SNLLOptimizer Class Reference

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

Inheritance diagram for SNLLOptimizer:

Public Member Functions
	SNLLOptimizer (ProblemDescDB &problem_db, Model &model)
	standard constructor More...
	SNLLOptimizer (const String &method_string, Model &model)
	alternate constructor for instantiations "on the fly" More...
	SNLLOptimizer (const RealVector &initial_pt, const RealVector &var_l_bnds, const RealVector &var_u_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_l_bnds, const RealVector &lin_ineq_u_bnds, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgts, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_tgts, void(*nlf1_obj_eval)(int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode), void(*nlf1_con_eval)(int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode), size_t max_iter=100, size_t max_eval=1000, Real conv_tol=1.e-4, Real grad_tol=1.e-4, Real max_step=1000.)
	alternate constructor for objective/constraint call-backs; analytic gradient case More...
	SNLLOptimizer (const RealVector &initial_pt, const RealVector &var_l_bnds, const RealVector &var_u_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_l_bnds, const RealVector &lin_ineq_u_bnds, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgts, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_tgts, void(*nlf0_obj_eval)(int n, const RealVector &x, double &f, int &result_mode), void(*nlf1_con_eval)(int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode), size_t max_iter=100, size_t max_eval=1000, Real conv_tol=1.e-4, Real grad_tol=1.e-4, Real max_step=1000.)
	alternate constructor for objective/constraint call-backs; mixed gradient case: numerical objective, analytic constraints More...
	SNLLOptimizer (const RealVector &initial_pt, const RealVector &var_l_bnds, const RealVector &var_u_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_l_bnds, const RealVector &lin_ineq_u_bnds, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgts, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_tgts, void(*nlf1_obj_eval)(int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode), void(*nlf0_con_eval)(int n, const RealVector &x, RealVector &g, int &result_mode), size_t max_iter=100, size_t max_eval=1000, Real conv_tol=1.e-4, Real grad_tol=1.e-4, Real max_step=1000.)
	alternate constructor for objective/constraint call-backs; mixed gradient case: analytic objective, numerical constraints More...
	SNLLOptimizer (const RealVector &initial_pt, const RealVector &var_l_bnds, const RealVector &var_u_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_l_bnds, const RealVector &lin_ineq_u_bnds, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_tgts, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_tgts, void(*nlf0_obj_eval)(int n, const RealVector &x, double &f, int &result_mode), void(*nlf0_con_eval)(int n, const RealVector &x, RealVector &g, int &result_mode), size_t max_iter=100, size_t max_eval=1000, Real conv_tol=1.e-4, Real grad_tol=1.e-4, Real max_step=1000.)
	alternate constructor for objective/constraint call-backs; numerical gradient case More...
	~SNLLOptimizer ()
	destructor
void	core_run ()
	Performs the iterations to determine the optimal solution.
void	reset ()
	restore initial state for repeated sub-iterator executions
void	declare_sources ()
	Declare sources to the evaluations database.
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)
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)
void	linear_constraints (const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_l_bnds, const RealVector &lin_ineq_u_bnds, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_targets)
	assign linear inequality and linear equality constraints for this iterator (user-functions mode for which Model updating is not used)
void	nonlinear_constraints (const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_targets)
	assign nonlinear inequality and equality constraint allowables for this iterator (user-functions mode for which Model updating is not used)
Public Member Functions inherited from Optimizer
void	get_common_stopping_criteria (int &max_fn_evals, int &max_iters, double &conv_tol, double &min_var_chg, double &obj_target)
int	num_nonlin_ineq_constraints_found () const
template<typename AdapterT >
bool	get_variable_bounds_from_dakota (typename AdapterT::VecT &lower, typename AdapterT::VecT &upper)
template<typename VecT >
void	get_responses_from_dakota (const RealVector &dak_fn_vals, VecT &funs, VecT &cEqs, VecT &cIneqs)
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	derived_set_communicators (ParLevLIter pl_iter)
	derived class contributions to setting the communicators associated with this Iterator instance
virtual void	derived_free_communicators (ParLevLIter pl_iter)
	derived class contributions to freeing the communicators associated with this Iterator instance
virtual void	pre_run ()
	pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori More...
virtual void	pre_output ()
	write variables to file, following pre-run
virtual void	post_input ()
	read tabular data for post-run mode
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_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	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
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.
Public Member Functions inherited from SNLLBase
	SNLLBase ()
	default constructor
	SNLLBase (ProblemDescDB &problem_db)
	standard constructor
	~SNLLBase ()
	destructor

