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

Inheritance diagram for SurrBasedMinimizer:

Protected Member Functions
	SurrBasedMinimizer (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits)
	constructor

	~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	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 ()
	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	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...

void	finalize_run ()
	utility function to perform common operations following post_run(); deallocation and resetting of instance pointers More...

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...

void	local_recast_retrieve (const Variables &vars, Response &response) const
	infers MOO/NLS solution from the solution of a single-objective optimizer 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...

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 Attributes
Iterator	approxSubProbMinimizer
	the minimizer used on the surrogate model to solve the approximate subproblem on each surrogate-based iteration

size_t	globalIterCount
	global iteration counter corresponding to number of surrogate-based minimizations

RealVector	lagrangeMult
	Lagrange multipliers for basic Lagrangian calculations.

RealVector	augLagrangeMult
	Lagrange multipliers for augmented Lagrangian calculations.

Real	penaltyParameter
	the penalization factor for violated constraints used in quadratic penalty calculations; increased in update_penalty()

RealVector	origNonlinIneqLowerBnds
	original nonlinear inequality constraint lower bounds (no relaxation)

RealVector	origNonlinIneqUpperBnds
	original nonlinear inequality constraint upper bounds (no relaxation)

RealVector	origNonlinEqTargets
	original nonlinear equality constraint targets (no relaxation)

Real	eta
	constant used in etaSequence updates

Real	alphaEta
	power for etaSequence updates when updating penalty

Real	betaEta
	power for etaSequence updates when updating multipliers

Real	etaSequence
	decreasing sequence of allowable constraint violation used in augmented Lagrangian updates (refer to Conn, Gould, and Toint, section 14.4)

size_t	miPLIndex
	index for the active ParallelLevel within ParallelConfiguration::miPLIters

Protected Attributes inherited from Minimizer
size_t	numFunctions
	number of response functions

size_t	numContinuousVars
	number of active continuous vars

size_t	numDiscreteIntVars
	number of active discrete integer vars

size_t	numDiscreteStringVars
	number of active discrete string vars

size_t	numDiscreteRealVars
	number of active discrete real vars

Real	constraintTol
	optimizer/least squares constraint tolerance

Real	bigRealBoundSize
	cutoff value for inequality constraint and continuous variable bounds

int	bigIntBoundSize
	cutoff value for discrete variable bounds

size_t	numNonlinearIneqConstraints
	number of nonlinear inequality constraints

size_t	numNonlinearEqConstraints
	number of nonlinear equality constraints

size_t	numLinearIneqConstraints
	number of linear inequality constraints

size_t	numLinearEqConstraints
	number of linear equality constraints

size_t	numNonlinearConstraints
	total number of nonlinear constraints

size_t	numLinearConstraints
	total number of linear constraints

size_t	numConstraints
	total number of linear and nonlinear constraints

bool	optimizationFlag
	flag for use where optimization and NLS must be distinguished

size_t	numUserPrimaryFns
	number of objective functions or least squares terms in the inbound model; always initialize at Minimizer, even if overridden later

size_t	numIterPrimaryFns
	number of objective functions or least squares terms in iterator's view, after transformations; always initialize at Minimizer, even if overridden later

bool	boundConstraintFlag
	convenience flag for denoting the presence of user-specified bound constraints. Used for method selection and error checking.

bool	speculativeFlag
	flag for speculative gradient evaluations

bool	calibrationDataFlag
	flag indicating whether user-supplied calibration data is active

ExperimentData	expData
	Container for experimental data to which to calibrate model using least squares or other formulations which minimize SSE.

size_t	numExperiments
	number of experiments

size_t	numTotalCalibTerms
	number of total calibration terms (sum over experiments of number of experimental data per experiment, including field data)

Model	dataTransformModel
	Shallow copy of the data transformation model, when present (cached in case further wrapped by other transformations)

bool	scaleFlag
	whether Iterator-level scaling is active

Model	scalingModel
	Shallow copy of the scaling transformation model, when present (cached in case further wrapped by other transformations)

Minimizer *	prevMinInstance
	pointer containing previous value of minimizerInstance

bool	vendorNumericalGradFlag
	convenience flag for gradient_type == numerical && method_source == vendor

std::shared_ptr< TPLDataTransfer >	dataTransferHandler
	Emerging helper class for handling data transfers to/from Dakota and the underlying TPL.

Protected Attributes inherited from Iterator
ProblemDescDB &	probDescDB
	class member reference to the problem description database More...

