EffGlobalMinimizer Class Reference

Inheritance diagram for EffGlobalMinimizer:

Public Member Functions
	EffGlobalMinimizer (ProblemDescDB &problem_db, Model &model)
	standard constructor
	EffGlobalMinimizer (Model &model, const String &approx_type, int samples, int seed, bool use_derivs, size_t max_iter, size_t max_eval, Real conv_tol)
	alternate constructor for instantiations "on the fly"
	~EffGlobalMinimizer ()
	destructor
void	pre_run ()
	pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori More...
void	core_run ()
	core portion of run; implemented by all derived classes and may include pre/post steps in lieu of separate pre/post More...
void	post_run (std::ostream &s)
	post-run portion of run (optional); verbose to print results; re-implemented by Iterators that can read all Variables/Responses and perform final analysis phase in a standalone way More...
const Model &	algorithm_space_model () const
Public Member Functions inherited from Minimizer
void	constraint_tolerance (Real constr_tol)
	set the method constraint tolerance (constraintTol)
Real	constraint_tolerance () const
	return the method constraint tolerance (constraintTol)
std::shared_ptr< TPLDataTransfer >	get_data_transfer_helper () const
bool	resize ()
	reinitializes iterator based on new variable size
Public Member Functions inherited from Iterator
	Iterator (std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	default constructor More...
	Iterator (ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	standard envelope constructor, which constructs its own model(s) More...
	Iterator (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate envelope constructor which uses the ProblemDescDB but accepts a model from a higher level (meta-iterator) context, instead of constructing its own More...
	Iterator (const String &method_string, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate envelope constructor for instantiations by name without the ProblemDescDB More...
	Iterator (const Iterator &iterator)
	copy constructor More...
virtual	~Iterator ()
	destructor
Iterator	operator= (const Iterator &iterator)
	assignment operator
virtual void	pre_output ()
	write variables to file, following pre-run
virtual void	post_input ()
	read tabular data for post-run mode
virtual void	reset ()
	restore initial state for repeated sub-iterator executions
virtual void	nested_variable_mappings (const SizetArray &c_index1, const SizetArray &di_index1, const SizetArray &ds_index1, const SizetArray &dr_index1, const ShortArray &c_target2, const ShortArray &di_target2, const ShortArray &ds_target2, const ShortArray &dr_target2)
	set primaryA{CV,DIV,DRV}MapIndices, secondaryA{CV,DIV,DRV}MapTargets within derived Iterators; supports computation of higher-level sensitivities in nested contexts (e.g., derivatives of statistics w.r.t. inserted design variables)
virtual void	nested_response_mappings (const RealMatrix &primary_coeffs, const RealMatrix &secondary_coeffs)
	set primaryResponseCoefficients, secondaryResponseCoefficients within derived Iterators; Necessary for scalarization case in MLMC NonDMultilevelSampling to map scalarization in nested context
virtual void	initialize_iterator (int job_index)
	used by IteratorScheduler to set the starting data for a run
virtual void	pack_parameters_buffer (MPIPackBuffer &send_buffer, int job_index)
	used by IteratorScheduler to pack starting data for an iterator run
virtual void	unpack_parameters_buffer (MPIUnpackBuffer &recv_buffer, int job_index)
	used by IteratorScheduler to unpack starting data for an iterator run
virtual void	unpack_parameters_initialize (MPIUnpackBuffer &recv_buffer, int job_index)
	used by IteratorScheduler to unpack starting data and initialize an iterator run
virtual void	pack_results_buffer (MPIPackBuffer &send_buffer, int job_index)
	used by IteratorScheduler to pack results data from an iterator run
virtual void	unpack_results_buffer (MPIUnpackBuffer &recv_buffer, int job_index)
	used by IteratorScheduler to unpack results data from an iterator run
virtual void	update_local_results (int job_index)
	used by IteratorScheduler to update local results arrays
virtual const Variables &	variables_results () const
	return a single final iterator solution (variables)
virtual const Response &	response_results () const
	return a single final iterator solution (response)
virtual const VariablesArray &	variables_array_results ()
	return multiple final iterator solutions (variables). This should only be used if returns_multiple_points() returns true.
virtual const ResponseArray &	response_array_results ()
	return multiple final iterator solutions (response). This should only be used if returns_multiple_points() returns true.
virtual void	response_results_active_set (const ActiveSet &set)
	set the requested data for the final iterator response results
virtual const RealSymMatrix &	response_error_estimates () const
	return error estimates associated with the final iterator solution
virtual bool	accepts_multiple_points () const
	indicates if this iterator accepts multiple initial points. Default return is false. Override to return true if appropriate.
virtual bool	returns_multiple_points () const
	indicates if this iterator returns multiple final points. Default return is false. Override to return true if appropriate.
virtual void	initial_point (const Variables &pt)
	sets the initial point for this iterator (user-functions mode for which Model updating is not used)
virtual void	initial_point (const RealVector &pt)
	sets the initial point (active continuous variables) for this iterator (user-functions mode for which Model updating is not used)
virtual void	initial_points (const VariablesArray &pts)
	sets the multiple initial points for this iterator. This should only be used if accepts_multiple_points() returns true.
virtual void	update_callback_data (const RealVector &cv_initial, const RealVector &cv_lower_bnds, const RealVector &cv_upper_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_lb, const RealVector &lin_ineq_ub, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgt, const RealVector &nln_ineq_lb, const RealVector &nln_ineq_ub, const RealVector &nln_eq_tgt)
	assign variable values and bounds and constraint coefficients and bounds for this iterator (user-functions mode for which iteratedModel is null)
virtual const RealMatrix &	callback_linear_ineq_coefficients () const
	return linear constraint coefficients for this iterator (user-functions mode for which iteratedModel is null)
virtual const RealVector &	callback_linear_ineq_lower_bounds () const
	return linear constraint lower bounds for this iterator (user-functions mode for which iteratedModel is null)