Protected Member Functions
void	initialize_run ()
	invokes Optimizer::initialize_run(), SNLLBase::snll_initialize_run(), and performs other set-up
void	post_run (std::ostream &s)
	performs data recovery and calls Optimizer::post_run()
void	finalize_run ()
	performs cleanup, restores instances and calls parent finalize
Protected Member Functions inherited from Optimizer
	Optimizer (std::shared_ptr< TraitsBase > traits)
	default constructor
	Optimizer (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits)
	alternate constructor; accepts a model
	Optimizer (unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits)
	alternate constructor for "on the fly" instantiations
	Optimizer (unsigned short method_name, size_t num_cv, size_t num_div, size_t num_dsv, size_t num_drv, 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)
	alternate constructor for "on the fly" instantiations
	~Optimizer ()
	destructor
void	print_results (std::ostream &s, short results_state=FINAL_RESULTS)
void	configure_constraint_maps ()
int	configure_inequality_constraints (CONSTRAINT_TYPE ctype)
void	configure_equality_constraints (CONSTRAINT_TYPE ctype, size_t index_offset)
template<typename AdapterT >
void	get_linear_constraints_and_bounds (typename AdapterT::VecT &lin_ineq_lower_bnds, typename AdapterT::VecT &lin_ineq_upper_bnds, typename AdapterT::VecT &lin_eq_targets, typename AdapterT::MatT &lin_ineq_coeffs, typename AdapterT::MatT &lin_eq_coeffs)
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
const Model &	algorithm_space_model () const
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 void	derived_init_communicators (ParLevLIter pl_iter)
	derived class contributions to initializing the communicators associated with this Iterator instance
virtual const VariablesArray &	initial_points () const
	gets the multiple initial points for this iterator. This will only be meaningful after a call to initial_points mutator.
StrStrSizet	run_identifier () const
	get the unique run identifier based on method name, id, and number of executions
void	initialize_model_graphics (Model &model, int iterator_server_id)
	helper function that encapsulates initialization operations, modular on incoming Model instance More...
void	export_final_surrogates (Model &data_fit_surr_model)
	export final surrogates generated, e.g., GP in EGO and friends More...
Protected Member Functions inherited from SNLLBase
void	copy_con_vals_dak_to_optpp (const RealVector &local_fn_vals, RealVector &g, size_t offset)
	convenience function for copying local_fn_vals to g; used by constraint evaluator functions
void	copy_con_vals_optpp_to_dak (const RealVector &g, RealVector &local_fn_vals, size_t offset)
	convenience function for copying g to local_fn_vals; used in final solution logging
void	copy_con_grad (const RealMatrix &local_fn_grads, RealMatrix &grad_g, size_t offset)
	convenience function for copying local_fn_grads to grad_g; used by constraint evaluator functions
void	copy_con_hess (const RealSymMatrixArray &local_fn_hessians, OPTPP::OptppArray< RealSymMatrix > &hess_g, size_t offset)
	convenience function for copying local_fn_hessians to hess_g; used by constraint evaluator functions
void	snll_pre_instantiate (bool bound_constr_flag, int num_constr)
	convenience function for setting OPT++ options prior to the method instantiation
void	snll_post_instantiate (int num_cv, bool vendor_num_grad_flag, const String &finite_diff_type, const RealVector &fdss, size_t max_iter, size_t max_eval, Real conv_tol, Real grad_tol, Real max_step, bool bound_constr_flag, int num_constr, short output_lev, OPTPP::OptimizeClass *the_optimizer, OPTPP::NLP0 *nlf_objective, OPTPP::FDNLF1 *fd_nlf1, OPTPP::FDNLF1 *fd_nlf1_con)
	convenience function for setting OPT++ options after the method instantiation
void	snll_initialize_run (OPTPP::NLP0 *nlf_objective, OPTPP::NLP *nlp_constraint, const RealVector &init_pt, bool bound_constr_flag, const RealVector &lower_bnds, const RealVector &upper_bnds, const RealMatrix &lin_ineq_coeffs, const RealVector &lin_ineq_l_bnds, const RealVector &lin_ineq_u_bnds, const RealMatrix &lin_eq_coeffs, const RealVector &lin_eq_targets, const RealVector &nln_ineq_l_bnds, const RealVector &nln_ineq_u_bnds, const RealVector &nln_eq_targets)
	convenience function for OPT++ configuration prior to the method invocation
void	snll_post_run (OPTPP::NLP0 *nlf_objective)
	convenience function for managing OPT++ results after method execution
void	snll_finalize_run (OPTPP::NLP0 *nlf_objective)
	convenience function for clearing OPT++ data after method execution
void	reset_base ()
	reset last{FnEvalLocn,EvalMode,EvalVars}

