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EffGlobalMinimizer Class Reference
Inheritance diagram for EffGlobalMinimizer:
SurrBasedMinimizer Minimizer Iterator

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 Modelalgorithm_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< TPLDataTransferget_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 Variablesvariables_results () const
 return a single final iterator solution (variables)
 
virtual const Responseresponse_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 variable_bounds (const RealVector &cv_lower_bnds, const RealVector &cv_upper_bnds)
 assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used)
 
virtual void linear_constraints (const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_lb, const RealVector &lin_ineq_ub, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgt)
 assign linear inequality and linear equality constraints for this iterator (user-functions mode for which Model updating is not used)
 
virtual void nonlinear_constraints (const RealVector &nln_ineq_lb, const RealVector &nln_ineq_ub, const RealVector &nln_eq_tgt)
 assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used)
 
virtual void initialize_graphics (int iterator_server_id=1)
 initialize the 2D graphics window and the tabular graphics data More...
 
virtual 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 ()
 perform a method switch, if possible, due to a detected conflict
 
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)
 
Modeliterated_model ()
 return the iteratedModel (iterators & meta-iterators using a single model instance)
 
ProblemDescDBproblem_description_db () const
 return the problem description database (probDescDB)
 
ParallelLibraryparallel_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 ActiveSetactive_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< Iteratoriterator_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< TraitsBasetraits () 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
 convergence checkers tolerance convergence on distance between predicted best-so-far samples
 
unsigned short eifConvergenceCntr
 counter for convergence in EIF
 
unsigned short eifConvergenceLimit
 limit convergence (compared with counter) of EIF
 
unsigned short distConvergenceCntr
 counter for distance in input space
 
unsigned short distConvergenceLimit
 limit for distance (compared with counter) in input space
 
unsigned short globalIterCount
 counter for global iteration
 

Static Private Attributes

static EffGlobalMinimizereffGlobalInstance
 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...
 
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...
 
- 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)
 
MinimizerprevMinInstance
 pointer containing previous value of minimizerInstance
 
bool vendorNumericalGradFlag
 convenience flag for gradient_type == numerical && method_source == vendor
 
std::shared_ptr< TPLDataTransferdataTransferHandler
 Emerging helper class for handling data transfers to/from Dakota and the underlying TPL.
 
- Protected Attributes inherited from Iterator
ProblemDescDBprobDescDB
 class member reference to the problem description database More...
 
ParallelLibraryparallelLib
 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
 
ResultsManagerresultsDB
 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< TraitsBasemethodTraits
 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 MinimizerminimizerInstance
 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

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.

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.

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().

const Model & algorithm_space_model ( ) const
inlinevirtual

default definition that gets redefined in selected derived Minimizers

Reimplemented from Minimizer.

References EffGlobalMinimizer::fHatModel.

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().

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().

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().

Real compute_probability_improvement ( const RealVector &  means,
const RealVector &  variances 
)
private
Real compute_expected_improvement ( const RealVector &  means,
const RealVector &  variances 
)
private
Real compute_lower_confidence_bound ( const RealVector &  means,
const RealVector &  variances 
)
private
Real compute_variances ( const RealVector &  variances)
private

variance function for the EGO MSE acquisition implementation

Compute the variances

Referenced by EffGlobalMinimizer::Variances_objective_eval().

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

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().

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().

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

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