virtual const RealVector &	callback_linear_ineq_upper_bounds () const
	return linear constraint upper bounds for this iterator (user-functions mode for which iteratedModel is null)
virtual void	initialize_graphics (int iterator_server_id=1)
	initialize the 2D graphics window and the tabular graphics data More...
virtual void	check_sub_iterator_conflict ()
	detect any conflicts due to recursive use of the same Fortran solver More...
virtual unsigned short	uses_method () const
	return name of any enabling iterator used by this iterator
virtual void	method_recourse (unsigned short method_name)
	perform a method switch, if possible, due to a detected conflict with the simultaneous use of method_name at an higher-level
virtual const VariablesArray &	all_variables ()
	return the complete set of evaluated variables
virtual const RealMatrix &	all_samples ()
	return the complete set of evaluated samples
virtual const IntResponseMap &	all_responses () const
	return the complete set of computed responses
virtual size_t	num_samples () const
	get the current number of samples
virtual void	sampling_reset (size_t min_samples, bool all_data_flag, bool stats_flag)
	reset sampling iterator to use at least min_samples
virtual void	sampling_reference (size_t samples_ref)
	set reference number of samples, which is a lower bound during reset
virtual void	sampling_increment ()
	increment to next in sequence of refinement samples
virtual void	random_seed (int seed)
	set randomSeed, if present
virtual unsigned short	sampling_scheme () const
	return sampling name
virtual bool	compact_mode () const
	returns Analyzer::compactMode
virtual IntIntPair	estimate_partition_bounds ()
	estimate the minimum and maximum partition sizes that can be utilized by this Iterator
virtual void	declare_sources ()
	Declare sources to the evaluations database.
void	init_communicators (ParLevLIter pl_iter)
	initialize the communicators associated with this Iterator instance
void	set_communicators (ParLevLIter pl_iter)
	set the communicators associated with this Iterator instance
void	free_communicators (ParLevLIter pl_iter)
	free the communicators associated with this Iterator instance
void	resize_communicators (ParLevLIter pl_iter, bool reinit_comms)
	Resize the communicators. This is called from the letter's resize()
void	parallel_configuration_iterator (ParConfigLIter pc_iter)
	set methodPCIter
ParConfigLIter	parallel_configuration_iterator () const
	return methodPCIter
void	parallel_configuration_iterator_map (std::map< size_t, ParConfigLIter > pci_map)
	set methodPCIterMap
std::map< size_t, ParConfigLIter >	parallel_configuration_iterator_map () const
	return methodPCIterMap
void	run (ParLevLIter pl_iter)
	invoke set_communicators(pl_iter) prior to run()
void	run ()
	orchestrate initialize/pre/core/post/finalize phases More...
void	assign_rep (std::shared_ptr< Iterator > iterator_rep)
	replaces existing letter with a new one More...
void	iterated_model (const Model &model)
	set the iteratedModel (iterators and meta-iterators using a single model instance)
Model &	iterated_model ()
	return the iteratedModel (iterators & meta-iterators using a single model instance)
ProblemDescDB &	problem_description_db () const
	return the problem description database (probDescDB)
ParallelLibrary &	parallel_library () const
	return the parallel library (parallelLib)
void	method_name (unsigned short m_name)
	set the method name to an enumeration value
unsigned short	method_name () const
	return the method name via its native enumeration value
void	method_string (const String &m_str)
	set the method name by string
String	method_string () const
	return the method name by string
String	method_enum_to_string (unsigned short method_enum) const
	convert a method name enumeration value to a string
unsigned short	method_string_to_enum (const String &method_str) const
	convert a method name string to an enumeration value
String	submethod_enum_to_string (unsigned short submethod_enum) const
	convert a sub-method name enumeration value to a string
const String &	method_id () const
	return the method identifier (methodId)
int	maximum_evaluation_concurrency () const
	return the maximum evaluation concurrency supported by the iterator
void	maximum_evaluation_concurrency (int max_conc)
	set the maximum evaluation concurrency supported by the iterator
size_t	maximum_iterations () const
	return the maximum iterations for this iterator
void	maximum_iterations (size_t max_iter)
	set the maximum iterations for this iterator
void	convergence_tolerance (Real conv_tol)
	set the method convergence tolerance (convergenceTol)
Real	convergence_tolerance () const
	return the method convergence tolerance (convergenceTol)
void	output_level (short out_lev)
	set the method output level (outputLevel)
short	output_level () const
	return the method output level (outputLevel)
void	summary_output (bool summary_output_flag)
	Set summary output control; true enables evaluation/results summary.
size_t	num_final_solutions () const
	return the number of solutions to retain in best variables/response arrays
void	num_final_solutions (size_t num_final)
	set the number of solutions to retain in best variables/response arrays
void	active_set (const ActiveSet &set)
	set the default active set (for use with iterators that employ evaluate_parameter_sets())
const ActiveSet &	active_set () const
	return the default active set (used by iterators that employ evaluate_parameter_sets())
void	active_set_request_vector (const ShortArray &asv)
	return the default active set request vector (used by iterators that employ evaluate_parameter_sets())
const ShortArray &	active_set_request_vector () const
	return the default active set request vector (used by iterators that employ evaluate_parameter_sets())
void	active_set_request_values (short asv_val)
	return the default active set request vector (used by iterators that employ evaluate_parameter_sets())
void	sub_iterator_flag (bool si_flag)
	set subIteratorFlag (and update summaryOutputFlag if needed)
bool	is_null () const
	function to check iteratorRep (does this envelope contain a letter?)
std::shared_ptr< Iterator >	iterator_rep () const
	returns iteratorRep for access to derived class member functions that are not mapped to the top Iterator level
virtual void	eval_tag_prefix (const String &eval_id_str)
	set the hierarchical eval ID tag prefix More...
std::shared_ptr< TraitsBase >	traits () const
	returns methodTraits for access to derived class member functions that are not mapped to the top TraitsBase level
bool	top_level ()
	Return whether the iterator is the top level iterator.
void	top_level (bool tflag)
	Set the iterator's top level flag.