Private Member Functions
void	default_instantiate_q_newton (void(*obj_eval)(int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode))
	instantiate an OPTPP_Q_NEWTON solver using standard settings
void	default_instantiate_q_newton (void(*obj_eval)(int n, const RealVector &x, double &f, int &result_mode))
	instantiate an OPTPP_Q_NEWTON solver using standard settings
void	default_instantiate_constraint (void(*con_eval)(int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode))
	instantiate constraint objectives using standard settings
void	default_instantiate_constraint (void(*con_eval)(int n, const RealVector &x, RealVector &g, int &result_mode))
	instantiate constraint objectives using standard settings
void	default_instantiate_newton (void(*obj_eval)(int mode, int n, const RealVector &x, double &f, RealVector &grad_f, RealSymMatrix &hess_f, int &result_mode), void(*con_eval)(int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, OPTPP::OptppArray< RealSymMatrix > &hess_g, int &result_mode))
	instantiate an OPTPP_NEWTON solver using standard settings

Static Private Member Functions
static void	nlf0_evaluator (int n, const RealVector &x, double &f, int &result_mode)
	objective function evaluator function for OPT++ methods which require only function values. More...
static void	nlf1_evaluator (int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode)
	objective function evaluator function which provides function values and gradients to OPT++ methods. More...
static void	nlf2_evaluator (int mode, int n, const RealVector &x, double &f, RealVector &grad_f, RealSymMatrix &hess_f, int &result_mode)
	objective function evaluator function which provides function values, gradients, and Hessians to OPT++ methods. More...
static void	constraint0_evaluator (int n, const RealVector &x, RealVector &g, int &result_mode)
	constraint evaluator function for OPT++ methods which require only constraint values. More...
static void	constraint1_evaluator (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode)
	constraint evaluator function which provides constraint values and gradients to OPT++ methods. More...
static void	constraint2_evaluator (int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, OPTPP::OptppArray< RealSymMatrix > &hess_g, int &result_mode)
	constraint evaluator function which provides constraint values, gradients, and Hessians to OPT++ methods. More...

Private Attributes
SNLLOptimizer *	prevSnllOptInstance
	pointer to the previously active object instance used for restoration in the case of iterator/model recursion
OPTPP::NLP0 *	nlfObjective
	objective NLF base class pointer
OPTPP::NLP0 *	nlfConstraint
	constraint NLF base class pointer
OPTPP::NLP *	nlpConstraint
	constraint NLP pointer
OPTPP::NLF0 *	nlf0
	pointer to objective NLF for nongradient optimizers
OPTPP::NLF1 *	nlf1
	pointer to objective NLF for (analytic) gradient-based optimizers
OPTPP::NLF1 *	nlf1Con
	pointer to constraint NLF for (analytic) gradient-based optimizers
OPTPP::FDNLF1 *	fdnlf1
	pointer to objective NLF for (finite diff) gradient-based optimizers
OPTPP::FDNLF1 *	fdnlf1Con
	pointer to constraint NLF for (finite diff) gradient-based optimizers
OPTPP::NLF2 *	nlf2
	pointer to objective NLF for full Newton optimizers
OPTPP::NLF2 *	nlf2Con
	pointer to constraint NLF for full Newton optimizers
OPTPP::OptimizeClass *	theOptimizer
	optimizer base class pointer
OPTPP::OptPDS *	optpds
	PDS optimizer pointer.
OPTPP::OptCG *	optcg
	CG optimizer pointer.
OPTPP::OptLBFGS *	optlbfgs
	L-BFGS optimizer pointer.
OPTPP::OptNewton *	optnewton
	Newton optimizer pointer.
OPTPP::OptQNewton *	optqnewton
	Quasi-Newton optimizer pointer.
OPTPP::OptFDNewton *	optfdnewton
	Finite Difference Newton opt pointer.
OPTPP::OptBCNewton *	optbcnewton
	Bound constrained Newton opt pointer.
OPTPP::OptBCQNewton *	optbcqnewton
	Bnd constrained Quasi-Newton opt ptr.
OPTPP::OptBCFDNewton *	optbcfdnewton
	Bnd constrained FD-Newton opt ptr.
OPTPP::OptNIPS *	optnips
	NIPS optimizer pointer.
OPTPP::OptQNIPS *	optqnips
	Quasi-Newton NIPS optimizer pointer.
OPTPP::OptFDNIPS *	optfdnips
	Finite Difference NIPS opt pointer.
String	setUpType
	flag for iteration mode: "model" (normal usage) or "user_functions" (user-supplied functions mode for "on the fly" instantiations). NonDReliability currently uses the user_functions mode.
RealVector	initialPoint
	initial point used in "user_functions" mode
RealVector	lowerBounds
	variable lower bounds used in "user_functions" mode
RealVector	upperBounds
	variable upper bounds used in "user_functions" mode
RealMatrix	linIneqCoeffs
	linear inequality constraint coefficients used in "user_functions" mode
RealVector	linIneqLowerBnds
	linear inequality constraint lower bounds used in "user_functions" mode
RealVector	linIneqUpperBnds
	linear inequality constraint upper bounds used in "user_functions" mode
RealMatrix	linEqCoeffs
	linear equality constraint coefficients used in "user_functions" mode
RealVector	linEqTargets
	linear equality constraint targets used in "user_functions" mode
RealVector	nlnIneqLowerBnds
	nonlinear inequality constraint lower bounds used in "user_functions" mode
RealVector	nlnIneqUpperBnds
	nonlinear inequality constraint upper bounds used in "user_functions" mode
RealVector	nlnEqTargets
	nonlinear equality constraint targets used in "user_functions" mode

