > &di_vector, const boost::multi_array< ScalarType3, 1 > &ds_array, const Teuchos::SerialDenseVector< OrdinalType, ScalarType4 > &dr_vector)
	free function to write Variables data vectors in input spec ordering More...

template<typename ScalarType >
void	write_ordered (std::ostream &s, const SizetArray &comp_totals, const std::vector< ScalarType > &c_array, const std::vector< ScalarType > &di_array, const std::vector< ScalarType > &ds_array, const std::vector< ScalarType > &dr_array)
	free function to write Variables data vectors in input spec ordering

void	size_and_fill (const SharedVariablesData &svd, size_t array_size, VariablesArray &vars_array)
	Reinitialize var_array to contain array_size freshly constructed Variables, sharing provided SVD.

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const DataEnvironment &data)
	MPIPackBuffer insertion operator for DataEnvironment.

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, DataEnvironment &data)
	MPIUnpackBuffer extraction operator for DataEnvironment.

std::ostream &	operator<< (std::ostream &s, const DataEnvironment &data)
	std::ostream insertion operator for DataEnvironment

String	interface_enum_to_string (unsigned short interface_type)

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const DataInterface &data)
	MPIPackBuffer insertion operator for DataInterface.

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, DataInterface &data)
	MPIUnpackBuffer extraction operator for DataInterface.

std::ostream &	operator<< (std::ostream &s, const DataInterface &data)
	std::ostream insertion operator for DataInterface

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const DataMethod &data)
	MPIPackBuffer insertion operator for DataMethod.

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, DataMethod &data)
	MPIUnpackBuffer extraction operator for DataMethod.

std::ostream &	operator<< (std::ostream &s, const DataMethod &data)
	std::ostream insertion operator for DataMethod

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const DataModel &data)
	MPIPackBuffer insertion operator for DataModel.

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, DataModel &data)
	MPIUnpackBuffer extraction operator for DataModel.

std::ostream &	operator<< (std::ostream &s, const DataModel &data)
	std::ostream insertion operator for DataModel

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const DataResponses &data)
	MPIPackBuffer insertion operator for DataResponses.

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, DataResponses &data)
	MPIUnpackBuffer extraction operator for DataResponses.

std::ostream &	operator<< (std::ostream &s, const DataResponses &data)
	std::ostream insertion operator for DataResponses

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const DataVariables &data)
	MPIPackBuffer insertion operator for DataVariables.

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, DataVariables &data)
	MPIUnpackBuffer extraction operator for DataVariables.

std::ostream &	operator<< (std::ostream &s, const DataVariables &data)
	std::ostream insertion operator for DataVariables

int	dlsolver_option (Opt_Info *)

RealVector const *	continuous_lower_bounds (Optimizer1 *o)

RealVector const *	continuous_upper_bounds (Optimizer1 *o)

RealVector const *	nonlinear_ineq_constraint_lower_bounds (Optimizer1 *o)

RealVector const *	nonlinear_ineq_constraint_upper_bounds (Optimizer1 *o)

RealVector const *	nonlinear_eq_constraint_targets (Optimizer1 *o)

RealVector const *	linear_ineq_constraint_lower_bounds (Optimizer1 *o)

RealVector const *	linear_ineq_constraint_upper_bounds (Optimizer1 *o)

RealVector const *	linear_eq_constraint_targets (Optimizer1 *o)

RealMatrix const *	linear_ineq_constraint_coeffs (Optimizer1 *o)

RealMatrix const *	linear_eq_constraint_coeffs (Optimizer1 *o)

void	ComputeResponses (Optimizer1 o, int mode, int n, double x)

void	GetFuncs (Optimizer1 o, int m0, int m1, double f)

void	GetGrads (Optimizer1 o, int m0, int m1, int n, int is, int js, double g)

void	GetContVars (Optimizer1 o, int n, double x)

void	SetBestContVars (Optimizer1 o, int n, double x)

void	SetBestRespFns (Optimizer1 o, int n, double x)

void *	dl_constructor (Optimizer1 , Dakota_funcs , dl_core_run_t , dl_destructor_t )

static RealVector const *	continuous_lower_bounds1 (Optimizer1 *o)

static RealVector const *	continuous_upper_bounds1 (Optimizer1 *o)

static RealVector const *	nonlinear_ineq_constraint_lower_bounds1 (Optimizer1 *o)

static RealVector const *	nonlinear_ineq_constraint_upper_bounds1 (Optimizer1 *o)

static RealVector const *	nonlinear_eq_constraint_targets1 (Optimizer1 *o)

static RealVector const *	linear_ineq_constraint_lower_bounds1 (Optimizer1 *o)

static RealVector const *	linear_ineq_constraint_upper_bounds1 (Optimizer1 *o)

static RealVector const *	linear_eq_constraint_targets1 (Optimizer1 *o)

static RealMatrix const *	linear_eq_constraint_coeffs1 (Optimizer1 *o)

static RealMatrix const *	linear_ineq_constraint_coeffs1 (Optimizer1 *o)

static void	ComputeResponses1 (Optimizer1 o, int mode, int n, double x)

static void	GetFuncs1 (Optimizer1 o, int m0, int m1, double f)

static void	GetGrads1 (Optimizer1 o, int m0, int m1, int n, int is, int js, double g)

static void	GetContVars1 (Optimizer1 o, int n, double x)

static void	SetBestContVars1 (Optimizer1 o, int n, double x)

static void	SetBestDiscVars1 (Optimizer1 o, int n, int x)

static void	SetBestRespFns1 (Optimizer1 o, int n, double x)

static double	Get_Real1 (Optimizer1 o, const char name)

static int	Get_Int1 (Optimizer1 o, const char name)

static bool	Get_Bool1 (Optimizer1 o, const char name)

DOTOptimizer *	new_DOTOptimizer (ProblemDescDB &problem_db)

DOTOptimizer *	new_DOTOptimizer (Model &model)

DOTOptimizer *	new_DOTOptimizer (ProblemDescDB &problem_db, Model &model)

void	copy_field_data (const RealVector &fn_vals, RealMatrix &fn_grad, const RealSymMatrixArray &fn_hess, size_t offset, size_t num_fns, Response &response)

void	copy_field_data (const RealVector &fn_vals, RealMatrix &fn_grad, const RealSymMatrixArray &fn_hess, size_t offset, size_t num_fns, short total_asv, Response &response)

void	interpolate_simulation_field_data (const Response &sim_resp, const RealMatrix &exp_coords, size_t field_num, short total_asv, size_t interp_resp_offset, Response &interp_resp)

void	linear_interpolate_1d (const RealMatrix &build_pts, const RealVector &build_vals, const RealMatrix &build_grads, const RealSymMatrixArray &build_hessians, const RealMatrix &pred_pts, RealVector &pred_vals, RealMatrix &pred_grads, RealSymMatrixArray &pred_hessians)
	Returns the value of at 1D function f and its gradient and hessians (if available) at the points of vector pred_pts using linear interpolation. The vector build_pts specifies the coordinates of the underlying interval at which the values (build_vals) of the function f are known. The length of output pred_vals is equal to the length of pred_pts. This function assumes the build_pts is in ascending order.

void	symmetric_eigenvalue_decomposition (const RealSymMatrix &matrix, RealVector &eigenvalues, RealMatrix &eigenvectors)
	Computes the eigenvalues and, optionally, eigenvectors of a real symmetric matrix A. More...

template<typename O , typename T >
int	binary_search (T target, Teuchos::SerialDenseVector< O, T > &data)
	find the interval containing a target value. This function assumes the data is in ascending order.

Real	getdist (const RealVector &x1, const RealVector &x2)

Real	mindist (const RealVector &x, const RealMatrix &xset, int except)

Real	mindistindx (const RealVector &x, const RealMatrix &xset, const IntArray &indx)

Real	getRmax (const RealMatrix &xset)

int	start_grid_computing (char analysis_driver_script, char params_file, char *results_file)

int	stop_grid_computing ()

int	perform_analysis (char *iteration_num)

int	length (const StringMultiArrayConstView &vec)
	Return the length of a StringMultiArrayConstView.

H5::DataType	h5_file_dtype (const short &)
	Return the HDF5 datatype to store a short.

H5::DataType	h5_file_dtype (const int &)
	Return the HDF5 datatype to store a int.

H5::DataType	h5_file_dtype (const unsigned int &)

H5::DataType	h5_file_dtype (const unsigned long &)

H5::DataType	h5_file_dtype (const unsigned long long &)

H5::DataType	h5_file_dtype (const Real &)
	Return the HDF5 datatype to store a Real.

H5::DataType	h5_file_dtype (const char *)
	Return the HDF5 datatype to store a string.

H5::DataType	h5_file_dtype (const ResultsOutputType t)
	Overloads for ResultsOutputType (used when creating empty datasets)

H5::DataType	h5_file_dtype (const String &)
	Return the HDF5 datatype to store a string.

H5::DataType	h5_mem_dtype (const Real &)
	Return the HDF5 datatype to read a Real in memory.

H5::DataType	h5_mem_dtype (const short &)
	Return the HDF5 datatype to read a short in memory.

H5::DataType	h5_mem_dtype (const int &)
	Return the HDF5 datatype to read an int in memory.

H5::DataType	h5_mem_dtype (const unsigned int &)
	Return the HDF5 datatype to read an unsigned int in memory.

H5::DataType	h5_mem_dtype (const unsigned long &)
	Return the HDF5 datatype to read an unsigned long (maybe a size_t) in memory.

H5::DataType	h5_mem_dtype (const unsigned long long &)
	Return the HDF5 datatype to read an unsigned long long (maybe a size_t) in memory.

H5::DataType	h5_mem_dtype (const char *)
	Return the HDF5 datatype to read a string in memory.

H5::DataType	h5_mem_dtype (const String &)
	Return the HDF5 datatype to read a string in memory.

H5::DataType	h5_mem_dtype (const ResultsOutputType t)
	Overloads for ResultsOutputType (used when creating empty datasets)

template<typename T >
int	length (const std::vector< T > &vec)
	Return the length of seeral types.

template<typename T >
int	length (const Teuchos::SerialDenseVector< int, T > &vec)
	Return the length of an SDV.

template<typename T >
std::vector< const char * >	pointers_to_strings (const T &data)
	Return a vector of pointers to strings.

template<typename T >
string	asstring (const T &val)
	Creates a string from the argument val using an ostringstream. More...

	PACKBUF (int, MPI_INT) PACKBUF(u_int

MPI_UNSIGNED	PACKBUF (long, MPI_LONG) PACKBUF(u_long

MPI_UNSIGNED MPI_UNSIGNED_LONG	PACKBUF (long long, MPI_LONG_LONG) PACKBUF(unsigned long long

MPI_UNSIGNED MPI_UNSIGNED_LONG MPI_UNSIGNED_LONG_LONG	PACKBUF (short, MPI_SHORT) PACKBUF(u_short

MPI_UNSIGNED MPI_UNSIGNED_LONG MPI_UNSIGNED_LONG_LONG MPI_UNSIGNED_SHORT	PACKBUF (char, MPI_CHAR) PACKBUF(u_char

MPI_UNSIGNED MPI_UNSIGNED_LONG MPI_UNSIGNED_LONG_LONG MPI_UNSIGNED_SHORT MPI_UNSIGNED_CHAR	PACKBUF (double, MPI_DOUBLE) PACKBUF(float

	UNPACKBUF (int, MPI_INT) UNPACKBUF(u_int

MPI_UNSIGNED	UNPACKBUF (long, MPI_LONG) UNPACKBUF(u_long

MPI_UNSIGNED MPI_UNSIGNED_LONG	UNPACKBUF (long long, MPI_LONG_LONG) UNPACKBUF(unsigned long long

MPI_UNSIGNED MPI_UNSIGNED_LONG MPI_UNSIGNED_LONG_LONG	UNPACKBUF (short, MPI_SHORT) UNPACKBUF(u_short

MPI_UNSIGNED MPI_UNSIGNED_LONG MPI_UNSIGNED_LONG_LONG MPI_UNSIGNED_SHORT	UNPACKBUF (char, MPI_CHAR) UNPACKBUF(u_char

MPI_UNSIGNED MPI_UNSIGNED_LONG MPI_UNSIGNED_LONG_LONG MPI_UNSIGNED_SHORT MPI_UNSIGNED_CHAR	UNPACKBUF (double, MPI_DOUBLE) UNPACKBUF(float

	PACKSIZE (int, MPI_INT) PACKSIZE(u_int

MPI_UNSIGNED	PACKSIZE (long, MPI_LONG) PACKSIZE(u_long

MPI_UNSIGNED MPI_UNSIGNED_LONG	PACKSIZE (long long, MPI_LONG_LONG) PACKSIZE(unsigned long long

MPI_UNSIGNED MPI_UNSIGNED_LONG MPI_UNSIGNED_LONG_LONG	PACKSIZE (short, MPI_SHORT) PACKSIZE(u_short

MPI_UNSIGNED MPI_UNSIGNED_LONG MPI_UNSIGNED_LONG_LONG MPI_UNSIGNED_SHORT	PACKSIZE (char, MPI_CHAR) PACKSIZE(u_char

MPI_UNSIGNED MPI_UNSIGNED_LONG MPI_UNSIGNED_LONG_LONG MPI_UNSIGNED_SHORT MPI_UNSIGNED_CHAR	PACKSIZE (double, MPI_DOUBLE) PACKSIZE(float

MPI_UNSIGNED MPI_UNSIGNED_LONG MPI_UNSIGNED_LONG_LONG MPI_UNSIGNED_SHORT MPI_UNSIGNED_CHAR MPI_FLOAT int	MPIPackSize (const bool &data, const int num=1)
	return packed size of a bool

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const int &data)
	insert an int

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const u_int &data)
	insert a u_int

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const long &data)
	insert a long

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const u_long &data)
	insert a u_long

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const long long &data)
	insert a long long

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const unsigned long long &data)
	insert a unsigned long long

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const short &data)
	insert a short

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const u_short &data)
	insert a u_short

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const char &data)
	insert a char

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const u_char &data)
	insert a u_char

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const double &data)
	insert a double

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const float &data)
	insert a float

MPIPackBuffer &	operator<< (MPIPackBuffer &buff, const bool &data)
	insert a bool

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, int &data)
	extract an int

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, u_int &data)
	extract a u_int

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, long &data)
	extract a long

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, u_long &data)
	extract a u_long

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, long long &data)
	extract a long long

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, unsigned long long &data)
	extract an unsigned long long

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, short &data)
	extract a short

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, u_short &data)
	extract a u_short

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, char &data)
	extract a char

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, u_char &data)
	extract a u_char

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, double &data)
	extract a double

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, float &data)
	extract a float

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &buff, bool &data)
	extract a bool

int	MPIPackSize (const int &data, const int num=1)
	return packed size of an int

int	MPIPackSize (const u_int &data, const int num=1)
	return packed size of a u_int

int	MPIPackSize (const long &data, const int num=1)
	return packed size of a long

int	MPIPackSize (const u_long &data, const int num=1)
	return packed size of a u_long

int	MPIPackSize (const long long &data, const int num=1)
	return packed size of a long long

int	MPIPackSize (const unsigned long long &data, const int num=1)
	return packed size of an unsigned long long

int	MPIPackSize (const short &data, const int num=1)
	return packed size of a short

int	MPIPackSize (const u_short &data, const int num=1)
	return packed size of a u_short

int	MPIPackSize (const char &data, const int num=1)
	return packed size of a char

int	MPIPackSize (const u_char &data, const int num=1)
	return packed size of a u_char

int	MPIPackSize (const double &data, const int num=1)
	return packed size of a double

int	MPIPackSize (const float &data, const int num=1)
	return packed size of a float

int	nidr_parse (const char , FILE )

const char **	arg_list_adjust (const char , void )

int	not_executable (const char driver_name, const char tdir)

static void	scale_chk (StringArray &ST, RealVector &S, const char what, const char *univ)

static void	BuildLabels (StringArray sa, size_t nsa, size_t n1, size_t n2, const char stub)

static int	mixed_check (IntSet S, int n, IntArray iv, const char *what)

static void	mixed_check2 (size_t n, IntArray iv, const char what)

static int	wronglen (size_t n, RealVector V, const char what)

static int	wronglen (size_t n, IntVector V, const char what)

static void	Vcopyup (RealVector V, RealVector M, size_t i, size_t n)

static void	Set_rv (RealVector *V, double d, size_t n)

static void	Set_iv (IntVector *V, int d, size_t n)

static void	wrong_number (const char what, const char kind, size_t nsv, size_t m)

static void	too_small (const char *kind)

static void	not_div (const char *kind, size_t nsv, size_t m)

static void	suppressed (const char kind, int ndup, int ip, String sp, Real rp)

static void	bad_initial_ivalue (const char *kind, int val)

static void	bad_initial_svalue (const char *kind, String val)

static void	bad_initial_rvalue (const char *kind, Real val)

static void	Vgen_ContinuousDes (DataVariablesRep *dv, size_t offset)

static void	Vgen_DiscreteDesRange (DataVariablesRep *dv, size_t offset)

static void	Vgen_ContinuousState (DataVariablesRep *dv, size_t offset)

static void	Vgen_DiscreteStateRange (DataVariablesRep *dv, size_t offset)

static void	Vchk_NormalUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_NormalUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_LognormalUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_LognormalUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_UniformUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_UniformUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_LoguniformUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_LoguniformUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_TriangularUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_TriangularUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_ExponentialUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_ExponentialUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_BetaUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_BetaUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_GammaUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_GammaUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_GumbelUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_GumbelUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_FrechetUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_FrechetUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_WeibullUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_WeibullUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_HistogramBinUnc (DataVariablesRep dv, size_t offset, Var_Info vi)
	Check the histogram bin input data, normalize the counts and populate the histogramUncBinPairs map data structure; map keys are guaranteed unique since the abscissas must increase.

static void	Vgen_HistogramBinUnc (DataVariablesRep *dv, size_t offset)
	Infer lower/upper bounds for histogram and set initial variable values based on initial_point or moments, snapping to bounds as needed. (Histogram bin doesn't have lower/upper bounds specifcation)

static void	Vchk_PoissonUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_PoissonUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_BinomialUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_BinomialUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_NegBinomialUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_NegBinomialUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_GeometricUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_GeometricUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_HyperGeomUnc (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_HyperGeomUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_HistogramPtIntUnc (DataVariablesRep dv, size_t offset, Var_Info vi)
	Check the histogram point integer input data, normalize the counts, and populate DataVariables::histogramUncPointIntPairs; map keys are guaranteed unique since the abscissas must increase.

static void	Vgen_HistogramPtIntUnc (DataVariablesRep *dv, size_t offset)
	Use the integer-valued point histogram data to initialize the lower, upper, and initial values of the variables, using value closest to mean if no initial point.

static void	Vchk_HistogramPtStrUnc (DataVariablesRep dv, size_t offset, Var_Info vi)
	Check the histogram point string input data, normalize the counts, and populate DataVariables::histogramUncPointStrPairs; map keys are guaranteed unique since the abscissas must increase (lexicographically)

static void	Vgen_HistogramPtStrUnc (DataVariablesRep *dv, size_t offset)
	Use the string-valued point histogram data to initialize the lower, upper, and initial values of the variables, using index closest to mean index if no initial point.

static void	Vchk_HistogramPtRealUnc (DataVariablesRep dv, size_t offset, Var_Info vi)
	Check the histogram point integer real data, normalize the counts, and populate DataVariables::histogramUncPointRealPairs; map keys are guaranteed unique since the abscissas must increase.

static void	Vgen_HistogramPtRealUnc (DataVariablesRep *dv, size_t offset)
	Use the real-valued point histogram data to initialize the lower, upper, and initial values of the variables, using value closest to mean if no initial point.

static void	Vchk_ContinuousIntervalUnc (DataVariablesRep dv, size_t offset, Var_Info vi)
	Check the continuous interval uncertain input data and populate DataVariables::continuousIntervalUncBasicProbs; map keys (real intervals) are checked for uniqueness because we don't have a theoretically sound way to combine duplicate intervals.

static void	Vgen_ContinuousIntervalUnc (DataVariablesRep *dv, size_t offset)

static void	Vchk_DiscreteIntervalUnc (DataVariablesRep dv, size_t offset, Var_Info vi)
	Check the discrete interval uncertain input data and populate DataVariables::discreteIntervalUncBasicProbs; map keys (integer intervals) are checked for uniqueness because we don't have a theoretically sound way to combine duplicate intervals.

static void	Vgen_DiscreteIntervalUnc (DataVariablesRep *dv, size_t offset)

static bool	check_set_keys (size_t num_v, size_t ds_len, const char kind, IntArray input_nds, int &avg_num_ds)
	validate the number of set elements (values) given the number of variables and an optional apportionment with elements_per_variable; return the average number per variable if equally distributed

static void	Vchk_DIset (size_t num_v, const char kind, IntArray input_ndsi, IntVector *input_dsi, IntSetArray &dsi_all, IntVector &dsi_init_pt)
	check discrete sets of integers (design and state variables); error if a duplicate value is specified error if not ordered to prevent user confusion

static void	Vchk_DIset (size_t num_v, const char kind, IntArray input_ndsi, IntVector input_dsi, RealVector input_dsip, IntRealMapArray &dsi_vals_probs, IntVector &dsi_init_pt)
	check discrete sets of integers (uncertain variables); error if a duplicate value is specified error if not ordered to prevent user confusion

static void	Vchk_DSset (size_t num_v, const char kind, IntArray input_ndss, StringArray *input_dss, StringSetArray &dss_all, StringArray &dss_init_pt)

static void	Vchk_DSset (size_t num_v, const char kind, IntArray input_ndss, StringArray input_dss, RealVector input_dssp, StringRealMapArray &dss_vals_probs, StringArray &dss_init_pt)

static void	Vchk_DRset (size_t num_v, const char kind, IntArray input_ndsr, RealVector *input_dsr, RealSetArray &dsr_all, RealVector &dsr_init_pt)

static void	Vchk_DRset (size_t num_v, const char kind, IntArray input_ndsr, RealVector input_dsr, RealVector input_dsrp, RealRealMapArray &dsr_vals_probs, RealVector &dsr_init_pt)

static void	Vchk_Adjacency (size_t num_v, const char *kind, const IntArray &num_e, const IntVector &input_ddsa, RealMatrixArray &dda_all)

static bool	check_LUV_size (size_t num_v, IntVector &L, IntVector &U, IntVector &V, bool aggregate_LUV, size_t offset)

static bool	check_LUV_size (size_t num_v, StringArray &L, StringArray &U, StringArray &V, bool aggregate_LUV, size_t offset)

static bool	check_LUV_size (size_t num_v, RealVector &L, RealVector &U, RealVector &V, bool aggregate_LUV, size_t offset)

template<typename T >
T	midpoint (T a, T b)
	Compute the midpoint of floating-point or integer range [a, b] (a <= b), possibly indices, rounding toward a if needed. (Eventually replace with C++20 midpoint, which is more general.)