Private Member Functions
void	initialize_sub_problem (const String &approx_type, int samples, int seed, bool use_derivs, const String &sample_reuse, const String &import_build_points_file=String(), unsigned short import_build_format=TABULAR_ANNOTATED, bool import_build_active_only=false, const String &export_approx_points_file=String(), unsigned short export_approx_format=TABULAR_ANNOTATED)
	shared ctor code for initializing Models and Minimizers for solving an approximate sub-problem
void	initialize_multipliers ()
	initialize Lagrange multipliers in the case of nonlinear constraints
void	check_parallelism ()
	function that checks if model supports asynchronous parallelism
void	build_gp ()
	build initial GP approximations after initial sampling
void	batch_synchronous_ego ()
	synchronous batch-sequential implementation: main function
void	batch_asynchronous_ego ()
	synchronous batch-sequential implementation: main function
void	construct_batch_acquisition (size_t new_acq, size_t new_batch)
	construct a batch of points from performing acquisition cycles
void	construct_batch_exploration (size_t new_expl, size_t new_batch)
	construct a batch of points from performing exploration cycles
void	evaluate_batch (bool rebuild)
	evaluate batch in parallel and replace liar responses
bool	query_batch (bool rebuild)
	perform nonblocking synchronization for parallel evaluation of truth responses and replace liar responses for any completions More...
void	backfill_batch (size_t new_acq, size_t new_expl)
	backfill any completed truth response evaluations in case of nonblocking synchronization
void	launch_batch ()
	launch all jobs in the variables map queues
void	launch_single (const Variables &vars_star)
	launch a single job
void	process_truth_response_map (const IntResponseMap &truth_resp_map, bool rebuild)
	update approximation data and constraint penalties/multipliers based on new truth data
void	update_variable_maps (const IntResponseMap &truth_resp_map)
	update variable map queues based on completed jobs
bool	converged ()
	check convergence indicators to assess if EGO has converged
void	retrieve_final_results ()
	post-processing: retrieve and export best samples and responses
void	initialize_counters_limits ()
	initialize counters and limits used for assessing convergence
void	update_convergence_counters (const Variables &vars_star, const Response &resp_star)
	update counters used for assessing convergence
void	update_convergence_counters (const Variables &vars_star)
	update counters used for assessing convergence in variables
void	update_convergence_counters (const Response &resp_star)
	update counters used for assessing convergence in response
void	pop_liar_responses ()
	delete all liar responses
void	append_liar (const Variables &vars_star, int liar_id, bool rebuild)
	evaluate and append a liar response
int	extract_id (IntVarsMCIter it, const IntVariablesMap &map)
	manage special value when iterator has advanced to end
void	compute_best_sample ()
	determine meritFnStar from among the GP build data for use in EIF More...
void	extract_best_sample ()
	extract best solution from among the GP build data for final results More...
void	extract_qoi_build_data (size_t data_index, RealVector &fn_vals)
	extra response function build data from across the set of QoI
Real	augmented_lagrangian (const RealVector &mean)
	helper for evaluating the value of the augmented Lagrangian merit fn
void	update_constraints (const RealVector &fn_vals)
	update constraint penalties and multipliers for a single response
void	update_constraints (const IntResponseMap &truth_resp_map)
	update constraint penalties and multipliers for a set of responses
bool	empty_queues () const
	helper for checking queued jobs in vars{Acquisition,Exploration}Map
Real	compute_probability_improvement (const RealVector &means, const RealVector &variances)
	probability improvement (PI) function for the EGO PI acquisition function implementation More...
Real	compute_expected_improvement (const RealVector &means, const RealVector &variances)
	expected improvement (EI) function for the EGO EI acquisition function implementation More...
Real	compute_lower_confidence_bound (const RealVector &means, const RealVector &variances)
	lower confidence bound (LCB) function for the EGO LCB acquisition function implementation More...
Real	compute_variances (const RealVector &variances)
	variance function for the EGO MSE acquisition implementation More...
RealVector	expected_violation (const RealVector &means, const RealVector &variances)
	expected violation function for the constraint functions More...
void	update_penalty ()
	initialize and update the penaltyParameter
void	debug_print_values (const Variables &vars)
	print mean and variance if debug flag is ON
void	debug_print_dist_counters (Real dist_cv_star)
	print counters if debug flag is ON
void	debug_print_eif_counters (Real eif_star)
	print counters if debug flag is ON
void	debug_plots ()
	DEBUG_PLOTS conditional - output set of samples used to build the GP if problem is 2D.