Static Private Attributes
static SNLLOptimizer *	snllOptInstance
	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 Optimizer
static void	not_available (const std::string &package_name)
	Static helper function: third-party opt packages which are not available.
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...
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...
Static Protected Member Functions inherited from SNLLBase
static void	init_fn (int n, RealVector &x)
	An initialization mechanism provided by OPT++ (not currently used).
Protected Attributes inherited from Optimizer
size_t	numObjectiveFns
	number of objective functions (iterator view)
bool	localObjectiveRecast
	flag indicating whether local recasting to a single objective is used
Optimizer *	prevOptInstance
	pointer containing previous value of optimizerInstance
int	numNonlinearIneqConstraintsFound
	number of nonlinear ineq constraints actually used (based on conditional and bigRealBoundSize
std::vector< int >	constraintMapIndices
	map from Dakota constraint number to APPS constraint number
std::vector< double >	constraintMapMultipliers
	multipliers for constraint transformations
std::vector< double >	constraintMapOffsets
	offsets for constraint transformations
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
Protected Attributes inherited from SNLLBase
String	searchMethod
	value_based_line_search, gradient_based_line_search, trust_region, or tr_pds
OPTPP::SearchStrategy	searchStrat
	enum: LineSearch, TrustRegion, or TrustPDS
OPTPP::MeritFcn	meritFn
	enum: NormFmu, ArgaezTapia, or VanShanno
Real	maxStep
	value from max_step specification
Real	stepLenToBndry
	value from steplength_to_boundary specification
Real	centeringParam
	value from centering_parameter specification
bool	constantASVFlag
	flags a user selection of active_set_vector == constant. By mapping this into mode override, reliance on duplicate detection can be avoided.
Static Protected Attributes inherited from Optimizer
static Optimizer *	optimizerInstance
	pointer to Optimizer instance used in static member functions
Static Protected Attributes inherited from Minimizer
static Minimizer *	minimizerInstance
	pointer to Minimizer used in static member functions
Static Protected Attributes inherited from SNLLBase
static Minimizer *	optLSqInstance
	pointer to the active base class object instance used within the static evaluator functions in order to avoid the need for static data
static bool	modeOverrideFlag
	flags OPT++ mode override (for combining value, gradient, and Hessian requests)
static EvalType	lastFnEvalLocn
	an enum used to track whether an nlf evaluator or a constraint evaluator was the last location of a function evaluation
static int	lastEvalMode
	copy of mode from constraint evaluators
static RealVector	lastEvalVars
	copy of variables from constraint evaluators

Detailed Description

Wrapper class for the OPT++ optimization library.

The SNLLOptimizer class provides a wrapper for OPT++, a C++ optimization library of nonlinear programming and pattern search techniques from the Computational Sciences and Mathematics Research (CSMR) department at Sandia's Livermore CA site. It uses a function pointer approach for which passed functions must be either global functions or static member functions. Any attribute used within static member functions must be either local to that function, a static member, or accessed by static pointer.