ParallelLibrary &	parallelLib
	class member reference to the parallel library

ParConfigLIter	methodPCIter
	the active ParallelConfiguration used by this Iterator instance

Model	iteratedModel
	the model to be iterated (for iterators and meta-iterators employing a single model instance)

size_t	myModelLayers
	number of Models locally (in Iterator or derived classes) wrapped around the initially passed in Model

unsigned short	methodName
	name of the iterator (the user's method spec)

Real	convergenceTol
	iteration convergence tolerance

size_t	maxIterations
	maximum number of iterations for the method

size_t	maxFunctionEvals
	maximum number of fn evaluations for the method

int	maxEvalConcurrency
	maximum number of concurrent model evaluations More...

ActiveSet	activeSet
	the response data requirements on each function evaluation

size_t	numFinalSolutions
	number of solutions to retain in best variables/response arrays

VariablesArray	bestVariablesArray
	collection of N best solution variables found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)

ResponseArray	bestResponseArray
	collection of N best solution responses found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)

bool	subIteratorFlag
	flag indicating if this Iterator is a sub-iterator (NestedModel::subIterator or DataFitSurrModel::daceIterator)

short	outputLevel
	output verbosity level: {SILENT,QUIET,NORMAL,VERBOSE,DEBUG}_OUTPUT

bool	summaryOutputFlag
	flag for summary output (evaluation stats, final results); default true, but false for on-the-fly (helper) iterators and sub-iterator use cases

ResultsManager &	resultsDB
	reference to the global iterator results database

EvaluationStore &	evaluationsDB
	reference to the global evaluation database

EvaluationsDBState	evaluationsDBState
	State of evaluations DB for this iterator.

ResultsNames	resultsNames
	valid names for iterator results

std::shared_ptr< TraitsBase >	methodTraits
	pointer that retains shared ownership of a TraitsBase object, or child thereof

bool	topLevel
	Whether this is the top level iterator.

bool	exportSurrogate = false
	whether to export final surrogates

String	surrExportPrefix
	base filename for exported surrogates

unsigned short	surrExportFormat = NO_MODEL_FORMAT
	(bitwise) format(s) to export

Additional Inherited Members
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

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 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 Attributes inherited from Minimizer
static Minimizer *	minimizerInstance
	pointer to Minimizer used in static member functions

Detailed Description

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

These minimizers use a SurrogateModel to perform optimization based either on local trust region methods or global updating methods.

Member Function Documentation

void print_results	(	std::ostream &	s,
		short	results_state = `FINAL_RESULTS`
	)

protectedvirtual

Redefines default iterator results printing to include optimization results (objective functions and constraints).

Reimplemented from Iterator.

References Dakota::abort_handler(), Iterator::activeSet, Iterator::bestResponseArray, Iterator::bestVariablesArray, Dakota::data_pairs, Model::interface_id(), Iterator::iteratedModel, Dakota::lookup_by_val(), Iterator::methodName, Minimizer::numFunctions, Minimizer::numUserPrimaryFns, Minimizer::optimizationFlag, Minimizer::print_residuals(), ActiveSet::request_values(), and Model::truth_model().

void update_lagrange_multipliers	(	const RealVector &	fn_vals,
		const RealMatrix &	fn_grads,
		SurrBasedLevelData &	tr_data
	)

protected

initialize and update Lagrange multipliers for basic Lagrangian

For the Rockafellar augmented Lagrangian, simple Lagrange multiplier updates are available which do not require the active constraint gradients. For the basic Lagrangian, Lagrange multipliers are estimated through solution of a nonnegative linear least squares problem.

References Dakota::abort_handler(), Minimizer::bigRealBoundSize, Minimizer::constraintTol, Model::continuous_lower_bounds(), Model::continuous_upper_bounds(), Iterator::iteratedModel, SurrBasedMinimizer::lagrangeMult, Minimizer::numContinuousVars, Minimizer::numNonlinearEqConstraints, Minimizer::numNonlinearIneqConstraints, Minimizer::numUserPrimaryFns, Minimizer::objective_gradient(), SurrBasedMinimizer::origNonlinIneqLowerBnds, SurrBasedMinimizer::origNonlinIneqUpperBnds, Model::primary_response_fn_sense(), and Model::primary_response_fn_weights().

Referenced by SurrBasedLocalMinimizer::hard_convergence_check().

void update_augmented_lagrange_multipliers ( const RealVector & fn_vals)

protected

initialize and update the Lagrange multipliers for augmented Lagrangian

For the Rockafellar augmented Lagrangian, simple Lagrange multiplier updates are available which do not require the active constraint gradients. For the basic Lagrangian, Lagrange multipliers are estimated through solution of a nonnegative linear least squares problem.