Static Private Member Functions
static void	PIF_objective_eval (const Variables &sub_model_vars, const Variables &recast_vars, const Response &sub_model_response, Response &recast_response)
	static function used as the objective function in the Expected Improvement (EIF) problem formulation for EGO More...
static void	EIF_objective_eval (const Variables &sub_model_vars, const Variables &recast_vars, const Response &sub_model_response, Response &recast_response)
	static function used as the objective function in the Expected Improvement (EIF) problem formulation for EGO More...
static void	LCB_objective_eval (const Variables &sub_model_vars, const Variables &recast_vars, const Response &sub_model_response, Response &recast_response)
	static function used as the objective function in the Lower-Confidence Bound (LCB) problem formulation for EGO More...
static void	Variances_objective_eval (const Variables &sub_model_vars, const Variables &recast_vars, const Response &sub_model_response, Response &recast_response)
	Variance formulation for primary. More...

Private Attributes
Model	fHatModel
	GP model of response, one approximation per response function.
Model	approxSubProbModel
	recast model which assimilates either (a) mean and variance to solve the max(EIF) sub-problem (used by EIF_objective_eval()) or (b) variance alone for pure exploration (used by Variances_objective_eval())
Real	meritFnStar
	minimum penalized response from among truth build data
RealVector	prevSubProbSoln
	previous solution to EIF approximation sub-problem
short	dataOrder
	order of the data used for surrogate construction, in ActiveSet request vector 3-bit format; user may override responses spec
int	batchSize
	total batch size for parallel EGO
int	batchSizeAcquisition
	number of new sampling points defined from maximizing acquisition function
int	batchSizeExploration
	number of new sampling points defined from maximizing posterior variance
int	batchEvalId
	counter for incrementing evaluation ids to allow synchronization with iteratedModel ids across acquisition and exploration job queues
IntVariablesMap	varsAcquisitionMap
	variable sets for batch evaluation of truth model, accumulated by construct_batch_acquisition()
IntVariablesMap	varsExplorationMap
	variable sets for batch evaluation of truth model, accumulated by construct_batch_exploration()
bool	parallelFlag
	bool flag if model supports asynchronous parallelism
bool	batchAsynch
	algorithm option for fully asynchronous batch updating of the GP
Real	distanceTol
	tolerance on distance between consecutive iterates
unsigned short	distConvergenceCntr
	counter for consecutive iterates with variable distance < distanceTol
unsigned short	distConvergenceLimit
	limit for distance convergence counter
unsigned short	eifConvergenceCntr
	counter for consecutive iterates with EIF value < convergenceTol
unsigned short	eifConvergenceLimit
	limit for EIF convergence counter
unsigned short	globalIterCount
	counter for global iteration

Static Private Attributes
static EffGlobalMinimizer *	effGlobalInstance
	pointer to the active object instance used within the static evaluator functions in order to avoid the need for static data