The user input mappings are as follows: max_iterations, max_function_evaluations, convergence_tolerance, max_step, gradient_tolerance, search_method, and search_scheme_size are set using OPT++'s setMaxIter(), setMaxFeval(), setFcnTol(), setMaxStep(), setGradTol(), setSearchStrategy(), and setSSS() member functions, respectively; output verbosity is used to toggle OPT++'s debug mode using the setDebug() member function. Internal to OPT++, there are 3 search strategies, while the DAKOTA search_method specification supports 4 (value_based_line_search, gradient_based_line_search, trust_region, or tr_pds). The difference stems from the "is_expensive" flag in OPT++. If the search strategy is LineSearch and "is_expensive" is turned on, then the value_based_line_search is used. Otherwise (the "is_expensive" default is off), the algorithm will use the gradient_based_line_search. Refer to [Meza, J.C., 1994] and to the OPT++ source in the Dakota/packages/OPTPP directory for information on OPT++ class member functions.

Constructor & Destructor Documentation

SNLLOptimizer() [1/6]

SNLLOptimizer	(	ProblemDescDB &	problem_db,
		Model &	model
	)

standard constructor

This constructor is used for normal instantiations using data from the ProblemDescDB.

References Dakota::abort_handler(), Minimizer::boundConstraintFlag, SNLLBase::centeringParam, SNLLOptimizer::constraint0_evaluator(), SNLLOptimizer::constraint1_evaluator(), SNLLOptimizer::constraint2_evaluator(), Iterator::convergenceTol, SNLLOptimizer::default_instantiate_constraint(), SNLLOptimizer::default_instantiate_newton(), SNLLOptimizer::default_instantiate_q_newton(), Model::fd_gradient_step_size(), SNLLOptimizer::fdnlf1, SNLLOptimizer::fdnlf1Con, ProblemDescDB::get_int(), ProblemDescDB::get_real(), SNLLBase::init_fn(), Model::interval_type(), Iterator::iteratedModel, Dakota::LARGE_SCALE, Iterator::maxEvalConcurrency, Iterator::maxFunctionEvals, Iterator::maxIterations, SNLLBase::maxStep, SNLLBase::meritFn, Iterator::method_enum_to_string(), Iterator::methodName, SNLLOptimizer::nlf0, SNLLOptimizer::nlf0_evaluator(), SNLLOptimizer::nlf1, SNLLOptimizer::nlf1_evaluator(), SNLLOptimizer::nlf1Con, SNLLOptimizer::nlf2_evaluator(), SNLLOptimizer::nlfConstraint, SNLLOptimizer::nlfObjective, SNLLOptimizer::nlpConstraint, Minimizer::numConstraints, Minimizer::numContinuousVars, Minimizer::numNonlinearConstraints, SNLLOptimizer::optbcfdnewton, SNLLOptimizer::optbcqnewton, SNLLOptimizer::optcg, SNLLOptimizer::optfdnewton, SNLLOptimizer::optfdnips, SNLLOptimizer::optlbfgs, SNLLOptimizer::optpds, SNLLOptimizer::optqnewton, SNLLOptimizer::optqnips, Iterator::outputLevel, Iterator::probDescDB, SNLLBase::searchStrat, SNLLBase::snll_post_instantiate(), SNLLBase::snll_pre_instantiate(), SNLLBase::stepLenToBndry, SNLLOptimizer::theOptimizer, and Minimizer::vendorNumericalGradFlag.

SNLLOptimizer() [2/6]

SNLLOptimizer	(	const String &	method_string,
		Model &	model
	)

alternate constructor for instantiations "on the fly"

This is an alternate constructor for instantiations on the fly using a Model but no ProblemDescDB.

References Dakota::abort_handler(), Minimizer::boundConstraintFlag, SNLLOptimizer::constraint1_evaluator(), SNLLOptimizer::constraint2_evaluator(), Iterator::convergenceTol, SNLLOptimizer::default_instantiate_constraint(), SNLLOptimizer::default_instantiate_newton(), SNLLOptimizer::default_instantiate_q_newton(), Model::fd_gradient_step_size(), Model::interval_type(), Iterator::iteratedModel, Iterator::maxFunctionEvals, Iterator::maxIterations, Iterator::method_enum_to_string(), Iterator::methodName, SNLLOptimizer::nlf1_evaluator(), SNLLOptimizer::nlf2_evaluator(), SNLLOptimizer::nlfObjective, Minimizer::numConstraints, Minimizer::numContinuousVars, Iterator::outputLevel, SNLLBase::snll_post_instantiate(), SNLLBase::snll_pre_instantiate(), SNLLOptimizer::theOptimizer, and Minimizer::vendorNumericalGradFlag.