static size_t	mid_or_next_lower_index (const size_t num_inds)
	get the middle or left-of-middle index among indices [0,num_inds-1]

static void	Vgen_DIset (size_t num_v, IntSetArray &sets, IntVector &L, IntVector &U, IntVector &V, bool aggregate_LUV=false, size_t offset=0)

static void	Vgen_DSset (size_t num_v, StringSetArray &sets, StringArray &L, StringArray &U, StringArray &V, bool aggregate_LUV=false, size_t offset=0)
	generate lower, upper, and initial point for string-valued sets

static void	Vgen_DIset (size_t num_v, IntRealMapArray &vals_probs, IntVector &IP, IntVector &L, IntVector &U, IntVector &V, bool aggregate_LUV=false, size_t offset=0)

static void	Vgen_DRset (size_t num_v, RealSetArray &sets, RealVector &L, RealVector &U, RealVector &V, bool aggregate_LUV=false, size_t offset=0)

static void	Vgen_DRset (size_t num_v, RealRealMapArray &vals_probs, RealVector &IP, RealVector &L, RealVector &U, RealVector &V, bool aggregate_LUV=false, size_t offset=0)

static void	Vgen_DSset (size_t num_v, StringRealMapArray &vals_probs, StringArray &IP, StringArray &L, StringArray &U, StringArray &V, bool aggregate_LUV=false, size_t offset=0)

static void	Vchk_DiscreteDesSetInt (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_DiscreteDesSetInt (DataVariablesRep *dv, size_t offset)

static void	Vchk_DiscreteDesSetStr (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_DiscreteDesSetStr (DataVariablesRep *dv, size_t offset)

static void	Vchk_DiscreteDesSetReal (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_DiscreteDesSetReal (DataVariablesRep *dv, size_t offset)

static void	Vchk_DiscreteUncSetInt (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_DiscreteUncSetInt (DataVariablesRep *dv, size_t offset)

static void	Vchk_DiscreteUncSetStr (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_DiscreteUncSetStr (DataVariablesRep *dv, size_t offset)

static void	Vchk_DiscreteUncSetReal (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_DiscreteUncSetReal (DataVariablesRep *dv, size_t offset)

static void	Vchk_DiscreteStateSetInt (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_DiscreteStateSetInt (DataVariablesRep *dv, size_t offset)

static void	Vchk_DiscreteStateSetStr (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_DiscreteStateSetStr (DataVariablesRep *dv, size_t offset)

static void	Vchk_DiscreteStateSetReal (DataVariablesRep dv, size_t offset, Var_Info vi)

static void	Vgen_DiscreteStateSetReal (DataVariablesRep *dv, size_t offset)

static const char *	Var_Name (StringArray sa, char buf, size_t i)

static void	Var_RealBoundIPCheck (DataVariablesRep dv, Var_rcheck b)
	For real-valued variables: verify lengths of bounds and initial point, validate bounds and adjust initial point to bounds.

static void	Var_IntBoundIPCheck (DataVariablesRep dv, Var_icheck ib)
	For integer-valued variables: verify lengths of bounds and initial point, validate bounds and initial point against bounds.

static void	flatten_rva (RealVectorArray rva, RealVector *prv)

static void	flatten_iva (IntVectorArray iva, IntVector *piv)

static void	flatten_rsm (RealSymMatrix rsm, RealVector *prv)

static void	flatten_rsa (RealSetArray rsa, RealVector *prv)

static void	flatten_ssa (StringSetArray ssa, StringArray *psa)

static void	flatten_isa (IntSetArray isa, IntVector *piv)

static void	flatten_rrma_keys (RealRealMapArray rrma, RealVector *prv)

static void	flatten_rrma_values (RealRealMapArray rrma, RealVector *prv)

static void	flatten_irma_keys (IntRealMapArray irma, IntVector *piv)

static void	flatten_irma_values (IntRealMapArray irma, RealVector *prv)

static void	flatten_srma_keys (StringRealMapArray srma, StringArray *psa)

static void	flatten_srma_values (StringRealMapArray srma, RealVector *prv)

static void	flatten_real_intervals (const RealRealPairRealMapArray &rrprma, RealVector probs, RealVector lb, RealVector **ub)
	Flatten real-valued interval uncertain variable intervals and probabilities back into separate arrays.

static void	flatten_int_intervals (const IntIntPairRealMapArray &iiprma, RealVector probs, IntVector lb, IntVector **ub)
	Flatten integer-valued interval uncertain variable intervals and probabilities back into separate arrays.

static void	var_iulbl (const char keyname, Values val, VarLabel *vl)

static Iface_mp_Rlit	MP3 (failAction, recoveryFnVals, recover)

static Iface_mp_ilit	MP3 (failAction, retryLimit, retry)

static Iface_mp_lit	MP2 (failAction, abort)

static Iface_mp_lit	MP2 (failAction, continuation)

static Iface_mp_type	MP2s (analysisScheduling, MASTER_SCHEDULING)

static Iface_mp_type	MP2s (analysisScheduling, PEER_SCHEDULING)

static Iface_mp_type	MP2s (evalScheduling, MASTER_SCHEDULING)

static Iface_mp_type	MP2s (evalScheduling, PEER_DYNAMIC_SCHEDULING)

static Iface_mp_type	MP2s (evalScheduling, PEER_STATIC_SCHEDULING)

static Iface_mp_type	MP2s (asynchLocalEvalScheduling, DYNAMIC_SCHEDULING)

static Iface_mp_type	MP2s (asynchLocalEvalScheduling, STATIC_SCHEDULING)

static Iface_mp_utype	MP2s (interfaceType, TEST_INTERFACE)

static Iface_mp_utype	MP2s (interfaceType, FORK_INTERFACE)

static Iface_mp_utype	MP2s (interfaceType, GRID_INTERFACE)

static Iface_mp_utype	MP2s (interfaceType, LEGACY_PYTHON_INTERFACE)

static Iface_mp_utype	MP2s (interfaceType, MATLAB_INTERFACE)

static Iface_mp_utype	MP2s (interfaceType, PLUGIN_INTERFACE)

static Iface_mp_utype	MP2s (interfaceType, PYTHON_INTERFACE)

static Iface_mp_utype	MP2s (interfaceType, SCILAB_INTERFACE)

static Iface_mp_utype	MP2s (interfaceType, SYSTEM_INTERFACE)

static Iface_mp_utype	MP2s (resultsFileFormat, LABELED_RESULTS)

static String	MP_ (algebraicMappings)

static String	MP_ (idInterface)

static String	MP_ (inputFilter)

static String	MP_ (outputFilter)

static String	MP_ (parametersFile)

static String	MP_ (pluginLibraryPath)

static String	MP_ (resultsFile)

static String	MP_ (workDir)

static String2DArray	MP_ (analysisComponents)

static StringArray	MP_ (analysisDrivers)

static StringArray	MP_ (copyFiles)

static StringArray	MP_ (linkFiles)

static bool	MP_ (activeSetVectorFlag)

static bool	MP_ (allowExistingResultsFlag)

static bool	MP_ (apreproFlag)

static bool	MP_ (asynchFlag)

static bool	MP_ (batchEvalFlag)

static bool	MP_ (dirSave)

static bool	MP_ (dirTag)

static bool	MP_ (evalCacheFlag)

static bool	MP_ (fileSaveFlag)

static bool	MP_ (fileTagFlag)

static bool	MP_ (nearbyEvalCacheFlag)

static bool	MP_ (numpyFlag)

static bool	MP_ (restartFileFlag)

static bool	MP_ (templateReplace)

static bool	MP_ (useWorkdir)

static bool	MP_ (verbatimFlag)

static int	MP_ (analysisServers)

static int	MP_ (asynchLocalAnalysisConcurrency)

static int	MP_ (asynchLocalEvalConcurrency)

static int	MP_ (evalServers)

static int	MP_ (procsPerAnalysis)

static int	MP_ (procsPerEval)

static Real	MP_ (nearbyEvalCacheTol)

static IntVector	MP_ (primeBase)

static IntVector	MP_ (refineSamples)

static IntVector	MP_ (sequenceLeap)

static IntVector	MP_ (sequenceStart)

static IntVector	MP_ (stepsPerVariable)

static Method_mp_ilit2	MP3 (replacementType, numberRetained, chc)

static Method_mp_ilit2	MP3 (replacementType, numberRetained, elitist)

static Method_mp_ilit2	MP3 (replacementType, numberRetained, random)

static Method_mp_ilit2z	MP3 (crossoverType, numCrossPoints, multi_point_binary)

static Method_mp_ilit2z	MP3 (crossoverType, numCrossPoints, multi_point_parameterized_binary)

static Method_mp_ilit2z	MP3 (crossoverType, numCrossPoints, multi_point_real)

static Method_mp_lit	MP2 (batchSelectionType, naive)

static Method_mp_lit	MP2 (batchSelectionType, distance)

static Method_mp_lit	MP2 (batchSelectionType, topology)

static Method_mp_lit	MP2 (batchSelectionType, cl)

static Method_mp_lit	MP2 (boxDivision, all_dimensions)

static Method_mp_lit	MP2 (boxDivision, major_dimension)

static Method_mp_lit	MP2 (convergenceType, average_fitness_tracker)

static Method_mp_lit	MP2 (convergenceType, best_fitness_tracker)

static Method_mp_lit	MP2 (convergenceType, metric_tracker)

static Method_mp_lit	MP2 (crossoverType, blend)

static Method_mp_lit	MP2 (crossoverType, two_point)

static Method_mp_lit	MP2 (crossoverType, uniform)

static Method_mp_lit	MP2 (dataDistCovInputType, diagonal)

static Method_mp_lit	MP2 (dataDistCovInputType, matrix)

static Method_mp_lit	MP2 (exploratoryMoves, adaptive)

static Method_mp_lit	MP2 (exploratoryMoves, multi_step)

static Method_mp_lit	MP2 (exploratoryMoves, simple)

static Method_mp_lit	MP2 (fitnessType, domination_count)

static Method_mp_lit	MP2 (fitnessType, layer_rank)

static Method_mp_lit	MP2 (fitnessType, linear_rank)

static Method_mp_lit	MP2 (fitnessType, merit_function)

static Method_mp_lit	MP2 (fitnessType, proportional)

static Method_mp_lit	MP2 (fitnessMetricType, predicted_variance)

static Method_mp_lit	MP2 (fitnessMetricType, distance)

static Method_mp_lit	MP2 (fitnessMetricType, gradient)

static Method_mp_lit	MP2 (initializationType, random)

static Method_mp_lit	MP2 (initializationType, unique_random)

static Method_mp_lit	MP2 (lipschitzType, global)

static Method_mp_lit	MP2 (lipschitzType, local)

static Method_mp_lit	MP2 (meritFunction, merit_max)

static Method_mp_lit	MP2 (meritFunction, merit_max_smooth)

static Method_mp_lit	MP2 (meritFunction, merit1)

static Method_mp_lit	MP2 (meritFunction, merit1_smooth)

static Method_mp_lit	MP2 (meritFunction, merit2)

static Method_mp_lit	MP2 (meritFunction, merit2_smooth)

static Method_mp_lit	MP2 (meritFunction, merit2_squared)

static Method_mp_lit	MP2 (mcmcType, adaptive_metropolis)

static Method_mp_lit	MP2 (mcmcType, delayed_rejection)

static Method_mp_lit	MP2 (mcmcType, dram)

static Method_mp_lit	MP2 (mcmcType, metropolis_hastings)

static Method_mp_lit	MP2 (mcmcType, multilevel)

static Method_mp_lit	MP2 (modelDiscrepancyType, global_kriging)

static Method_mp_lit	MP2 (modelDiscrepancyType, global_polynomial)

static Method_mp_lit	MP2 (mutationType, bit_random)

static Method_mp_lit	MP2 (mutationType, offset_cauchy)

static Method_mp_lit	MP2 (mutationType, offset_normal)

static Method_mp_lit	MP2 (mutationType, offset_uniform)

static Method_mp_lit	MP2 (mutationType, replace_uniform)

static Method_mp_lit	MP2 (patternBasis, coordinate)

static Method_mp_lit	MP2 (patternBasis, simplex)

static Method_mp_lit	MP2 (pointReuse, all)

static Method_mp_lit	MP2 (proposalCovInputType, diagonal)

static Method_mp_lit	MP2 (proposalCovInputType, matrix)

static Method_mp_lit	MP2 (proposalCovType, derivatives)

static Method_mp_lit	MP2 (proposalCovType, prior)

static Method_mp_lit	MP2 (proposalCovType, user)

static Method_mp_lit	MP2 (reliabilityIntegration, first_order)

static Method_mp_lit	MP2 (reliabilityIntegration, second_order)

static Method_mp_lit	MP2 (replacementType, elitist)

static Method_mp_lit	MP2 (replacementType, favor_feasible)

static Method_mp_lit	MP2 (replacementType, roulette_wheel)

static Method_mp_lit	MP2 (replacementType, unique_roulette_wheel)

static Method_mp_lit	MP2 (rngName, mt19937)

static Method_mp_lit	MP2 (rngName, rnum2)

static Method_mp_lit	MP2 (searchMethod, gradient_based_line_search)

static Method_mp_lit	MP2 (searchMethod, tr_pds)

static Method_mp_lit	MP2 (searchMethod, trust_region)

static Method_mp_lit	MP2 (searchMethod, value_based_line_search)

static Method_mp_lit	MP2 (trialType, grid)

static Method_mp_lit	MP2 (trialType, halton)

static Method_mp_lit	MP2 (trialType, random)

static Method_mp_lit	MP2 (useSurrogate, inform_search)

static Method_mp_lit	MP2 (useSurrogate, optimize)

static Method_mp_litc	MP3 (crossoverType, crossoverRate, shuffle_random)

static Method_mp_litc	MP3 (crossoverType, crossoverRate, null_crossover)

static Method_mp_litc	MP3 (mutationType, mutationRate, null_mutation)

static Method_mp_litc	MP3 (mutationType, mutationRate, offset_cauchy)

static Method_mp_litc	MP3 (mutationType, mutationRate, offset_normal)

static Method_mp_litc	MP3 (mutationType, mutationRate, offset_uniform)

static Method_mp_litc	MP3 (replacementType, fitnessLimit, below_limit)

static Method_mp_litrv	MP3 (nichingType, nicheVector, distance)

static Method_mp_litrv	MP3 (nichingType, nicheVector, max_designs)

static Method_mp_litrv	MP3 (nichingType, nicheVector, radial)

static Method_mp_litrv	MP3 (postProcessorType, distanceVector, distance_postprocessor)

static Method_mp_slit2	MP3 (initializationType, flatFile, flat_file)

static Method_mp_utype_lit	MP3s (methodName, dlDetails, DL_SOLVER)

static Real	MP_ (absConvTol)

static Real	MP_ (centeringParam)

static Real	MP_ (collocationRatio)

static Real	MP_ (collocRatioTermsOrder)

static Real	MP_ (constraintPenalty)

static Real	MP_ (constrPenalty)

static Real	MP_ (constraintTolerance)

static Real	MP_ (contractFactor)

static Real	MP_ (contractStepLength)

static Real	MP_ (convergenceTolerance)

static Real	MP_ (crossoverRate)

static Real	MP_ (falseConvTol)

static Real	MP_ (functionPrecision)

static Real	MP_ (globalBalanceParam)

static Real	MP_ (gradientTolerance)

static Real	MP_ (hybridLSProb)

static Real	MP_ (grThreshold)

static Real	MP_ (initDelta)

static Real	MP_ (initMeshSize)

static Real	MP_ (initStepLength)

static Real	MP_ (initTRRadius)

static Real	MP_ (lineSearchTolerance)

static Real	MP_ (localBalanceParam)

static Real	MP_ (maxBoxSize)

static Real	MP_ (maxStep)

static Real	MP_ (minBoxSize)

static Real	MP_ (minMeshSize)

static Real	MP_ (multilevEstimatorRate)

static Real	MP_ (mutationRate)

static Real	MP_ (mutationScale)

static Real	MP_ (percentVarianceExplained)

static Real	MP_ (priorPropCovMult)

static Real	MP_ (refinementRate)

static Real	MP_ (regressionL2Penalty)

static Real	MP_ (shrinkagePercent)

static Real	MP_ (singConvTol)

static Real	MP_ (singRadius)

static Real	MP_ (smoothFactor)

static Real	MP_ (solnTarget)

static Real	MP_ (solverRoundingTol)

static Real	MP_ (solverTol)

static Real	MP_ (statsRoundingTol)

static Real	MP_ (stepLenToBoundary)

static Real	MP_ (threshDelta)

static Real	MP_ (threshStepLength)

static Real	MP_ (trustRegionContract)

static Real	MP_ (trustRegionContractTrigger)

static Real	MP_ (trustRegionExpand)

static Real	MP_ (trustRegionExpandTrigger)

static Real	MP_ (trustRegionMinSize)

static Real	MP_ (vbdDropTolerance)

static Real	MP_ (volBoxSize)

static Real	MP_ (vns)

static Real	MP_ (wilksConfidenceLevel)

static Real	MP_ (tiCoverage)

static Real	MP_ (tiConfidenceLevel)

static Real	MP_ (xConvTol)

static RealVector	MP_ (anisoDimPref)

static RealVector	MP_ (concurrentParameterSets)

static RealVector	MP_ (dataDistCovariance)

static RealVector	MP_ (dataDistMeans)

static RealVector	MP_ (finalPoint)

static RealVector	MP_ (hyperPriorAlphas)

static RealVector	MP_ (hyperPriorBetas)

static RealVector	MP_ (listOfPoints)

static RealVector	MP_ (predictionConfigList)

static RealVector	MP_ (proposalCovData)

static RealVector	MP_ (regressionNoiseTol)

static RealVector	MP_ (scalarizationRespCoeffs)

static RealVector	MP_ (stepVector)

static RealVector	MP_ (trustRegionInitSize)

static RealVectorArray	MP_ (genReliabilityLevels)

static RealVectorArray	MP_ (probabilityLevels)

static RealVectorArray	MP_ (reliabilityLevels)

static RealVectorArray	MP_ (responseLevels)

static unsigned short	MP_ (adaptedBasisAdvancements)

static unsigned short	MP_ (cubIntOrder)

static unsigned short	MP_ (dagDepthLimit)

static unsigned short	MP_ (expansionOrder)

static unsigned short	MP_ (kickOrder)

static unsigned short	MP_ (maxCVOrderCandidates)

static unsigned short	MP_ (maxOrder)

static unsigned short	MP_ (quadratureOrder)

static unsigned short	MP_ (softConvLimit)

static unsigned short	MP_ (sparseGridLevel)

static unsigned short	MP_ (startOrder)

static unsigned short	MP_ (vbdOrder)

static unsigned short	MP_ (wilksOrder)

static SizetArray	MP_ (collocationPointsSeq)

static SizetArray	MP_ (expansionSamplesSeq)

static SizetArray	MP_ (pilotSamples)

static SizetArray	MP_ (randomSeedSeq)

static SizetArray	MP_ (startRankSeq)

static UShortArray	MP_ (expansionOrderSeq)

static UShortArray	MP_ (quadratureOrderSeq)

static UShortArray	MP_ (sparseGridLevelSeq)

static UShortArray	MP_ (startOrderSeq)

static UShortArray	MP_ (tensorGridOrder)

static UShortArray	MP_ (varPartitions)

static String	MP_ (advancedOptionsFilename)

static String	MP_ (betaSolverName)

static String	MP_ (dataDistFile)

static String	MP_ (displayFormat)

static String	MP_ (exportApproxPtsFile)

static String	MP_ (exportCorrModelFile)

static String	MP_ (exportCorrVarFile)

static String	MP_ (exportDiscrepFile)

static String	MP_ (exportExpansionFile)

static String	MP_ (exportMCMCPtsFile)

static String	MP_ (historyFile)

static String	MP_ (hybridGlobalMethodName)

static String	MP_ (hybridGlobalMethodPointer)

static String	MP_ (hybridGlobalModelPointer)

static String	MP_ (hybridLocalMethodName)

static String	MP_ (hybridLocalMethodPointer)

static String	MP_ (hybridLocalModelPointer)

static String	MP_ (idMethod)

static String	MP_ (importApproxPtsFile)

static String	MP_ (importBuildPtsFile)

static String	MP_ (importCandPtsFile)

static String	MP_ (importExpansionFile)

static String	MP_ (importPredConfigs)

static String	MP_ (logFile)

static String	MP_ (lowFidModelPointer)

static String	MP_ (modelExportPrefix)

static String	MP_ (modelPointer)

static String	MP_ (posteriorDensityExportFilename)

static String	MP_ (posteriorSamplesExportFilename)

static String	MP_ (posteriorSamplesImportFilename)

static String	MP_ (proposalCovFile)

static String	MP_ (pstudyFilename)

static String	MP_ (subMethodName)

static String	MP_ (subMethodPointer)

static String	MP_ (subModelPointer)

static StringArray	MP_ (hybridMethodNames)

static StringArray	MP_ (hybridMethodPointers)

static StringArray	MP_ (hybridModelPointers)

static StringArray	MP_ (miscOptions)

static bool	MP_ (adaptExpDesign)

static bool	MP_ (adaptOrder)

static bool	MP_ (adaptPosteriorRefine)

static bool	MP_ (adaptRank)

static bool	MP_ (backfillFlag)

static bool	MP_ (calModelDiscrepancy)

static bool	MP_ (chainDiagnostics)

static bool	MP_ (chainDiagnosticsCI)

static bool	MP_ (constantPenalty)

static bool	MP_ (crossValidation)

static bool	MP_ (crossValidNoiseOnly)

static bool	MP_ (dOptimal)

static bool	MP_ (evaluatePosteriorDensity)

static bool	MP_ (expansionFlag)

static bool	MP_ (exportSampleSeqFlag)

static bool	MP_ (exportSurrogate)

static bool	MP_ (fixedSeedFlag)

static bool	MP_ (fixedSequenceFlag)

static bool	MP_ (generatePosteriorSamples)

static bool	MP_ (gpmsaNormalize)

static bool	MP_ (importApproxActive)

static bool	MP_ (importBuildActive)

static bool	MP_ (latinizeFlag)

static bool	MP_ (logitTransform)

static bool	MP_ (mainEffectsFlag)

static bool	MP_ (methodScaling)