Additional Inherited Members
Static Public Member Functions inherited from Minimizer
static Real	sum_squared_residuals (size_t num_pri_fns, const RealVector &residuals, const RealVector &weights)
	return weighted sum of squared residuals
static void	print_residuals (size_t num_terms, const RealVector &best_terms, const RealVector &weights, size_t num_best, size_t best_index, std::ostream &s)
	print num_terms residuals and misfit for final results
static void	print_model_resp (size_t num_pri_fns, const RealVector &best_fns, size_t num_best, size_t best_index, std::ostream &s)
	print the original user model resp in the case of data transformations
static void	print_best_eval_ids (const String &interface_id, const Variables &best_vars, const ActiveSet &active_set, std::ostream &s)
	print best evaluation matching vars and set, or partial matches with matching variables only. More...
Protected Member Functions inherited from SurrBasedMinimizer
	SurrBasedMinimizer (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits)
	constructor
	SurrBasedMinimizer (Model &model, size_t max_iter, size_t max_eval, Real conv_tol, std::shared_ptr< TraitsBase > traits)
	alternate constructor for instantiations "on the fly"
	~SurrBasedMinimizer ()
	destructor
void	derived_init_communicators (ParLevLIter pl_iter)
	derived class contributions to initializing the communicators associated with this Iterator instance
void	derived_set_communicators (ParLevLIter pl_iter)
	derived class contributions to setting the communicators associated with this Iterator instance
void	derived_free_communicators (ParLevLIter pl_iter)
	derived class contributions to freeing the communicators associated with this Iterator instance
void	print_results (std::ostream &s, short results_state=FINAL_RESULTS)
void	initialize_from_model (Model &model)
	helper for shared ctor code
void	update_lagrange_multipliers (const RealVector &fn_vals, const RealMatrix &fn_grads, SurrBasedLevelData &tr_data)
	initialize and update Lagrange multipliers for basic Lagrangian More...
void	update_augmented_lagrange_multipliers (const RealVector &fn_vals)
	initialize and update the Lagrange multipliers for augmented Lagrangian More...
void	initialize_filter (SurrBasedLevelData &tr_data, const RealVector &fn_vals)
	(re-)initialize filter from a set of function values
bool	update_filter (SurrBasedLevelData &tr_data, const RealVector &fn_vals)
	update filter using a new set of function values More...
Real	lagrangian_merit (const RealVector &fn_vals, const BoolDeque &sense, const RealVector &primary_wts, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_tgts)
	compute a Lagrangian function from a set of function values More...
void	lagrangian_gradient (const RealVector &fn_vals, const RealMatrix &fn_grads, const BoolDeque &sense, const RealVector &primary_wts, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_tgts, RealVector &lag_grad)
	compute the gradient of the Lagrangian function
void	lagrangian_hessian (const RealVector &fn_vals, const RealMatrix &fn_grads, const RealSymMatrixArray &fn_hessians, const BoolDeque &sense, const RealVector &primary_wts, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_tgts, RealSymMatrix &lag_hess)
	compute the Hessian of the Lagrangian function
Real	augmented_lagrangian_merit (const RealVector &fn_vals, const BoolDeque &sense, const RealVector &primary_wts, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_tgts)
	compute an augmented Lagrangian function from a set of function values More...
void	augmented_lagrangian_gradient (const RealVector &fn_vals, const RealMatrix &fn_grads, const BoolDeque &sense, const RealVector &primary_wts, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_tgts, RealVector &alag_grad)
	compute the gradient of the augmented Lagrangian function
void	augmented_lagrangian_hessian (const RealVector &fn_vals, const RealMatrix &fn_grads, const RealSymMatrixArray &fn_hessians, const BoolDeque &sense, const RealVector &primary_wts, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_tgts, RealSymMatrix &alag_hess)
	compute the Hessian of the augmented Lagrangian function
Real	penalty_merit (const RealVector &fn_vals, const BoolDeque &sense, const RealVector &primary_wts)
	compute a penalty function from a set of function values More...
void	penalty_gradient (const RealVector &fn_vals, const RealMatrix &fn_grads, const BoolDeque &sense, const RealVector &primary_wts, RealVector &pen_grad)
	compute the gradient of the penalty function
Real	constraint_violation (const RealVector &fn_vals, const Real &constraint_tol)
	compute the constraint violation from a set of function values More...
Protected Member Functions inherited from Minimizer
	Minimizer (std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	default constructor
	Minimizer (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	standard constructor More...
	Minimizer (unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for "on the fly" instantiations
	Minimizer (unsigned short method_name, size_t num_lin_ineq, size_t num_lin_eq, size_t num_nln_ineq, size_t num_nln_eq, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for "on the fly" instantiations
	Minimizer (Model &model, size_t max_iter, size_t max_eval, Real conv_tol, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for "on the fly" instantiations
	~Minimizer ()
	destructor
void	update_from_model (const Model &model)
	set inherited data attributes based on extractions from incoming model
void	initialize_run ()
	utility function to perform common operations prior to pre_run(); typically memory initialization; setting of instance pointers More...
void	finalize_run ()
	utility function to perform common operations following post_run(); deallocation and resetting of instance pointers More...
Model	original_model (unsigned short recasts_left=0) const
	Return a shallow copy of the original model this Iterator was originally passed, optionally leaving recasts_left on top of it.
void	data_transform_model ()
	Wrap iteratedModel in a RecastModel that subtracts provided observed data from the primary response functions (variables and secondary responses are unchanged) More...
void	scale_model ()
	Wrap iteratedModel in a RecastModel that performs variable and/or response scaling. More...
void	enforce_null_model ()
	ensure iteratedModel is null when using function callbacks for evaluation
Real	objective (const RealVector &fn_vals, const BoolDeque &max_sense, const RealVector &primary_wts) const
	compute a composite objective value from one or more primary functions More...
Real	objective (const RealVector &fn_vals, size_t num_fns, const BoolDeque &max_sense, const RealVector &primary_wts) const
	compute a composite objective with specified number of source primary functions, instead of userPrimaryFns More...
void	objective_gradient (const RealVector &fn_vals, const RealMatrix &fn_grads, const BoolDeque &max_sense, const RealVector &primary_wts, RealVector &obj_grad) const
	compute the gradient of the composite objective function
void	objective_gradient (const RealVector &fn_vals, size_t num_fns, const RealMatrix &fn_grads, const BoolDeque &max_sense, const RealVector &primary_wts, RealVector &obj_grad) const
	compute the gradient of the composite objective function More...
void	objective_hessian (const RealVector &fn_vals, const RealMatrix &fn_grads, const RealSymMatrixArray &fn_hessians, const BoolDeque &max_sense, const RealVector &primary_wts, RealSymMatrix &obj_hess) const
	compute the Hessian of the composite objective function
void	objective_hessian (const RealVector &fn_vals, size_t num_fns, const RealMatrix &fn_grads, const RealSymMatrixArray &fn_hessians, const BoolDeque &max_sense, const RealVector &primary_wts, RealSymMatrix &obj_hess) const
	compute the Hessian of the composite objective function More...
virtual void	archive_best_results ()
	top-level archival method
void	archive_best_variables (const bool active_only=false) const
	archive best variables for the index'th final solution
void	archive_best_objective_functions () const
	archive the index'th set of objective functions
void	archive_best_constraints () const
	archive the index'th set of constraints
void	archive_best_residuals () const
	Archive residuals when calibration terms are used.
void	resize_best_vars_array (size_t newsize)
	Safely resize the best variables array to newsize taking into account the envelope-letter design pattern and any recasting. More...
void	resize_best_resp_array (size_t newsize)
	Safely resize the best response array to newsize taking into account the envelope-letter design pattern and any recasting. More...
bool	local_recast_retrieve (const Variables &vars, Response &response) const