SNLLOptimizer() [3/6]

SNLLOptimizer	(	const RealVector &	initial_pt,
		const RealVector &	var_l_bnds,
		const RealVector &	var_u_bnds,
		const RealMatrix &	lin_ineq_coeffs,
		const RealVector &	lin_ineq_l_bnds,
		const RealVector &	lin_ineq_u_bnds,
		const RealMatrix &	lin_eq_coeffs,
		const RealVector &	lin_eq_tgts,
		const RealVector &	nln_ineq_l_bnds,
		const RealVector &	nln_ineq_u_bnds,
		const RealVector &	nln_eq_tgts,
		void(*)(int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode)	nlf1_obj_eval,
		void(*)(int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode)	nlf1_con_eval,
		size_t	max_iter = 100,
		size_t	max_eval = 1000,
		Real	conv_tol = 1.e-4,
		Real	grad_tol = 1.e-4,
		Real	max_step = 1000.
	)

alternate constructor for objective/constraint call-backs; analytic gradient case

This is an alternate constructor for performing an optimization using the passed in objective function and constraint function pointers.

References Minimizer::bigRealBoundSize, Minimizer::boundConstraintFlag, Dakota::copy_data(), SNLLOptimizer::default_instantiate_constraint(), SNLLOptimizer::default_instantiate_q_newton(), SNLLOptimizer::initialPoint, SNLLOptimizer::lowerBounds, SNLLOptimizer::nlfObjective, Minimizer::numConstraints, Minimizer::numContinuousVars, Iterator::outputLevel, SNLLBase::snll_post_instantiate(), SNLLBase::snll_pre_instantiate(), SNLLOptimizer::theOptimizer, and SNLLOptimizer::upperBounds.

SNLLOptimizer() [4/6]

SNLLOptimizer	(	const RealVector &	initial_pt,
		const RealVector &	var_l_bnds,
		const RealVector &	var_u_bnds,
		const RealMatrix &	lin_ineq_coeffs,
		const RealVector &	lin_ineq_l_bnds,
		const RealVector &	lin_ineq_u_bnds,
		const RealMatrix &	lin_eq_coeffs,
		const RealVector &	lin_eq_tgts,
		const RealVector &	nln_ineq_l_bnds,
		const RealVector &	nln_ineq_u_bnds,
		const RealVector &	nln_eq_tgts,
		void(*)(int n, const RealVector &x, double &f, int &result_mode)	nlf0_obj_eval,
		void(*)(int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode)	nlf1_con_eval,
		size_t	max_iter = 100,
		size_t	max_eval = 1000,
		Real	conv_tol = 1.e-4,
		Real	grad_tol = 1.e-4,
		Real	max_step = 1000.
	)

alternate constructor for objective/constraint call-backs; mixed gradient case: numerical objective, analytic constraints

This is an alternate constructor for performing an optimization using the passed in objective function and constraint function pointers.

References Minimizer::bigRealBoundSize, Minimizer::boundConstraintFlag, Dakota::copy_data(), SNLLOptimizer::default_instantiate_constraint(), SNLLOptimizer::default_instantiate_q_newton(), SNLLOptimizer::initialPoint, SNLLOptimizer::lowerBounds, SNLLOptimizer::nlfObjective, Minimizer::numConstraints, Minimizer::numContinuousVars, Iterator::outputLevel, SNLLBase::snll_post_instantiate(), SNLLBase::snll_pre_instantiate(), SNLLOptimizer::theOptimizer, and SNLLOptimizer::upperBounds.

SNLLOptimizer() [5/6]

SNLLOptimizer	(	const RealVector &	initial_pt,
		const RealVector &	var_l_bnds,
		const RealVector &	var_u_bnds,
		const RealMatrix &	lin_ineq_coeffs,
		const RealVector &	lin_ineq_l_bnds,
		const RealVector &	lin_ineq_u_bnds,
		const RealMatrix &	lin_eq_coeffs,
		const RealVector &	lin_eq_tgts,
		const RealVector &	nln_ineq_l_bnds,
		const RealVector &	nln_ineq_u_bnds,
		const RealVector &	nln_eq_tgts,
		void(*)(int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode)	nlf1_obj_eval,
		void(*)(int n, const RealVector &x, RealVector &g, int &result_mode)	nlf0_con_eval,
		size_t	max_iter = 100,
		size_t	max_eval = 1000,
		Real	conv_tol = 1.e-4,
		Real	grad_tol = 1.e-4,
		Real	max_step = 1000.
	)

alternate constructor for objective/constraint call-backs; mixed gradient case: analytic objective, numerical constraints

This is an alternate constructor for performing an optimization using the passed in objective function and constraint function pointers.