static bool	MP_ (methodUseDerivsFlag)

static bool	MP_ (modelEvidence)

static bool	MP_ (modelEvidLaplace)

static bool	MP_ (modelEvidMC)

static bool	MP_ (mutualInfoKSG2)

static bool	MP_ (mutationAdaptive)

static bool	MP_ (normalizedCoeffs)

static bool	MP_ (pcaFlag)

static bool	MP_ (posteriorStatsKL)

static bool	MP_ (posteriorStatsKDE)

static bool	MP_ (posteriorStatsMutual)

static bool	MP_ (printPopFlag)

static bool	MP_ (pstudyFileActive)

static bool	MP_ (randomizeOrderFlag)

static bool	MP_ (regressDiag)

static bool	MP_ (relativeConvMetric)

static bool	MP_ (respScalingFlag)

static bool	MP_ (showAllEval)

static bool	MP_ (showMiscOptions)

static bool	MP_ (speculativeFlag)

static bool	MP_ (standardizedSpace)

static bool	MP_ (stdRegressionCoeffs)

static bool	MP_ (toleranceIntervalsFlag)

static bool	MP_ (surrBasedGlobalReplacePts)

static bool	MP_ (surrBasedLocalLayerBypass)

static bool	MP_ (tensorGridFlag)

static bool	MP_ (truthPilotConstraint)

static bool	MP_ (useTargetVarianceOptimizationFlag)

static bool	MP_ (vbdFlag)

static bool	MP_ (volQualityFlag)

static bool	MP_ (wilksFlag)

static short	MP_ (polynomialOrder)

static int	MP_ (batchSize)

static int	MP_ (batchSizeExplore)

static int	MP_ (buildSamples)

static int	MP_ (burnInSamples)

static int	MP_ (chainSamples)

static int	MP_ (concurrentRandomJobs)

static int	MP_ (contractAfterFail)

static int	MP_ (covarianceType)

static int	MP_ (crossoverChainPairs)

static int	MP_ (emulatorOrder)

static int	MP_ (expandAfterSuccess)

static int	MP_ (evidenceSamples)

static int	MP_ (iteratorServers)

static int	MP_ (jumpStep)

static int	MP_ (maxCrossIterations)

static int	MP_ (maxHifiEvals)

static int	MP_ (mutationRange)

static int	MP_ (neighborOrder)

static int	MP_ (newSolnsGenerated)

static int	MP_ (numChains)

static int	MP_ (numCR)

static int	MP_ (numSamples)

static int	MP_ (numSteps)

static int	MP_ (numSymbols)

static int	MP_ (numTrials)

static int	MP_ (populationSize)

static int	MP_ (procsPerIterator)

static int	MP_ (proposalCovUpdatePeriod)

static int	MP_ (numPushforwardSamples)

static int	MP_ (randomSeed)

static int	MP_ (samplesOnEmulator)

static int	MP_ (searchSchemeSize)

static int	MP_ (subSamplingPeriod)

static int	MP_ (totalPatternSize)

static int	MP_ (verifyLevel)

static size_t	MP_ (collocationPoints)

static size_t	MP_ (expansionSamples)

static size_t	MP_ (kickRank)

static size_t	MP_ (maxCVRankCandidates)

static size_t	MP_ (maxFunctionEvals)

static size_t	MP_ (maxIterations)

static size_t	MP_ (maxRank)

static size_t	MP_ (maxRefineIterations)

static size_t	MP_ (maxSolverIterations)

static size_t	MP_ (numCandidateDesigns)

static size_t	MP_ (numCandidates)

static size_t	MP_ (numDesigns)

static size_t	MP_ (numFinalSolutions)

static size_t	MP_ (numGenerations)

static size_t	MP_ (numOffspring)

static size_t	MP_ (numParents)

static size_t	MP_ (numPredConfigs)

static size_t	MP_ (startRank)

static Method_mp_type	MP2s (allocationTarget, TARGET_MEAN)

static Method_mp_type	MP2s (allocationTarget, TARGET_SCALARIZATION)

static Method_mp_type	MP2s (allocationTarget, TARGET_SIGMA)

static Method_mp_type	MP2s (allocationTarget, TARGET_VARIANCE)

static Method_mp_type	MP2s (c3AdvanceType, MAX_ORDER_ADVANCEMENT)

static Method_mp_type	MP2s (c3AdvanceType, MAX_RANK_ADVANCEMENT)

static Method_mp_type	MP2s (c3AdvanceType, MAX_RANK_ORDER_ADVANCEMENT)

static Method_mp_type	MP2s (c3AdvanceType, START_ORDER_ADVANCEMENT)

static Method_mp_type	MP2s (c3AdvanceType, START_RANK_ADVANCEMENT)

static Method_mp_type	MP2s (convergenceToleranceTarget, CONVERGENCE_TOLERANCE_TARGET_COST_CONSTRAINT)

static Method_mp_type	MP2s (convergenceToleranceTarget, CONVERGENCE_TOLERANCE_TARGET_VARIANCE_CONSTRAINT)

static Method_mp_type	MP2s (convergenceToleranceType, CONVERGENCE_TOLERANCE_TYPE_ABSOLUTE)

static Method_mp_type	MP2s (convergenceToleranceType, CONVERGENCE_TOLERANCE_TYPE_RELATIVE)

static Method_mp_type	MP2s (covarianceControl, DIAGONAL_COVARIANCE)

static Method_mp_type	MP2s (covarianceControl, FULL_COVARIANCE)

static Method_mp_type	MP2s (dagRecursionType, FULL_GRAPH_RECURSION)

static Method_mp_type	MP2s (dagRecursionType, KL_GRAPH_RECURSION)

static Method_mp_type	MP2s (dagRecursionType, PARTIAL_GRAPH_RECURSION)

static Method_mp_type	MP2s (distributionType, COMPLEMENTARY)

static Method_mp_type	MP2s (distributionType, CUMULATIVE)

static Method_mp_type	MP2s (emulatorType, EXPGP_EMULATOR)

static Method_mp_type	MP2s (emulatorType, GP_EMULATOR)

static Method_mp_type	MP2s (emulatorType, KRIGING_EMULATOR)

static Method_mp_type	MP2s (emulatorType, MF_PCE_EMULATOR)

static Method_mp_type	MP2s (emulatorType, MF_SC_EMULATOR)

static Method_mp_type	MP2s (emulatorType, ML_PCE_EMULATOR)

static Method_mp_type	MP2s (emulatorType, PCE_EMULATOR)

static Method_mp_type	MP2s (emulatorType, SC_EMULATOR)

static Method_mp_type	MP2s (emulatorType, VPS_EMULATOR)

static Method_mp_type	MP2s (ensembleSampSolnMode, OFFLINE_PILOT)

static Method_mp_type	MP2s (ensembleSampSolnMode, ONLINE_PILOT)

static Method_mp_type	MP2s (ensembleSampSolnMode, PILOT_PROJECTION)

static Method_mp_type	MP2s (evalSynchronize, BLOCKING_SYNCHRONIZATION)

static Method_mp_type	MP2s (evalSynchronize, NONBLOCKING_SYNCHRONIZATION)

static Method_mp_type	MP2p (expansionBasisType, ADAPTED_BASIS_EXPANDING_FRONT)

static Method_mp_type	MP2p (expansionBasisType, ADAPTED_BASIS_GENERALIZED)

static Method_mp_type	MP2p (expansionBasisType, HIERARCHICAL_INTERPOLANT)

static Method_mp_type	MP2p (expansionBasisType, NODAL_INTERPOLANT)

static Method_mp_type	MP2p (expansionBasisType, TENSOR_PRODUCT_BASIS)

static Method_mp_type	MP2p (expansionBasisType, TOTAL_ORDER_BASIS)

static Method_mp_type	MP2s (expansionType, ASKEY_U)

static Method_mp_type	MP2s (expansionType, STD_NORMAL_U)

static Method_mp_type	MP2p (finalMomentsType, CENTRAL_MOMENTS)

static Method_mp_type	MP2p (finalMomentsType, NO_MOMENTS)

static Method_mp_type	MP2p (finalMomentsType, STANDARD_MOMENTS)

static Method_mp_type	MP2s (finalStatsType, ESTIMATOR_PERFORMANCE)

static Method_mp_type	MP2s (finalStatsType, NO_FINAL_STATS)

static Method_mp_type	MP2s (finalStatsType, QOI_STATISTICS)

static Method_mp_type	MP2p (growthOverride, RESTRICTED)

static Method_mp_type	MP2p (growthOverride, UNRESTRICTED)

static Method_mp_type	MP2s (iteratorScheduling, MASTER_SCHEDULING)

static Method_mp_type	MP2s (iteratorScheduling, PEER_SCHEDULING)

static Method_mp_type	MP2s (lsRegressionType, EQ_CON_LS)

static Method_mp_type	MP2s (lsRegressionType, SVD_LS)

static Method_mp_type	MP2o (meritFn, ArgaezTapia)

static Method_mp_type	MP2o (meritFn, NormFmu)

static Method_mp_type	MP2o (meritFn, VanShanno)

static Method_mp_type	MP2s (methodOutput, DEBUG_OUTPUT)

static Method_mp_type	MP2s (methodOutput, NORMAL_OUTPUT)

static Method_mp_type	MP2s (methodOutput, QUIET_OUTPUT)

static Method_mp_type	MP2s (methodOutput, SILENT_OUTPUT)

static Method_mp_type	MP2s (methodOutput, VERBOSE_OUTPUT)

static Method_mp_type	MP2s (multilevAllocControl, ESTIMATOR_VARIANCE)

static Method_mp_type	MP2s (multilevAllocControl, GREEDY_REFINEMENT)

static Method_mp_type	MP2s (multilevAllocControl, RANK_SAMPLING)

static Method_mp_type	MP2s (multilevAllocControl, RIP_SAMPLING)

static Method_mp_type	MP2s (multilevDiscrepEmulation, DISTINCT_EMULATION)

static Method_mp_type	MP2s (multilevDiscrepEmulation, RECURSIVE_EMULATION)

static Method_mp_type	MP2p (nestingOverride, NESTED)

static Method_mp_type	MP2p (nestingOverride, NON_NESTED)

static Method_mp_type	MP2s (qoiAggregation, QOI_AGGREGATION_MAX)

static Method_mp_type	MP2s (qoiAggregation, QOI_AGGREGATION_SUM)

static Method_mp_type	MP2p (refinementControl, DIMENSION_ADAPTIVE_CONTROL_GENERALIZED)

static Method_mp_type	MP2p (refinementControl, DIMENSION_ADAPTIVE_CONTROL_DECAY)

static Method_mp_type	MP2p (refinementControl, DIMENSION_ADAPTIVE_CONTROL_SOBOL)

static Method_mp_type	MP2p (refinementControl, LOCAL_ADAPTIVE_CONTROL)

static Method_mp_type	MP2p (refinementControl, UNIFORM_CONTROL)

static Method_mp_type	MP2p (refinementType, P_REFINEMENT)

static Method_mp_type	MP2p (refinementType, H_REFINEMENT)

static Method_mp_type	MP2p (regressionType, BASIS_PURSUIT)

static Method_mp_type	MP2p (regressionType, BASIS_PURSUIT_DENOISING)

static Method_mp_type	MP2p (regressionType, DEFAULT_LEAST_SQ_REGRESSION)

static Method_mp_type	MP2p (regressionType, LASSO_REGRESSION)

static Method_mp_type	MP2p (regressionType, LEAST_ANGLE_REGRESSION)

static Method_mp_type	MP2p (regressionType, ORTHOG_LEAST_INTERPOLATION)

static Method_mp_type	MP2p (regressionType, ORTHOG_MATCH_PURSUIT)

static Method_mp_type	MP2s (regressionType, FT_LS)

static Method_mp_type	MP2s (regressionType, FT_RLS2)

static Method_mp_type	MP2s (responseLevelTarget, GEN_RELIABILITIES)

static Method_mp_type	MP2s (responseLevelTarget, PROBABILITIES)

static Method_mp_type	MP2s (responseLevelTarget, RELIABILITIES)

static Method_mp_type	MP2s (responseLevelTargetReduce, SYSTEM_PARALLEL)

static Method_mp_type	MP2s (responseLevelTargetReduce, SYSTEM_SERIES)

static Method_mp_type	MP2p (statsMetricMode, ACTIVE_EXPANSION_STATS)

static Method_mp_type	MP2p (statsMetricMode, COMBINED_EXPANSION_STATS)

static Method_mp_type	MP2s (surrBasedLocalAcceptLogic, FILTER)

static Method_mp_type	MP2s (surrBasedLocalAcceptLogic, TR_RATIO)

static Method_mp_type	MP2s (surrBasedLocalConstrRelax, HOMOTOPY)

static Method_mp_type	MP2s (surrBasedLocalMeritFn, ADAPTIVE_PENALTY_MERIT)

static Method_mp_type	MP2s (surrBasedLocalMeritFn, AUGMENTED_LAGRANGIAN_MERIT)

static Method_mp_type	MP2s (surrBasedLocalMeritFn, LAGRANGIAN_MERIT)

static Method_mp_type	MP2s (surrBasedLocalMeritFn, PENALTY_MERIT)

static Method_mp_type	MP2s (surrBasedLocalSubProbCon, LINEARIZED_CONSTRAINTS)

static Method_mp_type	MP2s (surrBasedLocalSubProbCon, NO_CONSTRAINTS)

static Method_mp_type	MP2s (surrBasedLocalSubProbCon, ORIGINAL_CONSTRAINTS)

static Method_mp_type	MP2s (surrBasedLocalSubProbObj, AUGMENTED_LAGRANGIAN_OBJECTIVE)

static Method_mp_type	MP2s (surrBasedLocalSubProbObj, LAGRANGIAN_OBJECTIVE)

static Method_mp_type	MP2s (surrBasedLocalSubProbObj, ORIGINAL_PRIMARY)

static Method_mp_type	MP2s (surrBasedLocalSubProbObj, SINGLE_OBJECTIVE)

static Method_mp_type	MP2s (wilksSidedInterval, ONE_SIDED_LOWER)

static Method_mp_type	MP2s (wilksSidedInterval, ONE_SIDED_UPPER)

static Method_mp_type	MP2s (wilksSidedInterval, TWO_SIDED)

static Method_mp_utype	MP2s (calibrateErrorMode, CALIBRATE_ONE)

static Method_mp_utype	MP2s (calibrateErrorMode, CALIBRATE_PER_EXPER)

static Method_mp_utype	MP2s (calibrateErrorMode, CALIBRATE_PER_RESP)

static Method_mp_utype	MP2s (calibrateErrorMode, CALIBRATE_BOTH)

static Method_mp_utype	MP2s (exportApproxFormat, TABULAR_NONE)

static Method_mp_utype	MP2s (exportApproxFormat, TABULAR_HEADER)

static Method_mp_utype	MP2s (exportApproxFormat, TABULAR_EVAL_ID)

static Method_mp_utype	MP2s (exportApproxFormat, TABULAR_IFACE_ID)

static Method_mp_utype	MP2s (exportApproxFormat, TABULAR_ANNOTATED)

static Method_mp_utype	MP2s (exportCorrModelFormat, TABULAR_NONE)

static Method_mp_utype	MP2s (exportCorrModelFormat, TABULAR_HEADER)

static Method_mp_utype	MP2s (exportCorrModelFormat, TABULAR_EVAL_ID)

static Method_mp_utype	MP2s (exportCorrModelFormat, TABULAR_IFACE_ID)

static Method_mp_utype	MP2s (exportCorrModelFormat, TABULAR_ANNOTATED)

static Method_mp_utype	MP2s (exportCorrVarFormat, TABULAR_NONE)

static Method_mp_utype	MP2s (exportCorrVarFormat, TABULAR_HEADER)

static Method_mp_utype	MP2s (exportCorrVarFormat, TABULAR_EVAL_ID)

static Method_mp_utype	MP2s (exportCorrVarFormat, TABULAR_IFACE_ID)

static Method_mp_utype	MP2s (exportCorrVarFormat, TABULAR_ANNOTATED)

static Method_mp_utype	MP2s (exportDiscrepFormat, TABULAR_NONE)

static Method_mp_utype	MP2s (exportDiscrepFormat, TABULAR_HEADER)

static Method_mp_utype	MP2s (exportDiscrepFormat, TABULAR_EVAL_ID)

static Method_mp_utype	MP2s (exportDiscrepFormat, TABULAR_IFACE_ID)

static Method_mp_utype	MP2s (exportDiscrepFormat, TABULAR_ANNOTATED)

static Method_mp_utype	MP2s (exportSamplesFormat, TABULAR_NONE)

static Method_mp_utype	MP2s (exportSamplesFormat, TABULAR_HEADER)

static Method_mp_utype	MP2s (exportSamplesFormat, TABULAR_EVAL_ID)

static Method_mp_utype	MP2s (exportSamplesFormat, TABULAR_IFACE_ID)

static Method_mp_utype	MP2s (exportSamplesFormat, TABULAR_ANNOTATED)

static Method_mp_utype	MP2s (importApproxFormat, TABULAR_NONE)

static Method_mp_utype	MP2s (importApproxFormat, TABULAR_HEADER)

static Method_mp_utype	MP2s (importApproxFormat, TABULAR_EVAL_ID)

static Method_mp_utype	MP2s (importApproxFormat, TABULAR_IFACE_ID)

static Method_mp_utype	MP2s (importApproxFormat, TABULAR_ANNOTATED)

static Method_mp_utype	MP2s (importBuildFormat, TABULAR_NONE)

static Method_mp_utype	MP2s (importBuildFormat, TABULAR_HEADER)

static Method_mp_utype	MP2s (importBuildFormat, TABULAR_EVAL_ID)

static Method_mp_utype	MP2s (importBuildFormat, TABULAR_IFACE_ID)

static Method_mp_utype	MP2s (importBuildFormat, TABULAR_ANNOTATED)

static Method_mp_utype	MP2s (importCandFormat, TABULAR_NONE)

static Method_mp_utype	MP2s (importCandFormat, TABULAR_HEADER)

static Method_mp_utype	MP2s (importCandFormat, TABULAR_EVAL_ID)

static Method_mp_utype	MP2s (importCandFormat, TABULAR_IFACE_ID)

static Method_mp_utype	MP2s (importCandFormat, TABULAR_ANNOTATED)

static Method_mp_utype	MP2s (importPredConfigFormat, TABULAR_NONE)

static Method_mp_utype	MP2s (importPredConfigFormat, TABULAR_HEADER)

static Method_mp_utype	MP2s (importPredConfigFormat, TABULAR_EVAL_ID)

static Method_mp_utype	MP2s (importPredConfigFormat, TABULAR_IFACE_ID)

static Method_mp_utype	MP2s (importPredConfigFormat, TABULAR_ANNOTATED)

static Method_mp_utype	MP2s (integrationRefine, AIS)

static Method_mp_utype	MP2s (integrationRefine, IS)

static Method_mp_utype	MP2s (integrationRefine, MMAIS)

static Method_mp_utype	MP2s (methodName, APPROXIMATE_CONTROL_VARIATE)

static Method_mp_utype	MP2s (methodName, ASYNCH_PATTERN_SEARCH)

static Method_mp_utype	MP2s (methodName, BRANCH_AND_BOUND)

static Method_mp_utype	MP2s (methodName, C3_FUNCTION_TRAIN)

static Method_mp_utype	MP2s (methodName, COLINY_BETA)

static Method_mp_utype	MP2s (methodName, COLINY_COBYLA)

static Method_mp_utype	MP2s (methodName, COLINY_DIRECT)

static Method_mp_utype	MP2s (methodName, COLINY_EA)

static Method_mp_utype	MP2s (methodName, COLINY_PATTERN_SEARCH)

static Method_mp_utype	MP2s (methodName, COLINY_SOLIS_WETS)

static Method_mp_utype	MP2s (methodName, CONMIN_FRCG)

static Method_mp_utype	MP2s (methodName, CONMIN_MFD)

static Method_mp_utype	MP2s (methodName, DACE)

static Method_mp_utype	MP2s (methodName, DATA_FIT_SURROGATE_BASED_LOCAL)

static Method_mp_utype	MP2s (methodName, DOT_BFGS)

static Method_mp_utype	MP2s (methodName, DOT_FRCG)

static Method_mp_utype	MP2s (methodName, DOT_MMFD)

static Method_mp_utype	MP2s (methodName, DOT_SLP)

static Method_mp_utype	MP2s (methodName, DOT_SQP)

static Method_mp_utype	MP2s (methodName, EFFICIENT_GLOBAL)

static Method_mp_utype	MP2s (methodName, FSU_CVT)

static Method_mp_utype	MP2s (methodName, FSU_HALTON)

static Method_mp_utype	MP2s (methodName, FSU_HAMMERSLEY)

static Method_mp_utype	MP2s (methodName, HIERARCH_SURROGATE_BASED_LOCAL)

static Method_mp_utype	MP2s (methodName, HYBRID)

static Method_mp_utype	MP2s (methodName, MESH_ADAPTIVE_SEARCH)

static Method_mp_utype	MP2s (methodName, MOGA)

static Method_mp_utype	MP2s (methodName, MULTI_START)

static Method_mp_utype	MP2s (methodName, NCSU_DIRECT)

static Method_mp_utype	MP2s (methodName, ROL)

static Method_mp_utype	MP2s (methodName, DEMO_TPL)

static Method_mp_utype	MP2s (methodName, NL2SOL)

static Method_mp_utype	MP2s (methodName, NLPQL_SQP)

static Method_mp_utype	MP2s (methodName, NLSSOL_SQP)

static Method_mp_utype	MP2s (methodName, MIT_NOWPAC)

static Method_mp_utype	MP2s (methodName, MIT_SNOWPAC)

static Method_mp_utype	MP2s (methodName, ADAPTIVE_SAMPLING)

static Method_mp_utype	MP2s (methodName, BAYES_CALIBRATION)

static Method_mp_utype	MP2s (methodName, GENIE_DIRECT)

static Method_mp_utype	MP2s (methodName, GENIE_OPT_DARTS)

static Method_mp_utype	MP2s (methodName, GPAIS)

static Method_mp_utype	MP2s (methodName, GLOBAL_EVIDENCE)

static Method_mp_utype	MP2s (methodName, GLOBAL_INTERVAL_EST)

static Method_mp_utype	MP2s (methodName, GLOBAL_RELIABILITY)

static Method_mp_utype	MP2s (methodName, IMPORTANCE_SAMPLING)

static Method_mp_utype	MP2s (methodName, LOCAL_EVIDENCE)

static Method_mp_utype	MP2s (methodName, LOCAL_INTERVAL_EST)