	infers MOO/NLS solution from the solution of a single-objective optimizer and returns true if lookup succeeds More...
void	reshape_best (size_t num_cv, size_t num_fns)
	reshape input/output sizes within best{Variables,Response}Array
Protected Member Functions inherited from Iterator
	Iterator (BaseConstructor, ProblemDescDB &problem_db, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	constructor initializes the base class part of letter classes (BaseConstructor overloading avoids infinite recursion in the derived class constructors - Coplien, p. 139) More...
	Iterator (NoDBBaseConstructor, unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for base iterator classes constructed on the fly More...
	Iterator (NoDBBaseConstructor, unsigned short method_name, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate constructor for base iterator classes constructed on the fly More...
	Iterator (NoDBBaseConstructor, Model &model, size_t max_iter, size_t max_eval, Real conv_tol, std::shared_ptr< TraitsBase > traits=std::shared_ptr< TraitsBase >(new TraitsBase()))
	alternate envelope constructor for instantiations without ProblemDescDB More...
virtual const VariablesArray &	initial_points () const
	gets the multiple initial points for this iterator. This will only be meaningful after a call to initial_points mutator.
StrStrSizet	run_identifier () const
	get the unique run identifier based on method name, id, and number of executions
void	initialize_model_graphics (Model &model, int iterator_server_id)
	helper function that encapsulates initialization operations, modular on incoming Model instance More...
void	export_final_surrogates (Model &data_fit_surr_model)
	export final surrogates generated, e.g., GP in EGO and friends More...
Static Protected Member Functions inherited from Iterator
static void	gnewton_set_recast (const Variables &recast_vars, const ActiveSet &recast_set, ActiveSet &sub_model_set)
	conversion of request vector values for the Gauss-Newton Hessian approximation More...
Protected Attributes inherited from SurrBasedMinimizer
Iterator	approxSubProbMinimizer
	the minimizer used on the surrogate model to solve the approximate subproblem on each surrogate-based iteration
size_t	globalIterCount
	global iteration counter corresponding to number of surrogate-based minimizations
RealVector	lagrangeMult
	Lagrange multipliers for basic Lagrangian calculations.
RealVector	augLagrangeMult
	Lagrange multipliers for augmented Lagrangian calculations.
Real	penaltyParameter
	the penalization factor for violated constraints used in quadratic penalty calculations; increased in update_penalty()
RealVector	origNonlinIneqLowerBnds
	original nonlinear inequality constraint lower bounds (no relaxation)
RealVector	origNonlinIneqUpperBnds
	original nonlinear inequality constraint upper bounds (no relaxation)
RealVector	origNonlinEqTargets
	original nonlinear equality constraint targets (no relaxation)
Real	eta
	constant used in etaSequence updates
Real	alphaEta
	power for etaSequence updates when updating penalty
Real	betaEta
	power for etaSequence updates when updating multipliers
Real	etaSequence
	decreasing sequence of allowable constraint violation used in augmented Lagrangian updates (refer to Conn, Gould, and Toint, section 14.4)
size_t	miPLIndex
	index for the active ParallelLevel within ParallelConfiguration::miPLIters
Protected Attributes inherited from Minimizer
size_t	numFunctions
	number of response functions
size_t	numContinuousVars
	number of active continuous vars
size_t	numDiscreteIntVars
	number of active discrete integer vars
size_t	numDiscreteStringVars
	number of active discrete string vars
size_t	numDiscreteRealVars
	number of active discrete real vars
Real	constraintTol
	optimizer/least squares constraint tolerance
Real	bigRealBoundSize
	cutoff value for inequality constraint and continuous variable bounds
int	bigIntBoundSize
	cutoff value for discrete variable bounds
size_t	numNonlinearIneqConstraints
	number of nonlinear inequality constraints
size_t	numNonlinearEqConstraints
	number of nonlinear equality constraints
size_t	numLinearIneqConstraints
	number of linear inequality constraints
size_t	numLinearEqConstraints
	number of linear equality constraints
size_t	numNonlinearConstraints
	total number of nonlinear constraints
size_t	numLinearConstraints
	total number of linear constraints
size_t	numConstraints
	total number of linear and nonlinear constraints
bool	optimizationFlag
	flag for use where optimization and NLS must be distinguished
size_t	numUserPrimaryFns
	number of objective functions or least squares terms in the inbound model; always initialize at Minimizer, even if overridden later
size_t	numIterPrimaryFns
	number of objective functions or least squares terms in iterator's view, after transformations; always initialize at Minimizer, even if overridden later
bool	boundConstraintFlag
	convenience flag for denoting the presence of user-specified bound constraints. Used for method selection and error checking.
bool	speculativeFlag
	flag for speculative gradient evaluations
bool	calibrationDataFlag
	flag indicating whether user-supplied calibration data is active
ExperimentData	expData
	Container for experimental data to which to calibrate model using least squares or other formulations which minimize SSE.
size_t	numExperiments
	number of experiments
size_t	numTotalCalibTerms
	number of total calibration terms (sum over experiments of number of experimental data per experiment, including field data)
Model	dataTransformModel
	Shallow copy of the data transformation model, when present (cached in case further wrapped by other transformations)
bool	scaleFlag
	whether Iterator-level scaling is active
Model	scalingModel
	Shallow copy of the scaling transformation model, when present (cached in case further wrapped by other transformations)
Minimizer *	prevMinInstance
	pointer containing previous value of minimizerInstance
bool	vendorNumericalGradFlag
	convenience flag for gradient_type == numerical && method_source == vendor
std::shared_ptr< TPLDataTransfer >	dataTransferHandler
	Emerging helper class for handling data transfers to/from Dakota and the underlying TPL.
Protected Attributes inherited from Iterator
ProblemDescDB &	probDescDB
	class member reference to the problem description database More...
ParallelLibrary &	parallelLib
	class member reference to the parallel library
ParConfigLIter	methodPCIter
	the active ParallelConfiguration used by this Iterator instance
Model	iteratedModel
	the model to be iterated (for iterators and meta-iterators employing a single model instance)
size_t	myModelLayers
	number of Models locally (in Iterator or derived classes) wrapped around the initially passed in Model
unsigned short	methodName
	name of the iterator (the user's method spec)
Real	convergenceTol
	iteration convergence tolerance
size_t	maxIterations
	maximum number of iterations for the method
size_t	maxFunctionEvals
	maximum number of fn evaluations for the method
int	maxEvalConcurrency
	maximum number of concurrent model evaluations More...
ActiveSet	activeSet
	the response data requirements on each function evaluation
size_t	numFinalSolutions
	number of solutions to retain in best variables/response arrays
VariablesArray	bestVariablesArray
	collection of N best solution variables found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)
ResponseArray	bestResponseArray
	collection of N best solution responses found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)
bool	subIteratorFlag
	flag indicating if this Iterator is a sub-iterator (NestedModel::subIterator or DataFitSurrModel::daceIterator)
short	outputLevel
	output verbosity level: {SILENT,QUIET,NORMAL,VERBOSE,DEBUG}_OUTPUT
bool	summaryOutputFlag
	flag for summary output (evaluation stats, final results); default true, but false for on-the-fly (helper) iterators and sub-iterator use cases
ResultsManager &	resultsDB
	reference to the global iterator results database
EvaluationStore &	evaluationsDB
	reference to the global evaluation database
EvaluationsDBState	evaluationsDBState
	State of evaluations DB for this iterator.
ResultsNames	resultsNames
	valid names for iterator results
std::shared_ptr< TraitsBase >	methodTraits
	pointer that retains shared ownership of a TraitsBase object, or child thereof
bool	topLevel
	Whether this is the top level iterator.
bool	exportSurrogate = false
	whether to export final surrogates
String	surrExportPrefix
	base filename for exported surrogates
unsigned short	surrExportFormat = NO_MODEL_FORMAT
	(bitwise) format(s) to export
Static Protected Attributes inherited from Minimizer
static Minimizer *	minimizerInstance
	pointer to Minimizer used in static member functions