References Minimizer::bigRealBoundSize, Minimizer::boundConstraintFlag, Dakota::copy_data(), SNLLOptimizer::default_instantiate_constraint(), SNLLOptimizer::default_instantiate_q_newton(), SNLLOptimizer::initialPoint, SNLLOptimizer::lowerBounds, SNLLOptimizer::nlfObjective, Minimizer::numConstraints, Minimizer::numContinuousVars, Iterator::outputLevel, SNLLBase::snll_post_instantiate(), SNLLBase::snll_pre_instantiate(), SNLLOptimizer::theOptimizer, and SNLLOptimizer::upperBounds.

SNLLOptimizer() [6/6]

SNLLOptimizer	(	const RealVector &	initial_pt,
		const RealVector &	var_l_bnds,
		const RealVector &	var_u_bnds,
		const RealMatrix &	lin_ineq_coeffs,
		const RealVector &	lin_ineq_l_bnds,
		const RealVector &	lin_ineq_u_bnds,
		const RealMatrix &	lin_eq_coeffs,
		const RealVector &	lin_eq_tgts,
		const RealVector &	nln_ineq_l_bnds,
		const RealVector &	nln_ineq_u_bnds,
		const RealVector &	nln_eq_tgts,
		void(*)(int n, const RealVector &x, double &f, int &result_mode)	nlf0_obj_eval,
		void(*)(int n, const RealVector &x, RealVector &g, int &result_mode)	nlf0_con_eval,
		size_t	max_iter = 100,
		size_t	max_eval = 1000,
		Real	conv_tol = 1.e-4,
		Real	grad_tol = 1.e-4,
		Real	max_step = 1000.
	)

alternate constructor for objective/constraint call-backs; numerical gradient case

This is an alternate constructor for performing an optimization using the passed in objective function and constraint function pointers.

References Minimizer::bigRealBoundSize, Minimizer::boundConstraintFlag, Dakota::copy_data(), SNLLOptimizer::default_instantiate_constraint(), SNLLOptimizer::default_instantiate_q_newton(), SNLLOptimizer::initialPoint, SNLLOptimizer::lowerBounds, SNLLOptimizer::nlfObjective, Minimizer::numConstraints, Minimizer::numContinuousVars, Iterator::outputLevel, SNLLBase::snll_post_instantiate(), SNLLBase::snll_pre_instantiate(), SNLLOptimizer::theOptimizer, and SNLLOptimizer::upperBounds.

Member Function Documentation

nlf0_evaluator()

void nlf0_evaluator ( int  n, const RealVector &  x, double &  f, int &  result_mode  )

staticprivate

objective function evaluator function for OPT++ methods which require only function values.

For use when DAKOTA computes f and gradients are not directly available. This is used by nongradient-based optimizers such as PDS and by gradient-based optimizers in vendor numerical gradient mode (opt++'s internal finite difference routine is used).

References Model::continuous_variables(), Model::current_response(), Model::evaluate(), Response::function_value(), Iterator::iteratedModel, SNLLBase::lastEvalVars, SNLLBase::lastFnEvalLocn, Minimizer::numNonlinearConstraints, Iterator::outputLevel, Model::primary_response_fn_sense(), and SNLLOptimizer::snllOptInstance.

Referenced by SNLLOptimizer::SNLLOptimizer().

nlf1_evaluator()

void nlf1_evaluator ( int  mode, int  n, const RealVector &  x, double &  f, RealVector &  grad_f, int &  result_mode  )

staticprivate

objective function evaluator function which provides function values and gradients to OPT++ methods.

For use when DAKOTA computes f and df/dX (regardless of gradient type). Vendor numerical gradient case is handled by nlf0_evaluator.

References Iterator::activeSet, Model::continuous_variables(), Model::current_response(), Model::evaluate(), Response::function_gradient_copy(), Response::function_value(), Iterator::iteratedModel, SNLLBase::lastEvalMode, SNLLBase::lastEvalVars, SNLLBase::lastFnEvalLocn, Minimizer::numNonlinearConstraints, Iterator::outputLevel, Model::primary_response_fn_sense(), ActiveSet::request_values(), and SNLLOptimizer::snllOptInstance.