static Method_mp_utype	MP2s (methodName, LOCAL_RELIABILITY)

static Method_mp_utype	MP2s (methodName, MULTIFIDELITY_FUNCTION_TRAIN)

static Method_mp_utype	MP2s (methodName, MULTIFIDELITY_POLYNOMIAL_CHAOS)

static Method_mp_utype	MP2s (methodName, MULTIFIDELITY_SAMPLING)

static Method_mp_utype	MP2s (methodName, MULTIFIDELITY_STOCH_COLLOCATION)

static Method_mp_utype	MP2s (methodName, MULTILEVEL_FUNCTION_TRAIN)

static Method_mp_utype	MP2s (methodName, MULTILEVEL_MULTIFIDELITY_SAMPLING)

static Method_mp_utype	MP2s (methodName, MULTILEVEL_POLYNOMIAL_CHAOS)

static Method_mp_utype	MP2s (methodName, MULTILEVEL_SAMPLING)

static Method_mp_utype	MP2s (methodName, POF_DARTS)

static Method_mp_utype	MP2s (methodName, RKD_DARTS)

static Method_mp_utype	MP2s (methodName, POLYNOMIAL_CHAOS)

static Method_mp_utype	MP2s (methodName, STOCH_COLLOCATION)

static Method_mp_utype	MP2s (methodName, SURROGATE_BASED_UQ)

static Method_mp_utype	MP2s (methodName, RANDOM_SAMPLING)

static Method_mp_utype	MP2s (methodName, NONLINEAR_CG)

static Method_mp_utype	MP2s (methodName, NPSOL_SQP)

static Method_mp_utype	MP2s (methodName, OPTPP_CG)

static Method_mp_utype	MP2s (methodName, OPTPP_FD_NEWTON)

static Method_mp_utype	MP2s (methodName, OPTPP_G_NEWTON)

static Method_mp_utype	MP2s (methodName, OPTPP_NEWTON)

static Method_mp_utype	MP2s (methodName, OPTPP_PDS)

static Method_mp_utype	MP2s (methodName, OPTPP_Q_NEWTON)

static Method_mp_utype	MP2s (methodName, PARETO_SET)

static Method_mp_utype	MP2s (methodName, PSUADE_MOAT)

static Method_mp_utype	MP2s (methodName, RICHARDSON_EXTRAP)

static Method_mp_utype	MP2s (methodName, SOGA)

static Method_mp_utype	MP2s (methodName, SURROGATE_BASED_GLOBAL)

static Method_mp_utype	MP2s (methodName, SURROGATE_BASED_LOCAL)

static Method_mp_utype	MP2s (methodName, VECTOR_PARAMETER_STUDY)

static Method_mp_utype	MP2s (methodName, LIST_PARAMETER_STUDY)

static Method_mp_utype	MP2s (methodName, CENTERED_PARAMETER_STUDY)

static Method_mp_utype	MP2s (methodName, MULTIDIM_PARAMETER_STUDY)

static Method_mp_utype	MP2s (modelExportFormat, TEXT_ARCHIVE)

static Method_mp_utype	MP2s (modelExportFormat, BINARY_ARCHIVE)

static Method_mp_utype	MP2s (numericalSolveMode, NUMERICAL_FALLBACK)

static Method_mp_utype	MP2s (numericalSolveMode, NUMERICAL_OVERRIDE)

static Method_mp_utype	MP2s (optSubProbSolver, SUBMETHOD_NONE)

static Method_mp_utype	MP2s (optSubProbSolver, SUBMETHOD_OPTPP)

static Method_mp_utype	MP2s (optSubProbSolver, SUBMETHOD_NPSOL)

static Method_mp_utype	MP2s (optSubProbSolver, SUBMETHOD_SBLO)

static Method_mp_utype	MP2s (optSubProbSolver, SUBMETHOD_EA)

static Method_mp_utype	MP2s (optSubProbSolver, SUBMETHOD_EGO)

static Method_mp_utype	MP2s (optSubProbSolver, SUBMETHOD_SBGO)

static Method_mp_utype	MP2s (optSubProbSolver, SUBMETHOD_LHS)

static Method_mp_utype	MP2s (pstudyFileFormat, TABULAR_NONE)

static Method_mp_utype	MP2s (pstudyFileFormat, TABULAR_HEADER)

static Method_mp_utype	MP2s (pstudyFileFormat, TABULAR_EVAL_ID)

static Method_mp_utype	MP2s (pstudyFileFormat, TABULAR_IFACE_ID)

static Method_mp_utype	MP2s (pstudyFileFormat, TABULAR_ANNOTATED)

static Method_mp_utype	MP2s (sampleType, SUBMETHOD_LHS)

static Method_mp_utype	MP2s (sampleType, SUBMETHOD_RANDOM)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_AMV_PLUS_U)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_AMV_PLUS_X)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_AMV_U)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_AMV_X)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_QMEA_U)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_QMEA_X)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_TANA_U)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_TANA_X)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_NO_APPROX)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_EGRA_U)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_EGRA_X)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_ACV_IS)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_ACV_MF)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_ACV_RD)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_COLLABORATIVE)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_EMBEDDED)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_SEQUENTIAL)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_MUQ)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_DREAM)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_WASABI)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_GPMSA)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_QUESO)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_LHS)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_RANDOM)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_OA_LHS)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_OAS)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_BOX_BEHNKEN)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_CENTRAL_COMPOSITE)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_GRID)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_CONVERGE_ORDER)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_CONVERGE_QOI)

static Method_mp_utype	MP2s (subMethod, SUBMETHOD_ESTIMATE_ORDER)

static SizetSet	MP_ (surrogateFnIndices)

static Model_mp_lit	MP2 (approxPointReuse, all)

static Model_mp_lit	MP2 (approxPointReuse, none)

static Model_mp_lit	MP2 (approxPointReuse, region)

static Model_mp_lit	MP2 (marsInterpolation, linear)

static Model_mp_lit	MP2 (marsInterpolation, cubic)

static Model_mp_lit	MP2 (modelType, active_subspace)

static Model_mp_lit	MP2 (modelType, adapted_basis)

static Model_mp_lit	MP2 (modelType, nested)

static Model_mp_lit	MP2 (modelType, random_field)

static Model_mp_lit	MP2 (modelType, simulation)

static Model_mp_lit	MP2 (modelType, surrogate)

static Model_mp_lit	MP2 (surrogateType, ensemble)

static Model_mp_lit	MP2 (surrogateType, global_exp_gauss_proc)

static Model_mp_lit	MP2 (surrogateType, global_exp_poly)

static Model_mp_lit	MP2 (surrogateType, global_function_train)

static Model_mp_lit	MP2 (surrogateType, global_gaussian)

static Model_mp_lit	MP2 (surrogateType, global_kriging)

static Model_mp_lit	MP2 (surrogateType, global_mars)

static Model_mp_lit	MP2 (surrogateType, global_moving_least_squares)

static Model_mp_lit	MP2 (surrogateType, global_neural_network)

static Model_mp_lit	MP2 (surrogateType, global_polynomial)

static Model_mp_lit	MP2 (surrogateType, global_radial_basis)

static Model_mp_lit	MP2 (surrogateType, global_voronoi_surrogate)

static Model_mp_lit	MP2 (surrogateType, local_taylor)

static Model_mp_lit	MP2 (surrogateType, multipoint_qmea)

static Model_mp_lit	MP2 (surrogateType, multipoint_tana)

static Model_mp_lit	MP2 (trendOrder, none)

static Model_mp_lit	MP2 (trendOrder, constant)

static Model_mp_lit	MP2 (trendOrder, linear)

static Model_mp_lit	MP2 (trendOrder, reduced_quadratic)

static Model_mp_lit	MP2 (trendOrder, quadratic)

static Model_mp_ord	MP2s (approxCorrectionOrder, 0)

static Model_mp_ord	MP2s (approxCorrectionOrder, 1)

static Model_mp_ord	MP2s (approxCorrectionOrder, 2)

static Model_mp_ord	MP2s (polynomialOrder, 1)

static Model_mp_ord	MP2s (polynomialOrder, 2)

static Model_mp_ord	MP2s (polynomialOrder, 3)

static Model_mp_type	MP2s (approxCorrectionType, ADDITIVE_CORRECTION)

static Model_mp_type	MP2s (approxCorrectionType, COMBINED_CORRECTION)

static Model_mp_type	MP2s (approxCorrectionType, MULTIPLICATIVE_CORRECTION)

static Model_mp_type	MP2s (pointsManagement, MINIMUM_POINTS)

static Model_mp_type	MP2s (pointsManagement, RECOMMENDED_POINTS)

static Model_mp_type	MP2s (subMethodScheduling, MASTER_SCHEDULING)

static Model_mp_type	MP2s (method_rotation, ROTATION_METHOD_UNRANKED)

static Model_mp_type	MP2s (method_rotation, ROTATION_METHOD_RANKED)

static Model_mp_type	MP2s (subMethodScheduling, PEER_SCHEDULING)

static Model_mp_utype	MP2s (analyticCovIdForm, EXP_L2)

static Model_mp_utype	MP2s (analyticCovIdForm, EXP_L1)

static Model_mp_utype	MP2s (exportApproxVarianceFormat, TABULAR_NONE)

static Model_mp_utype	MP2s (exportApproxVarianceFormat, TABULAR_HEADER)

static Model_mp_utype	MP2s (exportApproxVarianceFormat, TABULAR_EVAL_ID)

static Model_mp_utype	MP2s (exportApproxVarianceFormat, TABULAR_IFACE_ID)

static Model_mp_utype	MP2s (exportApproxVarianceFormat, TABULAR_ANNOTATED)

static Model_mp_utype	MP2s (importChallengeFormat, TABULAR_NONE)

static Model_mp_utype	MP2s (importChallengeFormat, TABULAR_HEADER)

static Model_mp_utype	MP2s (importChallengeFormat, TABULAR_EVAL_ID)

static Model_mp_utype	MP2s (importChallengeFormat, TABULAR_IFACE_ID)

static Model_mp_utype	MP2s (importChallengeFormat, TABULAR_ANNOTATED)

static Model_mp_utype	MP2s (modelExportFormat, ALGEBRAIC_FILE)

static Model_mp_utype	MP2s (modelExportFormat, ALGEBRAIC_CONSOLE)

static Model_mp_utype	MP2s (modelImportFormat, TEXT_ARCHIVE)

static Model_mp_utype	MP2s (modelImportFormat, BINARY_ARCHIVE)

static Model_mp_utype	MP2s (randomFieldIdForm, RF_KARHUNEN_LOEVE)

static Model_mp_utype	MP2s (randomFieldIdForm, RF_PCA_GP)

static Model_mp_utype	MP2s (subspaceNormalization, SUBSPACE_NORM_MEAN_VALUE)

static Model_mp_utype	MP2s (subspaceNormalization, SUBSPACE_NORM_MEAN_GRAD)

static Model_mp_utype	MP2s (subspaceNormalization, SUBSPACE_NORM_LOCAL_GRAD)

static Model_mp_utype	MP2s (subspaceSampleType, SUBMETHOD_LHS)

static Model_mp_utype	MP2s (subspaceSampleType, SUBMETHOD_RANDOM)

static Model_mp_utype	MP2s (subspaceIdCVMethod, MINIMUM_METRIC)

static Model_mp_utype	MP2s (subspaceIdCVMethod, RELATIVE_TOLERANCE)

static Model_mp_utype	MP2s (subspaceIdCVMethod, DECREASE_TOLERANCE)

static Real	MP_ (adaptedBasisCollocRatio)

static Real	MP_ (annRange)

static Real	MP_ (decreaseTolerance)

static Real	MP_ (discontGradThresh)

static Real	MP_ (discontJumpThresh)

static Real	MP_ (krigingNugget)

static Real	MP_ (percentFold)

static Real	MP_ (relTolerance)

static Real	MP_ (truncationTolerance)

static Real	MP_ (adaptedBasisTruncationTolerance)

static RealVector	MP_ (krigingCorrelations)

static RealVector	MP_ (primaryRespCoeffs)

static RealVector	MP_ (secondaryRespCoeffs)

static RealVector	MP_ (solutionLevelCost)

static String	MP_ (costRecoveryMetadata)

static String	MP_ (decompCellType)

static String	MP_ (exportApproxVarianceFile)

static String	MP_ (idModel)

static String	MP_ (importChallengePtsFile)

static String	MP_ (interfacePointer)

static String	MP_ (krigingOptMethod)

static String	MP_ (modelImportPrefix)

static String	MP_ (optionalInterfRespPointer)

static String	MP_ (propagationModelPointer)

static String	MP_ (refineCVMetric)

static String	MP_ (responsesPointer)

static String	MP_ (rfDataFileName)

static String	MP_ (solutionLevelControl)

static String	MP_ (truthModelPointer)

static String	MP_ (variablesPointer)

static StringArray	MP_ (diagMetrics)

static StringArray	MP_ (ensembleModelPointers)

static StringArray	MP_ (primaryVarMaps)

static StringArray	MP_ (secondaryVarMaps)

static bool	MP_ (autoRefine)

static bool	MP_ (crossValidateFlag)

static bool	MP_ (decompDiscontDetect)

static bool	MP_ (importSurrogate)

static bool	MP_ (hierarchicalTags)

static bool	MP_ (identityRespMap)

static bool	MP_ (importChallengeActive)

static bool	MP_ (importChalUseVariableLabels)

static bool	MP_ (importUseVariableLabels)

static bool	MP_ (modelUseDerivsFlag)

static bool	MP_ (domainDecomp)

static bool	MP_ (pointSelection)

static bool	MP_ (pressFlag)

static bool	MP_ (subspaceIdBingLi)

static bool	MP_ (subspaceIdConstantine)

static bool	MP_ (subspaceIdEnergy)

static bool	MP_ (subspaceBuildSurrogate)

static bool	MP_ (subspaceIdCV)

static bool	MP_ (subspaceCVIncremental)

static unsigned short	MP_ (adaptedBasisSparseGridLev)

static unsigned short	MP_ (adaptedBasisExpOrder)

static short	MP_ (annNodes)

static short	MP_ (annRandomWeight)

static short	MP_ (c3AdvanceType)

static short	MP_ (krigingFindNugget)

static short	MP_ (krigingMaxTrials)

static short	MP_ (marsMaxBases)

static short	MP_ (mlsWeightFunction)

static short	MP_ (rbfBases)

static short	MP_ (rbfMaxPts)

static short	MP_ (rbfMaxSubsets)

static short	MP_ (rbfMinPartition)

static int	MP_ (decompSupportLayers)

static int	MP_ (initialSamples)

static int	MP_ (numFolds)

static int	MP_ (numReplicates)

static int	MP_ (numRestarts)

static int	MP_ (pointsTotal)

static int	MP_ (refineCVFolds)

static int	MP_ (softConvergenceLimit)

static int	MP_ (subMethodProcs)

static int	MP_ (subMethodServers)

static int	MP_ (subspaceDimension)

static int	MP_ (subspaceCVMaxRank)

static IntSet	MP_ (idAnalyticGrads)

static IntSet	MP_ (idAnalyticHessians)

static IntSet	MP_ (idNumericalGrads)

static IntSet	MP_ (idNumericalHessians)

static IntSet	MP_ (idQuasiHessians)

static IntVector	MP_ (fieldLengths)

static IntVector	MP_ (numCoordsPerField)

static RealVector	MP_ (expConfigVars)

static RealVector	MP_ (expObservations)

static RealVector	MP_ (primaryRespFnWeights)

static RealVector	MP_ (nonlinearEqTargets)

static RealVector	MP_ (nonlinearIneqLowerBnds)

static RealVector	MP_ (nonlinearIneqUpperBnds)

static RealVector	MP_ (simVariance)

static RealVector	MP_ (fdGradStepSize)

static RealVector	MP_ (fdHessStepSize)

static RealVector	MP_ (primaryRespFnScales)

static RealVector	MP_ (nonlinearEqScales)

static RealVector	MP_ (nonlinearIneqScales)

static Resp_mp_lit	MP2 (gradientType, analytic)

static Resp_mp_lit	MP2 (gradientType, mixed)

static Resp_mp_lit	MP2 (gradientType, none)

static Resp_mp_lit	MP2 (gradientType, numerical)

static Resp_mp_lit	MP2 (hessianType, analytic)

static Resp_mp_lit	MP2 (hessianType, mixed)

static Resp_mp_lit	MP2 (hessianType, none)

static Resp_mp_lit	MP2 (hessianType, numerical)

static Resp_mp_lit	MP2 (hessianType, quasi)

static Resp_mp_lit	MP2 (intervalType, central)

static Resp_mp_lit	MP2 (intervalType, forward)

static Resp_mp_lit	MP2 (methodSource, dakota)

static Resp_mp_lit	MP2 (methodSource, vendor)

static Resp_mp_lit	MP2 (fdGradStepType, absolute)

static Resp_mp_lit	MP2 (fdGradStepType, bounds)

static Resp_mp_lit	MP2 (fdGradStepType, relative)

static Resp_mp_lit	MP2 (fdHessStepType, absolute)

static Resp_mp_lit	MP2 (fdHessStepType, bounds)

static Resp_mp_lit	MP2 (fdHessStepType, relative)

static Resp_mp_lit	MP2 (quasiHessianType, bfgs)

static Resp_mp_lit	MP2 (quasiHessianType, damped_bfgs)

static Resp_mp_lit	MP2 (quasiHessianType, sr1)

static String	MP_ (dataPathPrefix)

static String	MP_ (scalarDataFileName)

static String	MP_ (idResponses)

static StringArray	MP_ (metadataLabels)

static StringArray	MP_ (nonlinearEqScaleTypes)

static StringArray	MP_ (nonlinearIneqScaleTypes)

static StringArray	MP_ (primaryRespFnScaleTypes)

static StringArray	MP_ (primaryRespFnSense)

static StringArray	MP_ (responseLabels)

static StringArray	MP_ (varianceType)

static bool	MP_ (calibrationDataFlag)

static bool	MP_ (centralHess)

static bool	MP_ (interpolateFlag)

static bool	MP_ (ignoreBounds)

static bool	MP_ (readFieldCoords)

static size_t	MP_ (numExpConfigVars)

static size_t	MP_ (numExperiments)

static size_t	MP_ (numFieldLeastSqTerms)

static size_t	MP_ (numFieldObjectiveFunctions)

static size_t	MP_ (numFieldResponseFunctions)

static size_t	MP_ (numLeastSqTerms)

static size_t	MP_ (numNonlinearEqConstraints)

static size_t	MP_ (numNonlinearIneqConstraints)

static size_t	MP_ (numObjectiveFunctions)

static size_t	MP_ (numResponseFunctions)

static size_t	MP_ (numScalarLeastSqTerms)

static size_t	MP_ (numScalarObjectiveFunctions)

static size_t	MP_ (numScalarResponseFunctions)

static Resp_mp_utype	MP2s (scalarDataFormat, TABULAR_NONE)

static Resp_mp_utype	MP2s (scalarDataFormat, TABULAR_HEADER)

static Resp_mp_utype	MP2s (scalarDataFormat, TABULAR_EVAL_ID)

static Resp_mp_utype	MP2s (scalarDataFormat, TABULAR_EXPER_ANNOT)

static Env_mp_utype	MP2s (postRunInputFormat, TABULAR_NONE)

static Env_mp_utype	MP2s (postRunInputFormat, TABULAR_HEADER)

static Env_mp_utype	MP2s (postRunInputFormat, TABULAR_EVAL_ID)

static Env_mp_utype	MP2s (postRunInputFormat, TABULAR_IFACE_ID)

static Env_mp_utype	MP2s (postRunInputFormat, TABULAR_ANNOTATED)

static Env_mp_utype	MP2s (preRunOutputFormat, TABULAR_NONE)

static Env_mp_utype	MP2s (preRunOutputFormat, TABULAR_HEADER)

static Env_mp_utype	MP2s (preRunOutputFormat, TABULAR_EVAL_ID)

static Env_mp_utype	MP2s (preRunOutputFormat, TABULAR_IFACE_ID)

static Env_mp_utype	MP2s (preRunOutputFormat, TABULAR_ANNOTATED)

static Env_mp_utype	MP2s (tabularFormat, TABULAR_NONE)

static Env_mp_utype	MP2s (tabularFormat, TABULAR_HEADER)

static Env_mp_utype	MP2s (tabularFormat, TABULAR_EVAL_ID)

static Env_mp_utype	MP2s (tabularFormat, TABULAR_IFACE_ID)

static Env_mp_utype	MP2s (tabularFormat, TABULAR_ANNOTATED)

static Env_mp_utype	MP2s (resultsOutputFormat, RESULTS_OUTPUT_TEXT)

static Env_mp_utype	MP2s (resultsOutputFormat, RESULTS_OUTPUT_HDF5)

static Env_mp_utype	MP2s (modelEvalsSelection, MODEL_EVAL_STORE_TOP_METHOD)

static Env_mp_utype	MP2s (modelEvalsSelection, MODEL_EVAL_STORE_NONE)

static Env_mp_utype	MP2s (modelEvalsSelection, MODEL_EVAL_STORE_ALL)

static Env_mp_utype	MP2s (modelEvalsSelection, MODEL_EVAL_STORE_ALL_METHODS)

static Env_mp_utype	MP2s (interfEvalsSelection, INTERF_EVAL_STORE_SIMULATION)

static Env_mp_utype	MP2s (interfEvalsSelection, INTERF_EVAL_STORE_NONE)

static Env_mp_utype	MP2s (interfEvalsSelection, INTERF_EVAL_STORE_ALL)

static String	MP_ (errorFile)

static String	MP_ (outputFile)

static String	MP_ (postRunInput)

static String	MP_ (postRunOutput)

static String	MP_ (preRunInput)

static String	MP_ (preRunOutput)

static String	MP_ (readRestart)

static String	MP_ (resultsOutputFile)

static String	MP_ (runInput)

static String	MP_ (runOutput)

static String	MP_ (tabularDataFile)

static String	MP_ (topMethodPointer)

static String	MP_ (writeRestart)

static bool	MP_ (checkFlag)

static bool	MP_ (graphicsFlag)

static bool	MP_ (postRunFlag)

static bool	MP_ (preRunFlag)

static bool	MP_ (resultsOutputFlag)

static bool	MP_ (runFlag)

static bool	MP_ (tabularDataFlag)

static int	MP_ (outputPrecision)

static int	MP_ (stopRestart)

static size_t	MP_ (numBetaUncVars)

static size_t	MP_ (numBinomialUncVars)

static size_t	MP_ (numContinuousDesVars)

static size_t	MP_ (numContinuousIntervalUncVars)