Detailed Description

The EffGlobalMinimizer class provides an implementation of the Efficient Global Optimization algorithm developed by Jones, Schonlau, & Welch as well as adaptation of the concept to nonlinear least squares.

Member Function Documentation

pre_run()

void pre_run ( )

virtual

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

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

Reimplemented from Iterator.

References EffGlobalMinimizer::approxSubProbModel, EffGlobalMinimizer::check_parallelism(), EffGlobalMinimizer::initialize_counters_limits(), Model::initialize_mapping(), Iterator::methodPCIter, and SurrBasedMinimizer::miPLIndex.

core_run()

void core_run ( )

virtual

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

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

Reimplemented from Iterator.

References EffGlobalMinimizer::batch_asynchronous_ego(), EffGlobalMinimizer::batch_synchronous_ego(), EffGlobalMinimizer::batchAsynch, EffGlobalMinimizer::build_gp(), and EffGlobalMinimizer::effGlobalInstance.

post_run()

void post_run ( std::ostream &  s )

virtual

post-run portion of run (optional); verbose to print results; re-implemented by Iterators that can read all Variables/Responses and perform final analysis phase in a standalone way

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

Reimplemented from Minimizer.

References EffGlobalMinimizer::approxSubProbModel, Model::finalize_mapping(), Minimizer::post_run(), and EffGlobalMinimizer::retrieve_final_results().

algorithm_space_model()

const Model & algorithm_space_model ( ) const

inlinevirtual

default definition that gets redefined in selected derived Minimizers

Reimplemented from Minimizer.

References EffGlobalMinimizer::fHatModel.

query_batch()

bool query_batch ( bool  rebuild )

private

perform nonblocking synchronization for parallel evaluation of truth responses and replace liar responses for any completions

query running jobs and process any new completions

References Model::component_parallel_mode(), EffGlobalMinimizer::empty_queues(), EffGlobalMinimizer::fHatModel, Iterator::iteratedModel, EffGlobalMinimizer::process_truth_response_map(), Model::synchronize_nowait(), and EffGlobalMinimizer::update_variable_maps().

Referenced by EffGlobalMinimizer::batch_asynchronous_ego().

compute_best_sample()

void compute_best_sample ( )

private

determine meritFnStar from among the GP build data for use in EIF

Extract the best merit function from build data through evaluaton of points on fHatModel. This merit fn value is used within the EIF during an approximate sub-problem solve.

References Model::approximation_data(), EffGlobalMinimizer::augmented_lagrangian(), Model::continuous_variables(), Model::current_response(), Model::evaluate(), EffGlobalMinimizer::fHatModel, Response::function_values(), EffGlobalMinimizer::meritFnStar, and Minimizer::numFunctions.

Referenced by EffGlobalMinimizer::construct_batch_acquisition().

extract_best_sample()

void extract_best_sample ( )

private

extract best solution from among the GP build data for final results

Extract the best point from the build data for final results reporting.

References Model::approximation_data(), EffGlobalMinimizer::augmented_lagrangian(), Iterator::bestResponseArray, Iterator::bestVariablesArray, EffGlobalMinimizer::extract_qoi_build_data(), EffGlobalMinimizer::fHatModel, and Minimizer::numFunctions.