Referenced by SNLLOptimizer::SNLLOptimizer().

nlf2_evaluator()

void nlf2_evaluator ( int  mode, int  n, const RealVector &  x, double &  f, RealVector &  grad_f, RealSymMatrix &  hess_f, int &  result_mode  )

staticprivate

objective function evaluator function which provides function values, gradients, and Hessians to OPT++ methods.

For use when DAKOTA receives f, df/dX, & d^2f/dx^2 from the ApplicationInterface (analytic only). Finite differencing does not make sense for a full Newton approach, since lack of analytic gradients & Hessian should dictate the use of quasi-newton or fd-newton. Thus, there is no fdnlf2_evaluator for use with full Newton approaches, since it is preferable to use quasi-newton or fd-newton with nlf1. Gauss-Newton does not fit this model; it uses nlf2_evaluator_gn instead of nlf2_evaluator.

References Iterator::activeSet, Model::continuous_variables(), Model::current_response(), Model::evaluate(), Response::function_gradient_copy(), Response::function_hessian(), Response::function_value(), Iterator::iteratedModel, SNLLBase::lastEvalMode, SNLLBase::lastEvalVars, SNLLBase::lastFnEvalLocn, Minimizer::numNonlinearConstraints, Iterator::outputLevel, Model::primary_response_fn_sense(), ActiveSet::request_values(), and SNLLOptimizer::snllOptInstance.

Referenced by SNLLOptimizer::SNLLOptimizer().

constraint0_evaluator()

void constraint0_evaluator ( int  n, const RealVector &  x, RealVector &  g, int &  result_mode  )

staticprivate

constraint evaluator function for OPT++ methods which require only constraint values.

For use when DAKOTA computes g and gradients are not directly available. This is used by nongradient-based optimizers and by gradient-based optimizers in vendor numerical gradient mode (opt++'s internal finite difference routine is used).

References Model::continuous_variables(), SNLLBase::copy_con_vals_dak_to_optpp(), Model::current_response(), Model::evaluate(), Response::function_values(), Iterator::iteratedModel, SNLLBase::lastEvalVars, SNLLBase::lastFnEvalLocn, Optimizer::numObjectiveFns, Iterator::outputLevel, and SNLLOptimizer::snllOptInstance.

Referenced by SNLLOptimizer::SNLLOptimizer().

constraint1_evaluator()

void constraint1_evaluator ( int  mode, int  n, const RealVector &  x, RealVector &  g, RealMatrix &  grad_g, int &  result_mode  )

staticprivate

constraint evaluator function which provides constraint values and gradients to OPT++ methods.

For use when DAKOTA computes g and dg/dX (regardless of gradient type). Vendor numerical gradient case is handled by constraint0_evaluator.

References Iterator::activeSet, Model::continuous_variables(), SNLLBase::copy_con_grad(), SNLLBase::copy_con_vals_dak_to_optpp(), Model::current_response(), Model::evaluate(), Response::function_gradients(), Response::function_values(), Iterator::iteratedModel, SNLLBase::lastEvalMode, SNLLBase::lastEvalVars, SNLLBase::lastFnEvalLocn, Optimizer::numObjectiveFns, Iterator::outputLevel, ActiveSet::request_values(), and SNLLOptimizer::snllOptInstance.

Referenced by SNLLOptimizer::SNLLOptimizer().

constraint2_evaluator()

void constraint2_evaluator ( int  mode, int  n, const RealVector &  x, RealVector &  g, RealMatrix &  grad_g, OPTPP::OptppArray< RealSymMatrix > &  hess_g, int &  result_mode  )

staticprivate

constraint evaluator function which provides constraint values, gradients, and Hessians to OPT++ methods.

For use when DAKOTA computes g, dg/dX, & d^2g/dx^2 (analytic only).

References Iterator::activeSet, Model::continuous_variables(), SNLLBase::copy_con_grad(), SNLLBase::copy_con_hess(), SNLLBase::copy_con_vals_dak_to_optpp(), Model::current_response(), Model::evaluate(), Response::function_gradients(), Response::function_hessians(), Response::function_values(), Iterator::iteratedModel, SNLLBase::lastEvalMode, SNLLBase::lastEvalVars, SNLLBase::lastFnEvalLocn, Optimizer::numObjectiveFns, Iterator::outputLevel, ActiveSet::request_values(), and SNLLOptimizer::snllOptInstance.

Referenced by SNLLOptimizer::SNLLOptimizer().

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

• SNLLOptimizer.hpp
• SNLLOptimizer.cpp