static size_t	MP_ (numContinuousStateVars)

static size_t	MP_ (numDiscreteDesRangeVars)

static size_t	MP_ (numDiscreteDesSetIntVars)

static size_t	MP_ (numDiscreteDesSetStrVars)

static size_t	MP_ (numDiscreteDesSetRealVars)

static size_t	MP_ (numDiscreteIntervalUncVars)

static size_t	MP_ (numDiscreteStateRangeVars)

static size_t	MP_ (numDiscreteStateSetIntVars)

static size_t	MP_ (numDiscreteStateSetStrVars)

static size_t	MP_ (numDiscreteStateSetRealVars)

static size_t	MP_ (numDiscreteUncSetIntVars)

static size_t	MP_ (numDiscreteUncSetStrVars)

static size_t	MP_ (numDiscreteUncSetRealVars)

static size_t	MP_ (numExponentialUncVars)

static size_t	MP_ (numFrechetUncVars)

static size_t	MP_ (numGammaUncVars)

static size_t	MP_ (numGeometricUncVars)

static size_t	MP_ (numGumbelUncVars)

static size_t	MP_ (numHistogramBinUncVars)

static size_t	MP_ (numHistogramPtIntUncVars)

static size_t	MP_ (numHistogramPtStrUncVars)

static size_t	MP_ (numHistogramPtRealUncVars)

static size_t	MP_ (numHyperGeomUncVars)

static size_t	MP_ (numLognormalUncVars)

static size_t	MP_ (numLoguniformUncVars)

static size_t	MP_ (numNegBinomialUncVars)

static size_t	MP_ (numNormalUncVars)

static size_t	MP_ (numPoissonUncVars)

static size_t	MP_ (numTriangularUncVars)

static size_t	MP_ (numUniformUncVars)

static size_t	MP_ (numWeibullUncVars)

static IntVector	VP_ (ddsi)

static IntVector	VP_ (DIlb)

static IntVector	MP_ (discreteDesignRangeLowerBnds)

static IntVector	MP_ (discreteDesignRangeUpperBnds)

static IntVector	MP_ (discreteDesignRangeVars)

static IntVector	MP_ (discreteDesignSetIntVars)

static IntVector	MP_ (discreteIntervalUncVars)

static IntVector	MP_ (discreteStateRangeLowerBnds)

static IntVector	MP_ (discreteStateRangeUpperBnds)

static IntVector	MP_ (discreteStateRangeVars)

static IntVector	MP_ (discreteStateSetIntVars)

static IntVector	MP_ (discreteUncSetIntVars)

static IntVector	VP_ (DIub)

static IntVector	MP_ (histogramPointIntUncVars)

static IntVector	VP_ (hpia)

static IntVector	VP_ (dssi)

static IntVector	VP_ (ddsia)

static IntVector	VP_ (ddssa)

static IntVector	VP_ (ddsra)

static IntVector	VP_ (dusi)

static IntArray	VP_ (nddsi)

static IntArray	VP_ (nddss)

static IntArray	VP_ (nddsr)

static IntArray	VP_ (ndssi)

static IntArray	VP_ (ndsss)

static IntArray	VP_ (ndssr)

static IntArray	VP_ (ndusi)

static IntArray	VP_ (nduss)

static IntArray	VP_ (ndusr)

static IntArray	VP_ (nhbp)

static IntArray	VP_ (nhpip)

static IntArray	VP_ (nhpsp)

static IntArray	VP_ (nhprp)

static IntArray	VP_ (nCI)

static IntArray	VP_ (nDI)

static RealVector	MP_ (betaUncLowerBnds)

static RealVector	MP_ (betaUncUpperBnds)

static RealVector	MP_ (betaUncVars)

static RealVector	MP_ (binomialUncProbPerTrial)

static RealVector	MP_ (continuousDesignLowerBnds)

static RealVector	MP_ (continuousDesignUpperBnds)

static RealVector	MP_ (continuousDesignVars)

static RealVector	MP_ (continuousDesignScales)

static RealVector	MP_ (continuousIntervalUncVars)

static RealVector	MP_ (continuousStateLowerBnds)

static RealVector	MP_ (continuousStateUpperBnds)

static RealVector	MP_ (continuousStateVars)

static RealVector	MP_ (discreteDesignSetRealVars)

static RealVector	MP_ (discreteStateSetRealVars)

static RealVector	MP_ (discreteUncSetRealVars)

static RealVector	MP_ (frechetUncBetas)

static RealVector	MP_ (frechetUncVars)

static RealVector	MP_ (geometricUncProbPerTrial)

static RealVector	MP_ (gumbelUncBetas)

static RealVector	MP_ (gumbelUncVars)

static RealVector	MP_ (histogramBinUncVars)

static RealVector	MP_ (histogramPointRealUncVars)

static RealVector	MP_ (linearEqConstraintCoeffs)

static RealVector	MP_ (linearEqScales)

static RealVector	MP_ (linearEqTargets)

static RealVector	MP_ (linearIneqConstraintCoeffs)

static RealVector	MP_ (linearIneqLowerBnds)

static RealVector	MP_ (linearIneqUpperBnds)

static RealVector	MP_ (linearIneqScales)

static RealVector	MP_ (negBinomialUncProbPerTrial)

static RealVector	MP_ (normalUncLowerBnds)

static RealVector	MP_ (normalUncMeans)

static RealVector	MP_ (normalUncUpperBnds)

static RealVector	MP_ (normalUncVars)

static RealVector	MP_ (triangularUncModes)

static RealVector	MP_ (triangularUncVars)

static RealVector	MP_ (uniformUncVars)

static RealVector	MP_ (weibullUncVars)

static RealVector	VP_ (ddsr)

static RealVector	VP_ (dssr)

static RealVector	VP_ (dusr)

static RealVector	VP_ (CIlb)

static RealVector	VP_ (CIub)

static RealVector	VP_ (CIp)

static RealVector	VP_ (DIp)

static RealVector	VP_ (DSIp)

static RealVector	VP_ (DSSp)

static RealVector	VP_ (DSRp)

static RealVector	VP_ (hba)

static RealVector	VP_ (hbo)

static RealVector	VP_ (hbc)

static RealVector	VP_ (hpic)

static RealVector	VP_ (hpsc)

static RealVector	VP_ (hpra)

static RealVector	VP_ (hprc)

static RealVector	VP_ (ucm)

static String	MP_ (idVariables)

static StringArray	MP_ (continuousDesignLabels)

static StringArray	MP_ (continuousDesignScaleTypes)

static StringArray	MP_ (continuousStateLabels)

static StringArray	MP_ (discreteDesignRangeLabels)

static StringArray	MP_ (discreteDesignSetIntLabels)

static StringArray	MP_ (discreteDesignSetStrLabels)

static StringArray	MP_ (discreteDesignSetRealLabels)

static StringArray	MP_ (discreteStateRangeLabels)

static StringArray	MP_ (discreteStateSetIntLabels)

static StringArray	MP_ (discreteStateSetStrLabels)

static StringArray	MP_ (discreteStateSetRealLabels)

static StringArray	MP_ (discreteDesignSetStrVars)

static StringArray	MP_ (discreteUncSetStrVars)

static StringArray	MP_ (discreteStateSetStrVars)

static StringArray	MP_ (histogramPointStrUncVars)

static StringArray	MP_ (linearEqScaleTypes)

static StringArray	MP_ (linearIneqScaleTypes)

static StringArray	VP_ (hpsa)

static StringArray	VP_ (ddss)

static StringArray	VP_ (duss)

static StringArray	VP_ (dsss)

static BitArray	MP_ (discreteDesignSetIntCat)

static BitArray	MP_ (discreteDesignSetRealCat)

static BitArray	MP_ (discreteStateSetIntCat)

static BitArray	MP_ (discreteStateSetRealCat)

static BitArray	MP_ (discreteUncSetIntCat)

static BitArray	MP_ (discreteUncSetRealCat)

static Var_brv	MP2s (betaUncAlphas, 0.)

static Var_brv	MP2s (betaUncBetas, 0.)

static Var_brv	MP2s (exponentialUncBetas, 0.)

static Var_brv	MP2s (exponentialUncVars, 0.)

static Var_brv	MP2s (frechetUncAlphas, 2.)

static Var_brv	MP2s (gammaUncAlphas, 0.)

static Var_brv	MP2s (gammaUncBetas, 0.)

static Var_brv	MP2s (gammaUncVars, 0.)

static Var_brv	MP2s (gumbelUncAlphas, 0.)

static Var_brv	MP2s (lognormalUncErrFacts, 1.)

static Var_brv	MP2s (lognormalUncLambdas, 0.)

static Var_brv	MP2s (lognormalUncLowerBnds, 0.)

static Var_brv	MP2s (lognormalUncMeans, 0.)

static Var_brv	MP2s (lognormalUncStdDevs, 0.)

static Var_brv	MP2s (lognormalUncUpperBnds, std::numeric_limits< Real >::infinity())

static Var_brv	MP2s (lognormalUncVars, 0.)

static Var_brv	MP2s (lognormalUncZetas, 0.)

static Var_brv	MP2s (loguniformUncLowerBnds, 0.)

static Var_brv	MP2s (loguniformUncUpperBnds, std::numeric_limits< Real >::infinity())

static Var_brv	MP2s (loguniformUncVars, 0.)

static Var_brv	MP2s (normalUncStdDevs, 0.)

static Var_brv	MP2s (poissonUncLambdas, 0.)

static Var_brv	MP2s (triangularUncLowerBnds,-std::numeric_limits< Real >::infinity())

static Var_brv	MP2s (triangularUncUpperBnds, std::numeric_limits< Real >::infinity())

static Var_brv	MP2s (uniformUncLowerBnds,-std::numeric_limits< Real >::infinity())

static Var_brv	MP2s (uniformUncUpperBnds, std::numeric_limits< Real >::infinity())

static Var_brv	MP2s (weibullUncAlphas, 0.)

static Var_brv	MP2s (weibullUncBetas, 0.)

static Var_biv	MP2s (binomialUncNumTrials, 0)

static Var_biv	MP2s (binomialUncVars, 0)

static Var_biv	MP2s (geometricUncVars, 0)

static Var_biv	MP2s (hyperGeomUncNumDrawn, 0)

static Var_biv	MP2s (hyperGeomUncSelectedPop, 0)

static Var_biv	MP2s (hyperGeomUncTotalPop, 0)

static Var_biv	MP2s (hyperGeomUncVars, 0)

static Var_biv	MP2s (negBinomialUncNumTrials, 0)

static Var_biv	MP2s (negBinomialUncVars, 0)

static Var_biv	MP2s (poissonUncVars, 0)

static Var_mp_type	Vtype (varsDomain, MIXED_DOMAIN)

static Var_mp_type	Vtype (varsDomain, RELAXED_DOMAIN)

static Var_mp_type	Vtype (varsView, ALL_VIEW)

static Var_mp_type	Vtype (varsView, DESIGN_VIEW)

static Var_mp_type	Vtype (varsView, UNCERTAIN_VIEW)

static Var_mp_type	Vtype (varsView, ALEATORY_UNCERTAIN_VIEW)

static Var_mp_type	Vtype (varsView, EPISTEMIC_UNCERTAIN_VIEW)

static Var_mp_type	Vtype (varsView, STATE_VIEW)

template<class ContainerT >
void	flatten_num_array (const std::vector< ContainerT > &input_array, IntArray **pia)
	Free convenience function that flatten sizes of an array of std containers; takes an array of containers and returns an IntArray containing the sizes of each container in the input array. Note: Did not specialize for vector<RealVector> as no current use cases.

void	dn2f_ (int n, int p, Real x, Calcrj, int iv, int liv, int lv, Real v, int ui, void *ur, Vf)

void	dn2fb_ (int n, int p, Real x, Real b, Calcrj, int iv, int liv, int lv, Real v, int ui, void ur, Vf)

void	dn2g_ (int n, int p, Real x, Calcrj, Calcrj, int iv, int liv, int lv, Real v, int ui, void *ur, Vf)

void	dn2gb_ (int n, int p, Real x, Real b, Calcrj, Calcrj, int iv, int liv, int lv, Real v, int ui, void ur, Vf)

void	divset_ (int , int , int , int , Real *)

double	dr7mdc_ (int *)

static void	Rswapchk (Nl2Misc *q)

static int	hasnaninf (const double *d, int n)

NLPQLPOptimizer *	new_NLPQLPOptimizer (ProblemDescDB &problem_db, Model &model)

NLPQLPOptimizer *	new_NLPQLPOptimizer (Model &model)

void	print_c3_sobol_indices (double value, size_t ninteract, size_t interactions, void arg)

static const RealVector *	static_lev_cost_vec (NULL)

static size_t *	static_qoi (NULL)

static const Real *	static_eps_sq_div_2 (NULL)

static const RealVector *	static_Nlq_pilot (NULL)

static const size_t *	static_numFunctions (NULL)

static const size_t *	static_qoiAggregation (NULL)

static int *	static_randomSeed (NULL)

static const IntRealMatrixMap *	static_sum_Ql (NULL)

static const IntRealMatrixMap *	static_sum_Qlm1 (NULL)

static const IntIntPairRealMatrixMap *	static_sum_QlQlm1 (NULL)

static const RealMatrix *	static_scalarization_response_mapping (NULL)

static const IntRealMatrixMap *	static_levQoisamplesmatrixMap (NULL)

static const short *	static_cov_approximation_type (NULL)

static const Real *	static_mu_four_L (NULL)

static const Real *	static_mu_four_H (NULL)

static const Real *	static_var_L (NULL)

static const Real *	static_var_H (NULL)

static const Real *	static_Ax (NULL)

NOWPACOptimizer *	new_NOWPACOptimizer (ProblemDescDB &problem_db, Model &model)

NOWPACOptimizer *	new_NOWPACOptimizer (Model &model)

NPSOLOptimizer *	new_NPSOLOptimizer (ProblemDescDB &problem_db)

NPSOLOptimizer *	new_NPSOLOptimizer1 (Model &model)

NPSOLOptimizer *	new_NPSOLOptimizer2 (Model &model, int derivative_level, Real conv_tol)

NPSOLOptimizer *	new_NPSOLOptimizer3 (const RealVector &initial_point, const RealVector &var_lower_bnds, const RealVector &var_upper_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_lower_bnds, const RealVector &lin_ineq_upper_bnds, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_targets, const RealVector &nonlin_ineq_lower_bnds, const RealVector &nonlin_ineq_upper_bnds, const RealVector &nonlin_eq_targets, void(user_obj_eval)(int &, int &, double , double &, double , int &), void(user_con_eval)(int &, int &, int &, int &, int , double , double , double , int &), int derivative_level, Real conv_tol)

NPSOLOptimizer *	new_NPSOLOptimizer (ProblemDescDB &problem_db, Model &model)

NPSOLOptimizer *	new_NPSOLOptimizer (Model &model)

NPSOLOptimizer *	new_NPSOLOptimizer (Model &model, int, Real)

NPSOLOptimizer *	new_NPSOLOptimizer (const RealVector &initial_point, const RealVector &var_lower_bnds, const RealVector &var_upper_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_lower_bnds, const RealVector &lin_ineq_upper_bnds, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_targets, const RealVector &nonlin_ineq_lower_bnds, const RealVector &nonlin_ineq_upper_bnds, const RealVector &nonlin_eq_targets, void(user_obj_eval)(int &, int &, double , double &, double , int &), void(user_con_eval)(int &, int &, int &, int &, int , double , double , double , int &), int derivative_level, Real conv_tol)

void	start_dakota_heartbeat (int)

void	dak_sigcatch (int sig)

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, ParallelLevel &pl)
	MPIUnpackBuffer extraction operator for ParallelLevel. Calls read(MPIUnpackBuffer&).

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const ParallelLevel &pl)
	MPIPackBuffer insertion operator for ParallelLevel. Calls write(MPIPackBuffer&).

std::istream &	operator>> (std::istream &s, ParamResponsePair &pair)
	std::istream extraction operator for ParamResponsePair

std::ostream &	operator<< (std::ostream &s, const ParamResponsePair &pair)
	std::ostream insertion operator for ParamResponsePair

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, ParamResponsePair &pair)
	MPIUnpackBuffer extraction operator for ParamResponsePair.

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const ParamResponsePair &pair)
	MPIPackBuffer insertion operator for ParamResponsePair.

bool	operator== (const ParamResponsePair &pair1, const ParamResponsePair &pair2)
	equality operator for ParamResponsePair More...

bool	operator!= (const ParamResponsePair &pair1, const ParamResponsePair &pair2)
	inequality operator for ParamResponsePair More...

void	copy_gradient (int const fun_idx, std::vector< std::vector< double >> const &source, RealMatrix &dest)

void	copy_hessian (std::vector< std::vector< double >> const &source, RealSymMatrix &dest)

static void	Bad_name (const String &entry_name, const String &where)

static void	Locked_db ()

static void	Null_rep (const String &who)

std::pair< std::string, std::string >	split_entry_name (const std::string &entry_name, const std::string &context_msg)

boost::regex	PARAMS_TOKEN ("\\{PARAMETERS\\}")

boost::regex	RESULTS_TOKEN ("\\{RESULTS\\}")

String	substitute_params_and_results (const String &driver, const String &params, const String &results)
	Substitute parameters and results file names into driver strings.

MPIUnpackBuffer &	operator>> (MPIUnpackBuffer &s, ProgramOptions &p_opt)
	MPIUnpackBuffer extraction operator.

MPIPackBuffer &	operator<< (MPIPackBuffer &s, const ProgramOptions &p_opt)
	MPIPackBuffer insertion operator.

bool	set_compare (const ParamResponsePair &database_pr, const ActiveSet &search_set)
	search function for a particular ParamResponsePair within a PRPList based on ActiveSet content (request vector and derivative variables vector) More...

bool	id_vars_exact_compare (const ParamResponsePair &database_pr, const ParamResponsePair &search_pr)
	search function for a particular ParamResponsePair within a PRPMultiIndex More...

std::size_t	hash_value (const ParamResponsePair &prp)
	hash_value for ParamResponsePairs stored in a PRPMultiIndex

PRPCacheHIter	hashedCacheBegin (PRPCache &prp_cache)
	hashed definition of cache begin

PRPCacheHIter	hashedCacheEnd (PRPCache &prp_cache)
	hashed definition of cache end

PRPQueueHIter	hashedQueueBegin (PRPQueue &prp_queue)
	hashed definition of queue begin

PRPQueueHIter	hashedQueueEnd (PRPQueue &prp_queue)
	hashed definition of queue end

PRPCacheHIter	lookup_by_val (PRPMultiIndexCache &prp_cache, const ParamResponsePair &search_pr)
	find a ParamResponsePair based on the interface id, variables, and ActiveSet search data within search_pr. More...

PRPCacheHIter	lookup_by_val (PRPMultiIndexCache &prp_cache, const String &search_interface_id, const Variables &search_vars, const ActiveSet &search_set)
	find a ParamResponsePair within a PRPMultiIndexCache based on the interface id, variables, and ActiveSet search data

PRPCacheOIter	lookup_by_nearby_val (PRPMultiIndexCache &prp_cache, const String &search_interface_id, const Variables &search_vars, const ActiveSet &search_set, Real tol)

PRPCacheOIter	lookup_by_ids (PRPMultiIndexCache &prp_cache, const IntStringPair &search_ids)
	find a ParamResponsePair within a PRPMultiIndexCache based on search_ids (i.e. std::pair<eval_id,interface_id>) search data

PRPCacheOIter	lookup_by_ids (PRPMultiIndexCache &prp_cache, const IntStringPair &search_ids, const ParamResponsePair &search_pr)

PRPQueueHIter	lookup_by_val (PRPMultiIndexQueue &prp_queue, const ParamResponsePair &search_pr)
	find a ParamResponsePair based on the interface id, variables, and ActiveSet search data within search_pr. More...

PRPQueueHIter	lookup_by_val (PRPMultiIndexQueue &prp_queue, const String &search_interface_id, const Variables &search_vars, const ActiveSet &search_set)
	find a ParamResponsePair within a PRPMultiIndexQueue based on interface id, variables, and ActiveSet search data

PRPQueueOIter	lookup_by_eval_id (PRPMultiIndexQueue &prp_queue, int search_id)
	find a ParamResponsePair within a PRPMultiIndexQueue based on search_id (i.e. integer eval_id) search data

void	print_usage (std::ostream &s)
	print restart utility help message

void	print_restart (StringArray pos_args, String print_dest)
	print a restart file More...

void	print_restart_pdb (StringArray pos_args, String print_dest)
	print a restart file (PDB format) More...

void	print_restart_tabular (StringArray pos_args, String print_dest, unsigned short tabular_format, int tabular_precision)
	print a restart file (tabular format) More...

void	read_neutral (StringArray pos_args)
	read a restart file (neutral file format) More...

void	repair_restart (StringArray pos_args, String identifier_type)
	repair a restart file by removing corrupted evaluations More...

void	concatenate_restart (StringArray pos_args)
	concatenate multiple restart files More...

String	method_results_hdf5_link_name (const StrStrSizet &iterator_id)
	Create a method results name (HDF5 link name) from iterator_id.

String	method_hdf5_link_name (const StrStrSizet &iterator_id)
	Create a method name (HDF5 link name) from iterator_id.

String	execution_hdf5_link_name (const StrStrSizet &iterator_id)
	Create an execution name (HDF5 link name) from iterator_id.

String	object_hdf5_link_name (const StrStrSizet &iterator_id, const StringArray &location)

template<typename ScaleType >
String	scale_hdf5_link_name (const StrStrSizet &iterator_id, const StringArray &location, const ScaleType &scale)
	Create a scale name (hdf5 link name) for a scale from an iterator_id, the name of the result, the name of the response (can be empty), and the scale itself.

template<typename T >
void	expand_for_fields_sdv (const SharedResponseData &srd, const T &src_array, const String &src_desc, bool allow_by_element, T &expanded_array)
	expand primary response specs in SerialDenseVectors, e.g. scales, for fields no change on empty, expands 1 and num_groups, copies num_elements

template<typename T >
void	expand_for_fields_stl (const SharedResponseData &srd, const T &src_array, const String &src_desc, bool allow_by_element, T &expanded_array)
	expand primary response specs in STL containers, e.g. scale types, for fields no change on empty, expands 1 and num_groups, copies num_elements

static HANDLE *	wait_setup (std::map< pid_t, int > M, size_t pn)

static int	wait_for_one (size_t n, HANDLE h, int req1, size_t pi)

void	gauss_legendre_pts_wts_1D (int level, RealVector &result_0, RealVector &result_1)

void	lagrange_interpolation_1d (const RealVector &samples, const RealVector &abscissa, const RealVector &values, RealVector &result)

void	kronecker_product_2d (const RealMatrix &matrix1, const RealMatrix &matrix2, RealMatrix &matrix)

void	get_chebyshev_points (int order, RealVector &points)

void	chebyshev_derivative_matrix (int order, RealMatrix &derivative_matrix, RealVector &points)

int	salinas_main (int argc, char argv[], MPI_Comm comm)
	subroutine interface to SALINAS simulation code

Real	std_normal_coverage_inverse (const Real coverage)
	Given a required coverage c \in [0,1], this routine computes the value b such that. More...