Referenced by EffGlobalMinimizer::retrieve_final_results().

compute_probability_improvement()

Real compute_probability_improvement ( const RealVector &  means, const RealVector &  variances  )

private

probability improvement (PI) function for the EGO PI acquisition function implementation

Compute the PI acquisition function

References SurrBasedMinimizer::augLagrangeMult, EffGlobalMinimizer::expected_violation(), Iterator::iteratedModel, EffGlobalMinimizer::meritFnStar, Minimizer::numNonlinearConstraints, Minimizer::objective(), SurrBasedMinimizer::penaltyParameter, Model::primary_response_fn_sense(), and Model::primary_response_fn_weights().

Referenced by EffGlobalMinimizer::PIF_objective_eval().

compute_expected_improvement()

Real compute_expected_improvement ( const RealVector &  means, const RealVector &  variances  )

private

expected improvement (EI) function for the EGO EI acquisition function implementation

Compute the EI acquisition function

References SurrBasedMinimizer::augLagrangeMult, EffGlobalMinimizer::expected_violation(), Iterator::iteratedModel, EffGlobalMinimizer::meritFnStar, Minimizer::numNonlinearConstraints, Minimizer::objective(), SurrBasedMinimizer::penaltyParameter, Model::primary_response_fn_sense(), and Model::primary_response_fn_weights().

Referenced by EffGlobalMinimizer::debug_plots(), and EffGlobalMinimizer::EIF_objective_eval().

compute_lower_confidence_bound()

Real compute_lower_confidence_bound ( const RealVector &  means, const RealVector &  variances  )

private

lower confidence bound (LCB) function for the EGO LCB acquisition function implementation

Compute the LCB acquisition function

References SurrBasedMinimizer::augLagrangeMult, EffGlobalMinimizer::expected_violation(), Iterator::iteratedModel, Minimizer::numNonlinearConstraints, Minimizer::objective(), SurrBasedMinimizer::penaltyParameter, Model::primary_response_fn_sense(), and Model::primary_response_fn_weights().

Referenced by EffGlobalMinimizer::LCB_objective_eval().

compute_variances()

Real compute_variances ( const RealVector &  variances )

private

variance function for the EGO MSE acquisition implementation

Compute the variances

Referenced by EffGlobalMinimizer::Variances_objective_eval().

expected_violation()

RealVector expected_violation ( const RealVector &  means, const RealVector &  variances  )

private

expected violation function for the constraint functions

Compute the expected violation for constraints

References Minimizer::bigRealBoundSize, Minimizer::numNonlinearConstraints, Minimizer::numNonlinearEqConstraints, Minimizer::numNonlinearIneqConstraints, Minimizer::numUserPrimaryFns, SurrBasedMinimizer::origNonlinEqTargets, SurrBasedMinimizer::origNonlinIneqLowerBnds, and SurrBasedMinimizer::origNonlinIneqUpperBnds.

Referenced by EffGlobalMinimizer::compute_expected_improvement(), EffGlobalMinimizer::compute_lower_confidence_bound(), EffGlobalMinimizer::compute_probability_improvement(), and EffGlobalMinimizer::debug_print_values().

PIF_objective_eval()

void PIF_objective_eval ( const Variables &  sub_model_vars, const Variables &  recast_vars, const Response &  sub_model_response, Response &  recast_response  )

staticprivate

static function used as the objective function in the Expected Improvement (EIF) problem formulation for EGO

To maximize expected improvement (PI), the approxSubProbMinimizer will minimize -(compute_probability_improvement).

References Response::active_set_request_vector(), Model::approximation_variances(), EffGlobalMinimizer::compute_probability_improvement(), EffGlobalMinimizer::effGlobalInstance, EffGlobalMinimizer::fHatModel, Response::function_value(), and Response::function_values().

EIF_objective_eval()

void EIF_objective_eval ( const Variables &  sub_model_vars, const Variables &  recast_vars, const Response &  sub_model_response, Response &  recast_response  )

staticprivate

static function used as the objective function in the Expected Improvement (EIF) problem formulation for EGO

To maximize expected improvement (EI), the approxSubProbMinimizer will minimize -(compute_expected_improvement).

References Response::active_set_request_vector(), Model::approximation_variances(), EffGlobalMinimizer::compute_expected_improvement(), EffGlobalMinimizer::effGlobalInstance, EffGlobalMinimizer::fHatModel, Response::function_value(), and Response::function_values().

Referenced by EffGlobalMinimizer::construct_batch_acquisition().

LCB_objective_eval()

void LCB_objective_eval ( const Variables &  sub_model_vars, const Variables &  recast_vars, const Response &  sub_model_response, Response &  recast_response  )

staticprivate

static function used as the objective function in the Lower-Confidence Bound (LCB) problem formulation for EGO

To maximize lower confidence bound (LCB), the approxSubProbMinimizer will minimize -(compute_lower_confidence_bound).

References Response::active_set_request_vector(), Model::approximation_variances(), EffGlobalMinimizer::compute_lower_confidence_bound(), EffGlobalMinimizer::effGlobalInstance, EffGlobalMinimizer::fHatModel, Response::function_value(), and Response::function_values().

Variances_objective_eval()

void Variances_objective_eval ( const Variables &  sub_model_vars, const Variables &  recast_vars, const Response &  sub_model_response, Response &  recast_response  )

staticprivate

Variance formulation for primary.

To maximize variances, the approxSubProbMinimizer will minimize -(variances).

References Response::active_set_request_vector(), Model::approximation_variances(), EffGlobalMinimizer::compute_variances(), EffGlobalMinimizer::effGlobalInstance, EffGlobalMinimizer::fHatModel, and Response::function_value().

Referenced by EffGlobalMinimizer::construct_batch_exploration().

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

• EffGlobalMinimizer.hpp
• EffGlobalMinimizer.cpp