Real	computeDSTIEN_conversion_factor (const size_t number_of_samples, const Real alpha)
	This routine computes the multiplicative conversion factor to be applied to the sample standard deviation in order to get the standard deviation corresponding to the 'two sided tolerance interval equivalent normal' (DSTIEN). More...

void	computeDSTIEN (const IntResponseMap &resp_samples, const Real coverage, const Real alpha, size_t &num_valid_samples, RealVector &dstien_mus, Real &delta_mf, RealVector &sample_sigmas, RealVector &dstien_sigmas)
	This routine computes the r averages and r standard deviations corresponding to the 'two sided tolerance interval equivalent normal' (DSTIEN). More...

std::string	get_cwd_str ()

std::vector< std::string >	get_pathext ()

bool	contains (const bfs::path &dir_path, const std::string &file_name, boost::filesystem::path &complete_filepath)

Variables
PRPCache	data_pairs
	contains all parameter/response pairs.

double	PI = boost::math::constants::pi<double>()
	constant pi

double	HALF_LOG_2PI = std::log(2.0*PI)/2.0
	constant log(2*pi)/2.0

short	abort_mode = ABORT_EXITS
	by default Dakota exits or calls MPI_Abort on errors More...

std::ostream *	dakota_cout = &std::cout
	DAKOTA stdout initially points to std::cout, but may be redirected to a tagged ofstream if there are concurrent iterators.

std::ostream *	dakota_cerr = &std::cerr
	DAKOTA stderr initially points to std::cerr, but may be redirected to a tagged ofstream if there are concurrent iterators.

ResultsManager	iterator_results_db
	Global results database for iterator results.

EvaluationStore	evaluation_store_db
	Global database for evaluation storage.

int	write_precision = 10
	used in ostream data output functions (restart_util.cpp overrides default value)

MPIManager	dummy_mpi_mgr
	dummy MPIManager for ref initialization

ProgramOptions	dummy_prg_opt
	dummy ProgramOptions for ref initialization

OutputManager	dummy_out_mgr
	dummy OutputManager for ref initialization

ParallelLibrary	dummy_lib
	dummy ParallelLibrary for ref initialization

ProblemDescDB	dummy_db
	dummy ProblemDescDB for ref initialization

int	mc_ptr_int = 0
	global pointer for ModelCenter API

int	dc_ptr_int = 0
	global pointer for ModelCenter eval DB

ProblemDescDB *	Dak_pddb
	set by ProblemDescDB, for use in parsing

const size_t	SZ_MAX = std::numeric_limits<size_t>::max()
	special value returned by index() when entry not found

const size_t	_NPOS = SZ_MAX

const double	BIG_REAL_BOUND = 1.0e+30
	bound beyond which constraints are considered inactive

static UShortStrBimap	method_map
	bimap between method enums and strings; only used in this compilation unit

static UShortStrBimap	submethod_map
	bimap between sub-method enums and strings; only used in this compilation unit (using bimap for consistency, though at time of addition, only uni-directional mapping is supported) More...

Interface	dummy_interface
	dummy Interface object used for mandatory reference initialization or default virtual function return by reference when a real Interface instance is unavailable

Model	dummy_model
	dummy Model object used for mandatory reference initialization or default virtual function return by reference when a real Model instance is unavailable

Iterator	dummy_iterator
	dummy Iterator object used for mandatory reference initialization or default virtual function return by reference when a real Iterator instance is unavailable

Dakota_funcs *	DF

Dakota_funcs	DakFuncs0

const Real	REAL_DSET_FILL_VAL = NAN

const int	INT_DSET_FILL_VAL = INT_MAX

const String	STR_DSET_FILL_VAL = ""

const int	HDF5_CHUNK_SIZE = 40000

const char *	FIELD_NAMES []

const int	NUMBER_OF_FIELDS = 23

static const int	MPI_COMM_WORLD = 1

static const int	MPI_COMM_NULL = 0

static const int	MPI_COMM_SELF = 92

static const int	MPI_ANY_TAG = -1

static void *	MPI_REQUEST_NULL = NULL

FILE *	nidrin

const size_t	NIDR_MAX_ERROR_LEN = 8192
	maximum error length is roughly 100 lines at 80 char; using fixed error length instead of investing in converting to vsnprintf (C++11)

static const char *	auto_log_scaletypes [] = { "auto", "log", "none", 0 }

static Var_uinfo	CAUVLbl [CAUVar_Nkinds]

static Var_uinfo	DAUIVLbl [DAUIVar_Nkinds]

static Var_uinfo	DAUSVLbl [DAUSVar_Nkinds]

static Var_uinfo	DAURVLbl [DAURVar_Nkinds]

static Var_uinfo	CEUVLbl [CEUVar_Nkinds]

static Var_uinfo	DEUIVLbl [DEUIVar_Nkinds]

static Var_uinfo	DEUSVLbl [DEUSVar_Nkinds]

static Var_uinfo	DEURVLbl [DEURVar_Nkinds]

static Var_uinfo	DiscSetLbl [DiscSetVar_Nkinds]

static VarLabelChk	DesignAndStateLabelsCheck []
	Variables label array designations for design and state. All non-uncertain variables need to be in this array. Used in check_variables_node to check lengths and make_variable_defaults to build labels. More...

static VLreal	VLUncertainReal [NUM_UNC_REAL_CONT]
	Variables labels/bounds/values check array for real-valued uncertain variables; one array entry per contiguous container. These associate the individual variables given by, e.g., CAUVLbl, with the contiguous container in which they are stored. More...

static VLint	VLUncertainInt [NUM_UNC_INT_CONT]
	Variables labels/bounds/values check array for integer-valued uncertain variables; one array entry per contiguous container. These associate the individual variables given by, e.g., DAUIVLbl, with the contiguous container in which they are stored. More...

static VLstr	VLUncertainStr [NUM_UNC_STR_CONT]
	Variables labels/bounds/values check array for string-valued uncertain variables; one array entry per contiguous container. These associate the individual variables given by, e.g., DAUSVLbl, with the contiguous container in which they are stored. More...

static int	VLR_aleatory [NUM_UNC_REAL_CONT] = { 1, 0, 1, 0 }
	which uncertain real check array containers are aleatory (true = 1)

static int	VLI_aleatory [NUM_UNC_INT_CONT] = { 1, 0 }
	which uncertain integer check array containers are aleatory (true = 1)

static int	VLS_aleatory [NUM_UNC_STR_CONT] = { 1, 0 }
	which uncertain string check array containers are aleatory (true = 1)

static Var_check	var_mp_check_cv []

static Var_check	var_mp_check_dset []

static Var_check	var_mp_check_cau []

static Var_check	var_mp_check_daui []

static Var_check	var_mp_check_daus []

static Var_check	var_mp_check_daur []

static Var_check	var_mp_check_ceu []

static Var_check	var_mp_check_deui []

static Var_check	var_mp_check_deus []

static Var_check	var_mp_check_deur []

static Var_rcheck	var_mp_cbound []
	This is used within check_variables_node(): Var_RealBoundIPCheck() is applied to validate bounds and initial points. More...

static Var_icheck	var_mp_drange []
	This is used in check_variables_node(): Var_IntBoundIPCheck() is applied to validate bounds and initial points, and in make_variable_defaults(): Vgen_* is called to infer bounds. More...

TKFactoryDIPC	tk_factory_dipc ("dakota_dipc_tk")
	Static registration of RW TK with the QUESO TK factory.

TKFactoryDIPCLogit	tk_factory_dipclogit ("dakota_dipc_logit_tk")
	Static registration of Logit RW TK with the QUESO TK factory.

static time_t	start_time

const double	SCALING_MIN_SCALE = 1.0e10*DBL_MIN
	minimum value allowed for a characteristic value when scaling; ten orders of magnitude greater than DBL_MIN

const double	SCALING_MIN_LOG = SCALING_MIN_SCALE
	lower bound on domain of logarithm function when scaling

const double	SCALING_LOGBASE = 10.0
	logarithm base to be used when scaling

const double	SCALING_LN_LOGBASE = std::log(SCALING_LOGBASE)
	ln(SCALING_LOGBASE); needed in transforming variables in several places

const char *	SCI_FIELD_NAMES []

const int	SCI_NUMBER_OF_FIELDS = 26

const int	LARGE_SCALE = 100
	a (perhaps arbitrary) definition of large scale; choose a large-scale algorithm if numVars >= LARGE_SCALE

const double	POW_VAL = 1.0
	offset used text_book exponent: 1.0 is nominal, 1.4 used for B&B testing

const String	LEV_REF = "Dakota"
	levenshtein_distance computes the distance between its argument and this

Detailed Description

The primary namespace for DAKOTA.

The Dakota namespace encapsulates the core classes of the DAKOTA framework and prevents name clashes with third-party libraries from methods and packages. The C++ source files defining these core classes reside in Dakota/src as *.[ch]pp.

Typedef Documentation

◆ PRPMultiIndexCache

typedef bmi::multi_index_container<Dakota::ParamResponsePair, bmi::indexed_by< bmi::ordered_non_unique<bmi::tag<ordered>, bmi::const_mem_fun<Dakota::ParamResponsePair, const IntStringPair&, &Dakota::ParamResponsePair::eval_interface_ids> >, bmi::hashed_non_unique<bmi::tag<hashed>, bmi::identity<Dakota::ParamResponsePair>, partial_prp_hash, partial_prp_equality> > > PRPMultiIndexCache

Boost Multi-Index Container for globally caching ParamResponsePairs.

For a global cache, both evaluation and interface id's are used for tagging ParamResponsePair records.

◆ PRPMultiIndexQueue

typedef bmi::multi_index_container<Dakota::ParamResponsePair, bmi::indexed_by< bmi::ordered_unique<bmi::tag<ordered>, bmi::const_mem_fun<Dakota::ParamResponsePair, int, &Dakota::ParamResponsePair::eval_id> >, bmi::hashed_non_unique<bmi::tag<hashed>, bmi::identity<Dakota::ParamResponsePair>, partial_prp_hash, partial_prp_equality> > > PRPMultiIndexQueue

Boost Multi-Index Container for locally queueing ParamResponsePairs.

For a local queue, interface id's are expected to be consistent, such that evaluation id's are sufficient for tracking particular evaluations.

Enumeration Type Documentation

◆ anonymous enum [1/10]

anonymous enum

enum for selecting the models that store evaluations

Enumerator
SUBMETHOD_COLLABORATIVE	Type of hybrid meta-iterator:

◆ anonymous enum [2/10]

anonymous enum

interface synchronization types

Enumerator
SUBMETHOD_COLLABORATIVE	Type of hybrid meta-iterator:

◆ anonymous enum [3/10]

anonymous enum

Sub-methods, including sampling, inference algorithm, opt algorithm types.

Enumerator
SUBMETHOD_COLLABORATIVE	Type of hybrid meta-iterator:

◆ anonymous enum [4/10]

anonymous enum

define special values for SurrogateModel::responseMode

Enumerator
SUBMETHOD_COLLABORATIVE	Type of hybrid meta-iterator:

◆ anonymous enum [5/10]

anonymous enum

values for primary response types

Enumerator
SUBMETHOD_COLLABORATIVE	Type of hybrid meta-iterator:

◆ anonymous enum [6/10]

anonymous enum

Enumerator
SUBMETHOD_COLLABORATIVE	Type of hybrid meta-iterator:

◆ anonymous enum [7/10]

anonymous enum

Enumerator
SUBMETHOD_COLLABORATIVE	Type of hybrid meta-iterator:

◆ anonymous enum [8/10]

anonymous enum

Enumerator
SUBMETHOD_COLLABORATIVE	Type of hybrid meta-iterator:

◆ anonymous enum [9/10]

anonymous enum

Enumerator
SUBMETHOD_COLLABORATIVE	Type of hybrid meta-iterator:

◆ anonymous enum [10/10]

anonymous enum

enum indicating action on failed file operation

Enumerator
SUBMETHOD_COLLABORATIVE	Type of hybrid meta-iterator:

Function Documentation

◆ flush()

CommandShell & flush ( CommandShell & shell )

convenient shell manipulator function to "flush" the shell

global convenience function for manipulating the shell; invokes the class member flush function.

References CommandShell::flush().

Referenced by HDF5IOHelper::append_empty(), HDF5IOHelper::create_dataset(), HDF5IOHelper::create_empty_dataset(), HDF5IOHelper::create_group(), SysCallApplicInterface::spawn_analysis_to_shell(), SysCallApplicInterface::spawn_evaluation_to_shell(), SysCallApplicInterface::spawn_input_filter_to_shell(), and SysCallApplicInterface::spawn_output_filter_to_shell().

◆ apply_matrix_partial()

void Dakota::apply_matrix_partial	(	const MatrixType &	M,
		const VectorType &	v1,
		VectorType &	v2
	)

Applies a RealMatrix to a vector (or subset of vector) v1.

Optionally works with a subset of the passed vectors; applies the matrix M to the first M.numCols() entries in v1, and populates the first M.numRows entries in v2.

References abort_handler().

Referenced by apply_linear_constraints(), DakotaROLIneqConstraintsHess::applyAdjointHessian(), DakotaROLEqConstraintsHess::applyAdjointHessian(), DakotaROLIneqConstraintsGrad::applyJacobian(), DakotaROLEqConstraintsGrad::applyJacobian(), and DakotaROLObjectiveHess::hessVec().

◆ apply_matrix_transpose_partial()

void Dakota::apply_matrix_transpose_partial	(	const RealMatrix &	M,
		const VectorType &	v1,
		VectorType &	v2
	)

Applies transpose of a RealMatrix to a vector (or subset of vector) v1.

Optionally works with a subset of the passed vectors; applies the matrix M^T to the first M.numRows() entries in v1, and populates the first M.numCols() entries in v2.

References abort_handler().

Referenced by DakotaROLIneqConstraintsGrad::applyAdjointJacobian(), and DakotaROLEqConstraintsGrad::applyAdjointJacobian().

◆ abort_throw_or_exit()

void abort_throw_or_exit ( int dakota_code )

throw or exit depending on abort_mode

Throw a system_error or call std::exit, with (256 + dakota_code), where dakota_code < 0

RATIONALE: Avoid common "standard" exit codes and signals (signum.h) as well as uncaught signals / uncatchable SIGKILL which return 128

<signum> on Linux = [129, 192]

Return a value in [0,255] since some operating systems only return the 8 least significant bits, leaves [193, 255] for Dakota. This should make return codes consistent cross-platform.

References abort_mode.

Referenced by abort_handler(), and ParallelLibrary::abort_helper().

◆ register_signal_handlers()

void register_signal_handlers ( )

Tie various signal handlers to Dakota's abort_handler function.

Global function to register signal handlers at top-level.

References abort_handler().

Referenced by main().

◆ mpi_debug_hold()

void mpi_debug_hold ( )

Global function to hold Dakota processes to help with MPI debugging.

See details in code for details, depending on MPI implementation in use.

Referenced by main().

◆ abort_handler_t()

T Dakota::abort_handler_t ( int code )

Templatized abort_handler_t method that allows for convenient return from methods that otherwise have no sensible return from error clauses. Usage: MyType& method() { return abort_handler<MyType&>(-1); }

References abort_handler().

◆ svd()

void svd	(	RealMatrix &	matrix,
		RealVector &	singular_vals,
		RealMatrix &	v_trans,
		bool	compute_vectors = `true`
	)

Compute the SVD of an arbitrary matrix A = USV^T.

Uses Teuchos::LAPACK.GESVD() to compute the singular value decomposition, overwriting A with the left singular vectors U (or destroying A if compute_vectors = false); optionally returns right singular vectors in v_trans.

References abort_handler().

◆ qr()

int qr ( RealMatrix & A )

Compute an in-place QR factorization A = QR.

Uses Teuchos::LAPACK.GEQRF() to compute the QR decomposition, overwriting A with the transformations and R.

References abort_handler().

Referenced by SensAnalysisGlobal::partial_corr().

◆ qr_rsolve()

int qr_rsolve	(	const RealMatrix &	q_r,
		bool	transpose,
		RealMatrix &	rhs
	)

Perform a multiple right-hand sides Rinv * rhs solve using the R from a qr factorization.

Returns info > 0 if the matrix is singular

Uses Teuchos::LAPACK.TRTRS() to perform a triangular backsolve

References abort_handler().

Referenced by SensAnalysisGlobal::partial_corr().

◆ generate_system_seed()

int generate_system_seed ( )

clock microseconds-based random seed in [1, 1000000]

Mimics DDACE timeSeed(), which returns the trailing microseconds on the time of day clock. Historically, most algorithms opted for DDACE, Utilib, std::clock(), in that order.

Referenced by NonDWASABIBayesCalibration::calibrate(), NonDQuadrature::get_parameter_sets(), PSUADEDesignCompExp::get_parameter_sets(), FSUDesignCompExp::get_parameter_sets(), NonDSampling::initialize_sample_driver(), and NonDBayesCalibration::NonDBayesCalibration().

◆ operator!=() [1/5]

bool Dakota::operator!=	(	const ActiveSet &	set1,
		const ActiveSet &	set2
	)

inline

inequality operator for ActiveSet

inequality operator

◆ operator==() [1/2]

bool Dakota::operator==	(	const Model &	m1,
		const Model &	m2
	)

inline

equality operator for Envelope is true if same letter instance

equality operator (detect same letter instance)

References Model::modelRep.

◆ operator!=() [2/5]

bool Dakota::operator!=	(	const Model &	m1,
		const Model &	m2
	)

inline

inequality operator for Envelope is true if different letter instance

inequality operator (detect different letter instances)

References Model::modelRep.

◆ get_initial_values()

void Dakota::get_initial_values	(	const Model &	model,
		VecT &	values
	)

Adapter for copying initial continuous variables values from a Dakota Model into TPL vectors

References Model::continuous_variables(), and Model::cv().

Referenced by ROLOptimizer::set_problem().

◆ get_bounds() [1/3]

bool Dakota::get_bounds	(	const RealVector &	lower_source,
		const RealVector &	upper_source,
		VecT &	lower_target,
		VecT &	upper_target,
		Real	big_real_bound_size,
		Real	no_value
	)

Adapter for copying continuous variables data from Dakota RealVector into TPL vectors

Referenced by get_linear_constraints(), get_variable_bounds(), and ROLOptimizer::set_problem().

◆ get_bounds() [2/3]

void Dakota::get_bounds	(	const Model &	model,
		VecT &	lower_target,
		VecT &	upper_target
	)

Adapter for copying continuous variables data from a Dakota Model into TPL vectors

References Model::continuous_lower_bounds(), and Model::continuous_upper_bounds().

◆ get_bounds() [3/3]

void Dakota::get_bounds	(	const SetT &	source_set,
		VecT &	lower_target,
		VecT &	upper_target,
		int	target_offset
	)

Adapter originating from (and somewhat specialized based on) APPSOptimizer for copying discrete variables from a set-based Dakota container into TPL vectors

◆ get_mixed_bounds()

bool Dakota::get_mixed_bounds	(	const MaskType &	mask_set,
		const SetArray &	source_set,
		const Teuchos::SerialDenseVector< OrdinalType, ScalarType > &	lower_source,
		const Teuchos::SerialDenseVector< OrdinalType, ScalarType > &	upper_source,
		VectorType2 &	lower_target,
		VectorType2 &	upper_target,
		ScalarType	bigBoundSize,
		ScalarType	no_value,
		int	target_offset = `0`
	)

Adapter originating from (and somewhat specialized based on) APPSOptimizer for copying discrete integer variables data with bit masking from Dakota into TPL vectors

Referenced by get_variable_bounds().

◆ get_variable_bounds()

bool Dakota::get_variable_bounds	(	Model &	model,
		Real	big_real_bound_size,
		int	big_int_bound_size,
		typename AdapterT::VecT &	lower,
		typename AdapterT::VecT &	upper
	)

Adapter originating from (and somewhat specialized based on) APPSOptimizer for copying heterogeneous bounded data from Dakota::Variables into concatenated TPL vectors

References Model::continuous_lower_bounds(), Model::continuous_upper_bounds(), Model::cv(), Model::discrete_int_lower_bounds(), Model::discrete_int_sets(), Model::discrete_int_upper_bounds(), Model::discrete_real_lower_bounds(), Model::discrete_real_upper_bounds(), Model::discrete_set_int_values(), Model::discrete_set_real_values(), Model::discrete_set_string_values(), Model::div(), Model::drv(), get_bounds(), and get_mixed_bounds().

◆ configure_inequality_constraint_maps()

int Dakota::configure_inequality_constraint_maps	(	const Model &	model,
		Real	big_real_bound_size,
		CONSTRAINT_TYPE	ctype,
		IVecT &	map_indices,
		RVecT &	map_multipliers,
		RVecT &	map_offsets,
		Real	scaling = `1.0`
	)

Adapter for configuring inequality constraint maps used when transferring data between Dakota and a TPL

Referenced by Optimizer::configure_constraint_maps().

◆ configure_equality_constraint_maps()

void Dakota::configure_equality_constraint_maps	(	Model &	model,
		CONSTRAINT_TYPE	ctype,
		IVecT &	indices,
		size_t	index_offset,
		RVecT &	multipliers,
		RVecT &	values,
		bool	make_one_sided
	)

Adapter for configuring equality constraint maps used when transferring data between Dakota and a TPL

◆ get_linear_constraints()

void Dakota::get_linear_constraints	(	Model &	model,
		Real	big_real_bound_size,
		typename AdapterT::VecT &	lin_ineq_lower_bnds,
		typename AdapterT::VecT &	lin_ineq_upper_bnds,
		typename AdapterT::VecT &	lin_eq_targets,
		typename AdapterT::MatT &	lin_ineq_coeffs,
		typename AdapterT::MatT &	lin_eq_coeffs
	)

Adapter based initially on APPSOptimizer for linear constraint maps and including matrix and bounds data; bundles a few steps together which could (should?) be broken into two or more adapters

References copy_data(), get_bounds(), Model::linear_eq_constraint_coeffs(), Model::linear_eq_constraint_targets(), Model::linear_ineq_constraint_coeffs(), Model::linear_ineq_constraint_lower_bounds(), and Model::linear_ineq_constraint_upper_bounds().

◆ apply_linear_constraints()

void Dakota::apply_linear_constraints	(	const Model &	model,
		CONSTRAINT_EQUALITY_TYPE	etype,
		const VecT &	in_vals,
		VecT &	values,
		bool	adjoint = `false`
	)

Data adapter to transfer data from Dakota to third-party opt packages. The vector values might contain additional constraints; the first entries corresponding to linear constraints are populated by apply.

References apply_matrix_partial(), Model::linear_eq_constraint_coeffs(), Model::linear_eq_constraint_targets(), Model::linear_ineq_constraint_coeffs(), Model::num_linear_eq_constraints(), and Model::num_linear_ineq_constraints().

Referenced by DakotaROLIneqConstraints::value(), and DakotaROLEqConstraints::value().

◆ apply_nonlinear_constraints()

void Dakota::apply_nonlinear_constraints	(	const Model &	model,
		CONSTRAINT_EQUALITY_TYPE	etype,
		const VecT &	in_vals,
		VecT &	values,
		bool	adjoint = `false`
	)

Data adapter to transfer data from Dakota to third-party opt packages

If adjoint = false, (perhaps counter-intuitively) apply the Jacobian (transpose of the gradient) to in_vals, which should be of size num_continuous_vars: J*x = G'*x, resulting in num_nonlinear_const values getting populated (possibly a subset of the total constraint vector).

If adjoint = true, apply the adjoint Jacobian (gradient) to the nonlinear constraint portion of in_vals, which should be of size at least num_nonlinear_consts: J'*y = G*y, resulting in num_continuous_vars values getting populated.

References Model::current_response(), Model::cv(), Response::function_gradients(), Model::num_linear_eq_constraints(), Model::num_linear_ineq_constraints(), Model::num_nonlinear_eq_constraints(), and Model::num_nonlinear_ineq_constraints().

Referenced by DakotaROLIneqConstraintsGrad::applyAdjointJacobian(), DakotaROLEqConstraintsGrad::applyAdjointJacobian(), DakotaROLIneqConstraintsGrad::applyJacobian(), and DakotaROLEqConstraintsGrad::applyJacobian().

◆ set_best_responses()

void Dakota::set_best_responses	(	typename AdapterT::OptT &	optimizer,
		const Model &	model,
		bool	set_objectives,
		size_t	num_user_primary_fns,
		const std::vector< int >	constraint_map_indices,
		const std::vector< double >	constraint_map_multipliers,
		const std::vector< double >	constraint_map_offsets,
		ResponseArray &	response_array
	)

Data adapter for use by third-party opt packages to transfer response data to Dakota

References Model::num_nonlinear_eq_constraints(), Model::num_nonlinear_ineq_constraints(), and Model::primary_response_fn_sense().

◆ set_variables()

void Dakota::set_variables	(	const VectorType &	source,
		Model &	model,
		Variables &	vars
	)

copy appropriate slices of source vector to Dakota::Variables

References Variables::continuous_variables(), copy_data_partial(), Variables::cv(), Model::discrete_int_sets(), Variables::discrete_int_variables(), Variables::discrete_real_variables(), Model::discrete_set_int_values(), Model::discrete_set_real_values(), Model::discrete_set_string_values(), Variables::discrete_string_variable(), Variables::div(), Variables::drv(), Variables::dsv(), and set_index_to_value().

Referenced by NomadOptimizer::Evaluator::eval_x().

◆ get_variables()

void Dakota::get_variables	(	Model &	model,
		VectorType &	vec
	)

copy the various pieces comprising Dakota::Variables into a concatenated TPL vector

References abort_handler(), Model::continuous_variables(), copy_data_partial(), Model::cv(), Model::discrete_int_sets(), Model::discrete_int_variables(), Model::discrete_real_variables(), Model::discrete_set_int_values(), Model::discrete_set_real_values(), Model::discrete_set_string_values(), Model::discrete_string_variables(), Model::div(), Model::drv(), and Model::dsv().

◆ get_responses()

void Dakota::get_responses	(	const Model &	model,
		const RealVector &	dak_fn_vals,
		const std::vector< int >	constraint_map_indices,
		const std::vector< double >	constraint_map_multipliers,
		const std::vector< double >	constraint_map_offsets,
		vectorType &	f_vec,
		vectorType &	cEqs_vec,
		vectorType &	cIneqs_vec
	)

Data adapter to transfer data from Dakota to third-party opt packages

References Model::num_nonlinear_eq_constraints(), and Model::primary_response_fn_sense().

Referenced by NomadOptimizer::Evaluator::eval_x(), and Optimizer::get_responses_from_dakota().

◆ get_nonlinear_eq_constraints() [1/2]

void Dakota::get_nonlinear_eq_constraints	(	const Model &	model,
		VecT &	values,
		Real	scale,
		int	offset = `-1`
	)

Data adapter to transfer data from Dakota to third-party opt packages

References Model::current_response(), Response::function_values(), Model::nonlinear_eq_constraint_targets(), Model::num_linear_eq_constraints(), Model::num_nonlinear_eq_constraints(), and Model::num_nonlinear_ineq_constraints().

Referenced by DakotaROLEqConstraints::value().

◆ get_nonlinear_eq_constraints() [2/2]

void Dakota::get_nonlinear_eq_constraints	(	Model &	model,
		const RealVector &	curr_resp_vals,
		VecT &	values,
		Real	scale,
		int	offset = `0`
	)

Data adapter to transfer data from Dakota to third-party opt packages

References Model::nonlinear_eq_constraint_targets(), and Model::num_nonlinear_eq_constraints().

◆ get_nonlinear_ineq_constraints()

void Dakota::get_nonlinear_ineq_constraints	(	const Model &	model,
		VecT &	values
	)

Data adapter to transfer data from Dakota to third-party opt packages (ROL-specific)

References copy_data_partial(), Model::current_response(), Response::function_values(), Model::num_linear_ineq_constraints(), and Model::num_nonlinear_ineq_constraints().

◆ get_nonlinear_bounds()

void Dakota::get_nonlinear_bounds	(	Model &	model,
		VecT &	nonlin_ineq_lower,
		VecT &	nonlin_ineq_upper,
		VecT &	nonlin_eq_targets
	)

Would like to combine the previous adapter with this one (based on APPSOptimizer and COLINOptimizer) and then see how much more generalization is needed to support other TPLs like JEGA.

Data adapter to transfer data from Dakota to third-party opt packages

References copy_data(), Model::nonlinear_eq_constraint_targets(), Model::nonlinear_ineq_constraint_lower_bounds(), and Model::nonlinear_ineq_constraint_upper_bounds().

Referenced by COLINApplication::set_problem().

◆ operator!=() [3/5]

bool Dakota::operator!=	(	const Response &	resp1,
		const Response &	resp2
	)

inline

inequality operator for Response

inequality operator

◆ operator!=() [4/5]

bool Dakota::operator!=	(	const Variables &	vars1,
		const Variables &	vars2
	)

inline

inequality operator for Variables

strict inequality operator

◆ write_ordered() [1/2]

void Dakota::write_ordered	(	std::ostream &	s,
		const SizetArray &	comp_totals,
		const Teuchos::SerialDenseVector< OrdinalType, ScalarType1 > &	c_vector,
		const Teuchos::SerialDenseVector< OrdinalType, ScalarType2 > &	di_vector,
		const Teuchos::SerialDenseVector< OrdinalType, ScalarType3 > &	ds_vector,
		const Teuchos::SerialDenseVector< OrdinalType, ScalarType4 > &	dr_vector
	)

inline

free function to write Variables data vectors in input spec ordering

written for arbitrary types, but typical use will be ScalarType1 = Real, ScalarType2 = int, ScalarType3 = string, and ScalarType4 = int or Real.

Referenced by ParamStudy::pre_run().

◆ write_ordered() [2/2]

void Dakota::write_ordered	(	std::ostream &	s,
		const SizetArray &	comp_totals,
		const Teuchos::SerialDenseVector< OrdinalType, ScalarType1 > &	c_vector,
		const Teuchos::SerialDenseVector< OrdinalType, ScalarType2 > &	di_vector,
		const boost::multi_array< ScalarType3, 1 > &	ds_array,
		const Teuchos::SerialDenseVector< OrdinalType, ScalarType4 > &	dr_vector
	)

inline

free function to write Variables data vectors in input spec ordering

written for arbitrary types, but typical use will be ScalarType1 = Real, ScalarType2 = int, ScalarType3 = string, and ScalarType4 = int or Real.

◆ copy_field_data() [1/2]

void copy_field_data	(	const RealVector &	fn_vals,
		RealMatrix &	fn_grad,
		const RealSymMatrixArray &	fn_hess,
		size_t	offset,
		size_t	num_fns,
		Response &	response
	)

This assumes the souce gradient/Hessian are size less or equal to the destination response, and that the leading part is to be populated.

References Response::active_set_request_vector(), Response::function_gradient_view(), Response::function_hessian_view(), and Response::function_value().

Referenced by ExperimentData::scale_residuals().

◆ copy_field_data() [2/2]

void Dakota::copy_field_data	(	const RealVector &	fn_vals,
		RealMatrix &	fn_grad,
		const RealSymMatrixArray &	fn_hess,
		size_t	offset,
		size_t	num_fns,
		short	total_asv,
		Response &	response
	)

This assumes the souce gradient/Hessian are size less or equal to the destination response, and that the leading part is to be populated.

References Response::function_gradient_view(), Response::function_hessian_view(), and Response::function_value().

◆ symmetric_eigenvalue_decomposition()

void symmetric_eigenvalue_decomposition	(	const RealSymMatrix &	matrix,
		RealVector &	eigenvalues,
		RealMatrix &	eigenvectors
	)

Computes the eigenvalues and, optionally, eigenvectors of a real symmetric matrix A.

Eigenvalues are returned in ascending order.

References symmetric_eigenvalue_decomposition().

Referenced by NonDBayesCalibration::get_positive_definite_covariance_from_hessian(), and symmetric_eigenvalue_decomposition().

◆ getdist()

Real Dakota::getdist	(	const RealVector &	x1,
		const RealVector &	x2
	)

Gets the Euclidean distance between x1 and x2

Referenced by mindist(), and mindistindx().

◆ mindist()

Real Dakota::mindist	(	const RealVector &	x,
		const RealMatrix &	xset,
		int	except
	)

Returns the minimum distance between the point x and the points in the set xset (compares against all points in xset except point "except"): if except is not needed, pass 0.

References getdist().

Referenced by getRmax().

◆ mindistindx()

Real Dakota::mindistindx	(	const RealVector &	x,
		const RealMatrix &	xset,
		const IntArray &	indx
	)

Gets the min distance between x and points in the set xset defined by the nindx values in indx.

References getdist().

Referenced by GaussProcApproximation::pointsel_add_sel().

◆ getRmax()

Real Dakota::getRmax ( const RealMatrix & xset )

Gets the maximum of the min distance between each point and the rest of the set.

References mindist().

Referenced by GaussProcApproximation::pointsel_add_sel().

◆ start_grid_computing()

int Dakota::start_grid_computing	(	char *	analysis_driver_script,
		char *	params_file,
		char *	results_file
	)

sample function prototype for launching grid computing

◆ stop_grid_computing()

int Dakota::stop_grid_computing ( )

sample function prototype for terminating grid computing

◆ perform_analysis()

int Dakota::perform_analysis ( char * iteration_num )

sample function prototype for submitting a grid evaluation

◆ asstring()

string Dakota::asstring ( const T & val )

Creates a string from the argument val using an ostringstream.

This only gets used in this file and is only ever called with ints so no error checking is in place.

Parameters

val	The value of type T to convert to a string.

Returns: The string representation of val created using an ostringstream.

Referenced by JEGAOptimizer::LoadTheConstraints().

◆ start_dakota_heartbeat()

void start_dakota_heartbeat ( int seconds )

Heartbeat function provided by dakota_filesystem_utils; pass output interval in seconds, or -1 to use $DAKOTA_HEARTBEAT

Referenced by OutputManager::OutputManager().

◆ operator==() [2/2]

bool Dakota::operator==	(	const ParamResponsePair &	pair1,
		const ParamResponsePair &	pair2
	)

inline

equality operator for ParamResponsePair

equality operator

References ParamResponsePair::evalInterfaceIds, ParamResponsePair::prpResponse, and ParamResponsePair::prpVariables.

◆ operator!=() [5/5]

bool Dakota::operator!=	(	const ParamResponsePair &	pair1,
		const ParamResponsePair &	pair2
	)

inline

inequality operator for ParamResponsePair

inequality operator

◆ set_compare()

bool Dakota::set_compare	(	const ParamResponsePair &	database_pr,
		const ActiveSet &	search_set
	)

inline

search function for a particular ParamResponsePair within a PRPList based on ActiveSet content (request vector and derivative variables vector)

a global function to compare the ActiveSet of a particular database_pr (presumed to be in the global history list) with a passed in ActiveSet (search_set).

References ParamResponsePair::active_set(), ActiveSet::derivative_vector(), and ActiveSet::request_vector().

Referenced by lookup_by_val().

◆ id_vars_exact_compare()

bool Dakota::id_vars_exact_compare	(	const ParamResponsePair &	database_pr,
		const ParamResponsePair &	search_pr
	)

inline

search function for a particular ParamResponsePair within a PRPMultiIndex

a global function to compare the interface id and variables of a particular database_pr (presumed to be in the global history list) with a passed in key of interface id and variables provided by search_pr.

References ParamResponsePair::interface_id(), and ParamResponsePair::variables().

Referenced by partial_prp_equality::operator()().

◆ lookup_by_val() [1/2]

PRPCacheHIter Dakota::lookup_by_val	(	PRPMultiIndexCache &	prp_cache,
		const ParamResponsePair &	search_pr
	)

inline

find a ParamResponsePair based on the interface id, variables, and ActiveSet search data within search_pr.

Lookup occurs in two steps: (1) PRPMultiIndexCache lookup based on strict equality in interface id and variables, and (2) set_compare() post-processing based on ActiveSet subset logic.

References ParamResponsePair::active_set(), and set_compare().

Referenced by NonDDREAMBayesCalibration::archive_acceptance_chain(), Minimizer::archive_best_results(), DataTransformModel::archive_submodel_responses(), NonDMUQBayesCalibration::cache_chain(), NonDQUESOBayesCalibration::cache_chain(), Model::db_lookup(), ApplicationInterface::duplication_detect(), SurrBasedLocalMinimizer::find_response(), Minimizer::local_recast_retrieve(), lookup_by_val(), Minimizer::print_best_eval_ids(), DiscrepancyCorrection::search_db(), and NonDLocalReliability::update_mpp_search_data().

◆ lookup_by_val() [2/2]

PRPQueueHIter Dakota::lookup_by_val	(	PRPMultiIndexQueue &	prp_queue,
		const ParamResponsePair &	search_pr
	)

inline

find a ParamResponsePair based on the interface id, variables, and ActiveSet search data within search_pr.

Lookup occurs in two steps: (1) PRPMultiIndexQueue lookup based on strict equality in interface id and variables, and (2) set_compare() post-processing based on ActiveSet subset logic.

References ParamResponsePair::active_set(), and set_compare().

◆ print_restart()

void print_restart	(	StringArray	pos_args,
		String	print_dest
	)

print a restart file

Usage: "dakota_restart_util print dakota.rst"
"dakota_restart_util to_neutral dakota.rst dakota.neu"

Prints all evals. in full precision to either stdout or a neutral file. The former is useful for ensuring that duplicate detection is successful in a restarted run (e.g., starting a new method from the previous best), and the latter is used for translating binary files between platforms.

References abort_handler(), ParamResponsePair::eval_id(), ParamResponsePair::write_annotated(), and write_precision.

Referenced by main().

◆ print_restart_pdb()

void print_restart_pdb	(	StringArray	pos_args,
		String	print_dest
	)

print a restart file (PDB format)

Usage: "dakota_restart_util to_pdb dakota.rst dakota.pdb"

Unrolls all data associated with a particular tag for all evaluations and then writes this data in a tabular format (e.g., to a PDB database or MATLAB/TECPLOT data file).

References abort_handler(), Variables::continuous_variables(), Variables::discrete_int_variables(), Variables::discrete_real_variables(), and Response::function_values().

Referenced by main().

◆ print_restart_tabular()

void print_restart_tabular	(	StringArray	pos_args,
		String	print_dest,
		unsigned short	tabular_format,
		int	tabular_precision
	)

print a restart file (tabular format)

Usage: "dakota_restart_util to_tabular dakota.rst dakota.txt"

Unrolls all data associated with a particular tag for all evaluations and then writes this data in a tabular format (e.g., to a PDB database or MATLAB/TECPLOT data file).

References abort_handler(), Variables::acv(), Variables::adiv(), Variables::adrv(), Variables::adsv(), Variables::all_continuous_variable_labels(), Variables::all_discrete_int_variable_labels(), Variables::all_discrete_real_variable_labels(), Variables::all_discrete_string_variable_labels(), Response::function_labels(), ParamResponsePair::interface_id(), ParamResponsePair::response(), ParamResponsePair::variables(), write_precision, ParamResponsePair::write_tabular(), and ParamResponsePair::write_tabular_labels().

Referenced by main().

◆ read_neutral()

void read_neutral ( StringArray pos_args )

read a restart file (neutral file format)

Usage: "dakota_restart_util from_neutral dakota.neu dakota.rst"

Reads evaluations from a neutral file. This is used for translating binary files between platforms.

References abort_handler(), and ParamResponsePair::read_annotated().

Referenced by main().

◆ repair_restart()

void repair_restart	(	StringArray	pos_args,
		String	identifier_type
	)

repair a restart file by removing corrupted evaluations

Usage: "dakota_restart_util remove 0.0 dakota_old.rst dakota_new.rst"
"dakota_restart_util remove_ids 2 7 13 dakota_old.rst dakota_new.rst"

Repairs a restart file by removing corrupted evaluations. The identifier for evaluation removal can be either a double precision number (all evaluations having a matching response function value are removed) or a list of integers (all evaluations with matching evaluation ids are removed).

References abort_handler(), Response::active_set_request_vector(), contains(), ParamResponsePair::eval_id(), Response::function_values(), and ParamResponsePair::response().

Referenced by main().

◆ concatenate_restart()

void concatenate_restart ( StringArray pos_args )

concatenate multiple restart files

Usage: "dakota_restart_util cat dakota_1.rst ... dakota_n.rst dakota_new.rst"

Combines multiple restart files into a single restart database.

References abort_handler().

Referenced by main().

◆ std_normal_coverage_inverse()

Real std_normal_coverage_inverse ( const Real coverage )

Given a required coverage c \in [0,1], this routine computes the value b such that.

   F(b) = \int_{-b}^{b} (1/sqrt(2\pi)) * exp{-x*x/2) dx = c

   There are two ways to compute b:
   1) b is the value such that \int_{-\infty}^{b} (1/sqrt(2\pi)) * exp{-x*x/2) dx = (1+c)/2.
      This is the approach used when calling the python scipy stats.norm.ppf((1+c)/2) routine.
   2) c = F(b) = 2 * \int_{0}^{b} (1/sqrt(2\pi)) * exp{-x*x/2) dx = erf(b/sqrt(2)), that is,
      b = sqrt(2) * erf_inv(c). This is the approach used in this DAKOTA routine.

   If c \in [0,1], this routine returns b, otherwise it aborts/throws an exception.

Parameters

[in] coverage = the required coverage level c above

return = the value b explained above

References abort_handler().

Referenced by computeDSTIEN().

◆ computeDSTIEN_conversion_factor()

Real computeDSTIEN_conversion_factor	(	const size_t	number_of_samples,
		const Real	alpha
	)

This routine computes the multiplicative conversion factor to be applied to the sample standard deviation in order to get the standard deviation corresponding to the 'two sided tolerance interval equivalent normal' (DSTIEN).

More especifically, this routine expects the following input information:

m, the number of samples;
alpha, a value in the closed interval [0,1] such that (1-alpha) is the required confidence level.

The input information must satisty the following conditions, otherwise the routine aborts/throws an exception:

m >= 2;
alpha \in [0,1].

If all input information is valid, then this routine returns the following value 'mcf' for the multiplicative conversion factor:

mcf = sqrt(1. + 1./m)
- sqrt( (m - 1.) / quant )
- sqrt( 1. + (m - 3. - quant) / (2.*(m+1.)*(m+1.)) ), where:
quant is the alpha-quantile of a chi-square random variable with m-1 degrees of freedom.

See: Charles F. Jekel and Vicente Romero, "Conservative and Efficient Tail Probability Estimation from Sparse Sample Data", Sandia Report SAND2020-7572J, equations (4) and (5).

Parameters

[in]	number_of_samples	= the value 'm' on the text above
[in]	alpha	= the value such that (1-alpha) is the required confidence level

return = value of the multiplicative conversion factor

References abort_handler().

Referenced by computeDSTIEN().

◆ computeDSTIEN()

void computeDSTIEN	(	const IntResponseMap &	resp_samples,
		const Real	coverage,
		const Real	alpha,
		size_t &	num_valid_samples,
		RealVector &	dstien_mus,
		Real &	delta_mf,
		RealVector &	sample_sigmas,
		RealVector &	dstien_sigmas
	)

This routine computes the r averages and r standard deviations corresponding to the 'two sided tolerance interval equivalent normal' (DSTIEN).

More especifically, this routine expects the following input information:

n >= 2 response samples, each sample being a vector of r >= 1 responses;
c, the required coverage level;
a value alpha in the closed interval [0,1] such that (1-alpha) is the required confidence level.

The input information must satisty the following conditions, otherwise the routine aborts/throws an exception:

n >= 2;
r >= 1, where r is the number of responses in the first response sample;
all remaining (n-1) response samples must have the same number r of resposens;
c \in [0,1];
alpha \in [0,1];

The output vectors dstien_mus and dstien_sigmas will have size r on output (that is, they are internally resized if they are not supplied with size r)

If all input information is valid, then this routine selects the m <= n response samples that are valid (meaning, all r responses are finite values):

if m == 0, this routine returns:
- all r averages equal to std::numeric_limits<Real>::quiet_NaN(),
- the delta_mf equal to std::numeric_limits<Real>::quiet_NaN(),
- all r sample std deviations equal to std::numeric_limits<Real>::quiet_NaN(),
- all r DSTIEN std deviations equal to std::numeric_limits<Real>::quiet_NaN();
if m == 1, this routine returns:
- the r averages equal to the only valid sample,
- the delta_mf equal to std::numeric_limits<Real>::quiet_NaN(),
- all r sample std deviations equal to std::numeric_limits<Real>::quiet_NaN(),
- all r DSTIEN std deviations equal to std::numeric_limits<Real>::quiet_NaN();
if m >= 2, this routine uses such m response samples to compute:
- the r averages dstien_mus,
- delta_mf = computeDSTIEN_conversion_factor(m, alpha)
  - std_normal_coverage_inverse(c), so that the tolerance intervals are given by [dstien_mus - delta_mf*sample_sigmas, dstien_mus + delta_mf*sample_sigmas]
- the r sample standard deviations sample_sigmas,
- the r DSTIEN standard deviations dstien_sigmas = computeDSTIEN_conversion_factor(m, alpha) * sample_sigmas.

Parameters

[in]	resp_samples	= the set of n response samples, each sample with r components
[in]	coverage	= the required coverage level c above
[in]	alpha	= the value such that (1-alpha) is the required confidence level
[out]	num_valid_samples	= number of valid samples ('m' on the text above) used to compute 'ties_mus' and 'dstien_sigmas'
[out]	dstien_mus	= the r averages
[out]	delta_mf	= the multiplicative factor explained above
[out]	sample_sigmas	= the r sample standard deviations
[out]	dstien_sigmas	= the r DSTIEN standard deviations

References abort_handler(), computeDSTIEN_conversion_factor(), and std_normal_coverage_inverse().

Referenced by NonDSampling::compute_statistics().

◆ get_pathext()

std::vector<std::string> Dakota::get_pathext ( )

Utility function for executable file search algorithms

Referenced by WorkdirHelper::which().

◆ contains()

bool Dakota::contains	(	const bfs::path &	dir_path,
		const std::string &	file_name,
		boost::filesystem::path &	complete_filepath
	)

inline

Utility function for "which" sets complete_filepath from dir_path/file_name combo

Variable Documentation

◆ abort_mode

short abort_mode = ABORT_EXITS

by default Dakota exits or calls MPI_Abort on errors

whether dakota exits/aborts or throws on errors

Referenced by abort_throw_or_exit(), Environment::exit_mode(), and PythonInterface::python_run().

◆ submethod_map

UShortStrBimap submethod_map

static

Initial value:

=
  boost::assign::list_of<UShortStrBimap::relation>
  (HYBRID,                      "hybrid")
  (SUBMETHOD_COLLABORATIVE,     "collaborative")
  (SUBMETHOD_EMBEDDED,          "embedded")
  (SUBMETHOD_SEQUENTIAL,        "sequential")
  (SUBMETHOD_LHS,               "lhs")
  (SUBMETHOD_RANDOM,            "random")
  (SUBMETHOD_BOX_BEHNKEN,       "box_behnken")
  (SUBMETHOD_CENTRAL_COMPOSITE, "central_composite")
  (SUBMETHOD_GRID,              "grid")
  (SUBMETHOD_OA_LHS,            "oa_lhs")
  (SUBMETHOD_OAS,               "oas")
  (SUBMETHOD_ACV_IS,            "acv_is")
  (SUBMETHOD_ACV_MF,            "acv_mf")
  (SUBMETHOD_ACV_RD,            "acv_rd")
  (SUBMETHOD_DREAM,             "dream")
  (SUBMETHOD_WASABI,            "wasabi")
  (SUBMETHOD_GPMSA,             "gpmsa")
  (SUBMETHOD_MUQ,               "muq")
  (SUBMETHOD_QUESO,             "queso")
  (SUBMETHOD_OPTPP,             "nip")
  (SUBMETHOD_NPSOL,             "sqp")
  (SUBMETHOD_EA,                "ea")
  (SUBMETHOD_EGO,               "ego")
  (SUBMETHOD_SBGO,              "sbgo")
  (SUBMETHOD_CONVERGE_ORDER,    "converge_order")
  (SUBMETHOD_CONVERGE_QOI,      "converge_qoi")
  (SUBMETHOD_ESTIMATE_ORDER,    "estimate_order")

bimap between sub-method enums and strings; only used in this compilation unit (using bimap for consistency, though at time of addition, only uni-directional mapping is supported)

Referenced by Iterator::submethod_enum_to_string().

◆ DakFuncs0

Dakota_funcs DakFuncs0

Initial value:

= {
        fprintf,
        abort_handler,
        dlsolver_option,
        continuous_lower_bounds1,
        continuous_upper_bounds1,
        nonlinear_ineq_constraint_lower_bounds1,
        nonlinear_ineq_constraint_upper_bounds1,
        nonlinear_eq_constraint_targets1,
        linear_ineq_constraint_lower_bounds1,
        linear_ineq_constraint_upper_bounds1,
        linear_eq_constraint_targets1,
        linear_ineq_constraint_coeffs1,
        linear_eq_constraint_coeffs1,
        ComputeResponses1,
        GetFuncs1,
        GetGrads1,
        GetContVars1,
        SetBestContVars1,
        SetBestDiscVars1,
        SetBestRespFns1,
        Get_Real1,
        Get_Int1,
        Get_Bool1
        }

◆ FIELD_NAMES

const char* FIELD_NAMES[]

Initial value:

= { "numFns", "numVars", "numACV", "numADIV",  
                              "numADRV", "numDerivVars", "xC", "xDI",    
                              "xDR", "xCLabels", "xDILabels",            
                              "xDRLabels", "directFnASV", "directFnDVV", 
                              "fnFlag", "gradFlag", "hessFlag",          
                              "fnVals",  "fnGrads",  "fnHessians",       
                              "fnLabels", "failure", "currEvalId" }

fields to pass to Matlab in Dakota structure

Referenced by MatlabInterface::matlab_engine_run(), and MatlabInterface::MatlabInterface().

◆ NUMBER_OF_FIELDS

const int NUMBER_OF_FIELDS = 23

number of fields in above structure

Referenced by MatlabInterface::matlab_engine_run(), and MatlabInterface::MatlabInterface().

◆ CAUVLbl

Var_uinfo CAUVLbl[CAUVar_Nkinds]

static

Initial value:

= {
  VarLabelInfo(nuv_, NormalUnc),
  VarLabelInfo(lnuv_, LognormalUnc),
  VarLabelInfo(uuv_, UniformUnc),
  VarLabelInfo(luuv_, LoguniformUnc),
  VarLabelInfo(tuv_, TriangularUnc),
  VarLabelInfo(euv_, ExponentialUnc),
  VarLabelInfo(beuv_, BetaUnc),
  VarLabelInfo(gauv_, GammaUnc),
  VarLabelInfo(guuv_, GumbelUnc),
  VarLabelInfo(fuv_, FrechetUnc),
  VarLabelInfo(wuv_, WeibullUnc),
  VarLabelInfo(hbuv_, HistogramBinUnc)
}

◆ DAUIVLbl

Var_uinfo DAUIVLbl[DAUIVar_Nkinds]

static

Initial value:

= {
  VarLabelInfo(puv_, PoissonUnc),
  VarLabelInfo(biuv_, BinomialUnc),
  VarLabelInfo(nbuv_, NegBinomialUnc),
  VarLabelInfo(geuv_, GeometricUnc),
  VarLabelInfo(hguv_, HyperGeomUnc),
  VarLabelInfo(hpiuv_, HistogramPtIntUnc)
}

◆ DAUSVLbl

Var_uinfo DAUSVLbl[DAUSVar_Nkinds]

static

Initial value:

= {
  VarLabelInfo(hpsuv_, HistogramPtStrUnc)
}

◆ DAURVLbl

Var_uinfo DAURVLbl[DAURVar_Nkinds]

static

Initial value:

= {
  VarLabelInfo(hpruv_, HistogramPtRealUnc)
}

◆ CEUVLbl

Var_uinfo CEUVLbl[CEUVar_Nkinds]

static

Initial value:

= {
  VarLabelInfo(ciuv_, ContinuousIntervalUnc)
}

◆ DEUIVLbl

Var_uinfo DEUIVLbl[DEUIVar_Nkinds]

static

Initial value:

= {
  VarLabelInfo(diuv_, DiscreteIntervalUnc),
  VarLabelInfo(dusiv_, DiscreteUncSetInt)
}

◆ DEUSVLbl

Var_uinfo DEUSVLbl[DEUSVar_Nkinds]

static

Initial value:

= {
  VarLabelInfo(dussv_, DiscreteUncSetStr)
}

◆ DEURVLbl

Var_uinfo DEURVLbl[DEURVar_Nkinds]

static

Initial value:

= {
  VarLabelInfo(dusrv_, DiscreteUncSetReal)
}

◆ DiscSetLbl

Var_uinfo DiscSetLbl[DiscSetVar_Nkinds]

static

Initial value:

= {
  VarLabelInfo(ddsiv_, DiscreteDesSetInt),
  VarLabelInfo(ddssv_, DiscreteDesSetStr),
  VarLabelInfo(ddsrv_, DiscreteDesSetReal),
  VarLabelInfo(dssiv_, DiscreteStateSetInt),
  VarLabelInfo(dsssv_, DiscreteStateSetStr),
  VarLabelInfo(dssrv_, DiscreteStateSetReal)
}

◆ DesignAndStateLabelsCheck

VarLabelChk DesignAndStateLabelsCheck[]

static

Initial value:

= {
  { AVI numContinuousDesVars, AVI continuousDesignLabels, "cdv_", "cdv_descriptors" },
  { AVI numDiscreteDesRangeVars, AVI discreteDesignRangeLabels, "ddriv_", "ddriv_descriptors" },
  { AVI numDiscreteDesSetIntVars, AVI discreteDesignSetIntLabels, "ddsiv_", "ddsiv_descriptors" },
  { AVI numDiscreteDesSetStrVars, AVI discreteDesignSetStrLabels, "ddssv_", "ddssv_descriptors" },
  { AVI numDiscreteDesSetRealVars, AVI discreteDesignSetRealLabels, "ddsrv_", "ddsrv_descriptors" },
  { AVI numContinuousStateVars, AVI continuousStateLabels, "csv_", "csv_descriptors" },
  { AVI numDiscreteStateRangeVars, AVI discreteStateRangeLabels, "dsriv_", "dsriv_descriptors" },
  { AVI numDiscreteStateSetIntVars, AVI discreteStateSetIntLabels, "dssiv_", "dssiv_descriptors" },
  { AVI numDiscreteStateSetStrVars, AVI discreteStateSetStrLabels, "dsssv_", "dsssv_descriptors" },
  { AVI numDiscreteStateSetRealVars, AVI discreteStateSetRealLabels, "dssrv_", "dssrv_descriptors" },
  { AVI numContinuousDesVars, AVI continuousDesignScaleTypes, 0, "cdv_scale_types" }
}

Variables label array designations for design and state. All non-uncertain variables need to be in this array. Used in check_variables_node to check lengths and make_variable_defaults to build labels.

Referenced by NIDRProblemDescDB::check_variables_node(), and NIDRProblemDescDB::make_variable_defaults().

◆ VLUncertainReal

VLreal VLUncertainReal[NUM_UNC_REAL_CONT]

static

Initial value:

= {
 {CAUVar_Nkinds,  AVI CAUv,  CAUVLbl,
        DVR continuousAleatoryUncLabels,
        DVR continuousAleatoryUncLowerBnds,
        DVR continuousAleatoryUncUpperBnds,
        DVR continuousAleatoryUncVars},
 {CEUVar_Nkinds,  AVI CEUv,  CEUVLbl,
        DVR continuousEpistemicUncLabels,
        DVR continuousEpistemicUncLowerBnds,
        DVR continuousEpistemicUncUpperBnds,
        DVR continuousEpistemicUncVars},
 {DAURVar_Nkinds, AVI DAURv, DAURVLbl,
        DVR discreteRealAleatoryUncLabels,
        DVR discreteRealAleatoryUncLowerBnds,
        DVR discreteRealAleatoryUncUpperBnds,
        DVR discreteRealAleatoryUncVars},
 {DEURVar_Nkinds, AVI DEURv, DEURVLbl,
        DVR discreteRealEpistemicUncLabels,
        DVR discreteRealEpistemicUncLowerBnds,
        DVR discreteRealEpistemicUncUpperBnds,
        DVR discreteRealEpistemicUncVars}}

Variables labels/bounds/values check array for real-valued uncertain variables; one array entry per contiguous container. These associate the individual variables given by, e.g., CAUVLbl, with the contiguous container in which they are stored.

Referenced by NIDRProblemDescDB::check_variables_node(), and NIDRProblemDescDB::make_variable_defaults().

◆ VLUncertainInt

VLint VLUncertainInt[NUM_UNC_INT_CONT]

static

Initial value:

= {
 {DAUIVar_Nkinds, AVI DAUIv, DAUIVLbl,
        DVR discreteIntAleatoryUncLabels,
        DVR discreteIntAleatoryUncLowerBnds,
        DVR discreteIntAleatoryUncUpperBnds,
        DVR discreteIntAleatoryUncVars},
 {DEUIVar_Nkinds, AVI DEUIv, DEUIVLbl,
        DVR discreteIntEpistemicUncLabels,
        DVR discreteIntEpistemicUncLowerBnds,
        DVR discreteIntEpistemicUncUpperBnds,
        DVR discreteIntEpistemicUncVars}}

Variables labels/bounds/values check array for integer-valued uncertain variables; one array entry per contiguous container. These associate the individual variables given by, e.g., DAUIVLbl, with the contiguous container in which they are stored.

Referenced by NIDRProblemDescDB::check_variables_node(), and NIDRProblemDescDB::make_variable_defaults().

◆ VLUncertainStr

VLstr VLUncertainStr[NUM_UNC_STR_CONT]

static

Initial value:

= {
 {DAUSVar_Nkinds, AVI DAUSv, DAUSVLbl,
        DVR discreteStrAleatoryUncLabels,
        DVR discreteStrAleatoryUncLowerBnds,
        DVR discreteStrAleatoryUncUpperBnds,
        DVR discreteStrAleatoryUncVars},
 {DEUSVar_Nkinds, AVI DEUSv, DEUSVLbl,
        DVR discreteStrEpistemicUncLabels,
        DVR discreteStrEpistemicUncLowerBnds,
        DVR discreteStrEpistemicUncUpperBnds,
        DVR discreteStrEpistemicUncVars}}

Variables labels/bounds/values check array for string-valued uncertain variables; one array entry per contiguous container. These associate the individual variables given by, e.g., DAUSVLbl, with the contiguous container in which they are stored.

Referenced by NIDRProblemDescDB::check_variables_node(), and NIDRProblemDescDB::make_variable_defaults().

◆ var_mp_check_cv

Var_check var_mp_check_cv[]

static

Initial value:

= {
        Vchk_3(continuous_design,ContinuousDes),
        Vchk_3(continuous_state,ContinuousState) }

◆ var_mp_check_dset

Var_check var_mp_check_dset[]

static

Initial value:

= {
        Vchk_3(discrete_design_set_integer,DiscreteDesSetInt),
        Vchk_3(discrete_design_set_string,DiscreteDesSetStr),
        Vchk_3(discrete_design_set_real,DiscreteDesSetReal),
        Vchk_3(discrete_state_set_integer,DiscreteStateSetInt),
        Vchk_3(discrete_state_set_string,DiscreteStateSetStr),
        Vchk_3(discrete_state_set_real,DiscreteStateSetReal) }

◆ var_mp_check_cau

Var_check var_mp_check_cau[]

static

Initial value:

= {
        Vchk_3(normal_uncertain,NormalUnc),
        Vchk_3(lognormal_uncertain,LognormalUnc),
        Vchk_3(uniform_uncertain,UniformUnc),
        Vchk_3(loguniform_uncertain,LoguniformUnc),
        Vchk_3(triangular_uncertain,TriangularUnc),
        Vchk_3(exponential_uncertain,ExponentialUnc),
        Vchk_3(beta_uncertain,BetaUnc),
        Vchk_3(gamma_uncertain,GammaUnc),
        Vchk_3(gumbel_uncertain,GumbelUnc),
        Vchk_3(frechet_uncertain,FrechetUnc),
        Vchk_3(weibull_uncertain,WeibullUnc),
        Vchk_3(histogram_bin_uncertain,HistogramBinUnc) }

◆ var_mp_check_daui

Var_check var_mp_check_daui[]

static

Initial value:

= {
        Vchk_3(poisson_uncertain,PoissonUnc),
        Vchk_3(binomial_uncertain,BinomialUnc),
        Vchk_3(negative_binomial_uncertain,NegBinomialUnc),
        Vchk_3(geometric_uncertain,GeometricUnc),
        Vchk_3(hypergeometric_uncertain,HyperGeomUnc),
        Vchk_3(histogram_point_int_uncertain,HistogramPtIntUnc) }

◆ var_mp_check_daus

Var_check var_mp_check_daus[]

static

Initial value:

= {

Vchk_3(histogram_point_str_uncertain,HistogramPtStrUnc) }

◆ var_mp_check_daur

Var_check var_mp_check_daur[]

static

Initial value:

= {

Vchk_3(histogram_point_real_uncertain,HistogramPtRealUnc) }

◆ var_mp_check_ceu

Var_check var_mp_check_ceu[]

static

Initial value:

= {

Vchk_3(continuous_interval_uncertain,ContinuousIntervalUnc) }

◆ var_mp_check_deui

Var_check var_mp_check_deui[]

static

Initial value:

= {
        Vchk_3(discrete_interval_uncertain,DiscreteIntervalUnc),
        Vchk_3(discrete_uncertain_set_integer,DiscreteUncSetInt) }

◆ var_mp_check_deus

Var_check var_mp_check_deus[]

static

Initial value:

= {

Vchk_3(discrete_uncertain_set_string,DiscreteUncSetStr) }

◆ var_mp_check_deur

Var_check var_mp_check_deur[]

static

Initial value:

= {

Vchk_3(discrete_uncertain_set_real,DiscreteUncSetReal) }

◆ var_mp_cbound

Var_rcheck var_mp_cbound[]

static

Initial value:

= {
        Vchk_7(continuous_design,ContinuousDes,continuousDesign),
        Vchk_7(continuous_state,ContinuousState,continuousState),
        
        
        
        Vchk_5(normal_uncertain,NormalUnc,normalUnc),
        Vchk_5(lognormal_uncertain,LognormalUnc,lognormalUnc),
        Vchk_5(uniform_uncertain,UniformUnc,uniformUnc),
        Vchk_5(loguniform_uncertain,LoguniformUnc,loguniformUnc),
        Vchk_5(triangular_uncertain,TriangularUnc,triangularUnc),
        Vchk_5(beta_uncertain,BetaUnc,betaUnc) }

This is used within check_variables_node(): Var_RealBoundIPCheck() is applied to validate bounds and initial points.

Referenced by NIDRProblemDescDB::check_variables_node().

◆ var_mp_drange

Var_icheck var_mp_drange[]

static

Initial value:

= {
        Vchk_7(discrete_design_range,DiscreteDesRange,discreteDesignRange),
        Vchk_7(discrete_state_range,DiscreteStateRange,discreteStateRange) }

This is used in check_variables_node(): Var_IntBoundIPCheck() is applied to validate bounds and initial points, and in make_variable_defaults(): Vgen_* is called to infer bounds.

Referenced by NIDRProblemDescDB::check_variables_node(), and NIDRProblemDescDB::make_variable_defaults().

◆ SCI_FIELD_NAMES

const char* SCI_FIELD_NAMES[]

Initial value:

= { "dakota_type", "numFns", "numVars", "numACV", "numADIV",  
                                  "numADRV", "numDerivVars", "xC", "xDI",    
                                  "xDR", "xCLabels", "xDILabels",            
                                  "xDRLabels", "directFnASV", "directFnASM", 
                                  "directFnDVV", "directFnDVV_bool",         
                                  "fnFlag", "gradFlag", "hessFlag",          
                                  "fnVals",  "fnGrads",  "fnHessians",       
                                  "fnLabels", "failure", "currEvalId" }

fields to pass to Scilab in Dakota structure

Referenced by ScilabInterface::scilab_engine_run().

◆ SCI_NUMBER_OF_FIELDS

const int SCI_NUMBER_OF_FIELDS = 26

number of fields in above structure

Referenced by ScilabInterface::scilab_engine_run().

Classes

Typedefs

Enumerations

Functions

Variables

Detailed Description

Typedef Documentation

◆ PRPMultiIndexCache

◆ PRPMultiIndexQueue

Enumeration Type Documentation

◆ anonymous enum [1/10]

◆ anonymous enum [2/10]

◆ anonymous enum [3/10]

◆ anonymous enum [4/10]

◆ anonymous enum [5/10]

◆ anonymous enum [6/10]

◆ anonymous enum [7/10]

◆ anonymous enum [8/10]

◆ anonymous enum [9/10]

◆ anonymous enum [10/10]

Function Documentation

◆ flush()

◆ apply_matrix_partial()

◆ apply_matrix_transpose_partial()

◆ abort_throw_or_exit()

◆ register_signal_handlers()

◆ mpi_debug_hold()

◆ abort_handler_t()

◆ svd()

◆ qr()

◆ qr_rsolve()

◆ generate_system_seed()

◆ operator!=() [1/5]

◆ operator==() [1/2]

◆ operator!=() [2/5]

◆ get_initial_values()

◆ get_bounds() [1/3]

◆ get_bounds() [2/3]

◆ get_bounds() [3/3]

◆ get_mixed_bounds()

◆ get_variable_bounds()

◆ configure_inequality_constraint_maps()

◆ configure_equality_constraint_maps()

◆ get_linear_constraints()

◆ apply_linear_constraints()

◆ apply_nonlinear_constraints()

◆ set_best_responses()

◆ set_variables()

◆ get_variables()

◆ get_responses()

◆ get_nonlinear_eq_constraints() [1/2]

◆ get_nonlinear_eq_constraints() [2/2]

◆ get_nonlinear_ineq_constraints()

◆ get_nonlinear_bounds()

◆ operator!=() [3/5]

◆ operator!=() [4/5]

◆ write_ordered() [1/2]

◆ write_ordered() [2/2]

◆ copy_field_data() [1/2]

◆ copy_field_data() [2/2]

◆ symmetric_eigenvalue_decomposition()

◆ getdist()

◆ mindist()

◆ mindistindx()

◆ getRmax()

◆ start_grid_computing()

◆ stop_grid_computing()

◆ perform_analysis()

◆ asstring()

◆ start_dakota_heartbeat()

◆ operator==() [2/2]

◆ operator!=() [5/5]

◆ set_compare()

◆ id_vars_exact_compare()

◆ lookup_by_val() [1/2]

◆ lookup_by_val() [2/2]

◆ print_restart()

◆ print_restart_pdb()

◆ print_restart_tabular()

◆ read_neutral()