References SurrBasedMinimizer::augLagrangeMult, SurrBasedMinimizer::betaEta, Minimizer::bigRealBoundSize, SurrBasedMinimizer::etaSequence, Minimizer::numNonlinearEqConstraints, Minimizer::numNonlinearIneqConstraints, Minimizer::numUserPrimaryFns, SurrBasedMinimizer::origNonlinEqTargets, SurrBasedMinimizer::origNonlinIneqLowerBnds, SurrBasedMinimizer::origNonlinIneqUpperBnds, and SurrBasedMinimizer::penaltyParameter.

Referenced by SurrBasedLocalMinimizer::compute_trust_region_ratio(), SurrBasedLocalMinimizer::hard_convergence_check(), and EffGlobalMinimizer::update_constraints().

bool update_filter	(	SurrBasedLevelData &	tr_data,
		const RealVector &	fn_vals
	)

protected

update filter using a new set of function values

Update the paretoFilter with fn_vals if new iterate is non-dominated.

References SurrBasedMinimizer::constraint_violation(), Iterator::iteratedModel, Minimizer::numNonlinearConstraints, Minimizer::objective(), Model::primary_response_fn_sense(), and Model::primary_response_fn_weights().

Referenced by SurrBasedLocalMinimizer::compute_trust_region_ratio().

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
	)

protected

compute a Lagrangian function from a set of function values

The Lagrangian function computation sums the objective function and the Lagrange multipler terms for inequality/equality constraints. This implementation follows the convention in Vanderplaats with g<=0 and h=0. The bounds/targets passed in may reflect the original constraints or the relaxed constraints.

References Minimizer::bigRealBoundSize, Minimizer::constraintTol, SurrBasedMinimizer::lagrangeMult, Minimizer::numNonlinearEqConstraints, Minimizer::numNonlinearIneqConstraints, Minimizer::numUserPrimaryFns, and Minimizer::objective().

Referenced by SurrBasedLocalMinimizer::approx_subprob_objective_eval(), and SurrBasedLocalMinimizer::compute_trust_region_ratio().

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
	)

protected

compute an augmented Lagrangian function from a set of function values

The Rockafellar augmented Lagrangian function sums the objective function, Lagrange multipler terms for inequality/equality constraints, and quadratic penalty terms for inequality/equality constraints. This implementation follows the convention in Vanderplaats with g<=0 and h=0. The bounds/targets passed in may reflect the original constraints or the relaxed constraints.

References SurrBasedMinimizer::augLagrangeMult, Minimizer::bigRealBoundSize, Minimizer::numNonlinearEqConstraints, Minimizer::numNonlinearIneqConstraints, Minimizer::numUserPrimaryFns, Minimizer::objective(), and SurrBasedMinimizer::penaltyParameter.

Referenced by SurrBasedLocalMinimizer::approx_subprob_objective_eval(), EffGlobalMinimizer::augmented_lagrangian(), and SurrBasedLocalMinimizer::compute_trust_region_ratio().

Real penalty_merit	(	const RealVector &	fn_vals,
		const BoolDeque &	sense,
		const RealVector &	primary_wts
	)

protected

compute a penalty function from a set of function values

The penalty function computation applies a quadratic penalty to any constraint violations and adds this to the objective function(s) p = f + r_p cv.

References SurrBasedMinimizer::constraint_violation(), Minimizer::constraintTol, Minimizer::objective(), and SurrBasedMinimizer::penaltyParameter.

Referenced by SurrBasedLocalMinimizer::compute_trust_region_ratio().

Real constraint_violation	(	const RealVector &	fn_vals,
		const Real &	constraint_tol
	)

protected

compute the constraint violation from a set of function values

Compute the quadratic constraint violation defined as cv = g+^T g+

h+^T h+. This implementation supports equality constraints and 2-sided inequalities. The constraint_tol allows for a small constraint infeasibility (used for penalty methods, but not Lagrangian methods).

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

Referenced by SurrBasedLocalMinimizer::compute_trust_region_ratio(), SurrBasedLocalMinimizer::hard_convergence_check(), SurrBasedMinimizer::initialize_filter(), SurrBasedMinimizer::penalty_merit(), SurrBasedLocalMinimizer::relax_constraints(), EffGlobalMinimizer::update_constraints(), SurrBasedMinimizer::update_filter(), and SurrBasedLocalMinimizer::update_penalty().

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

SurrBasedMinimizer.hpp
SurrBasedMinimizer.cpp

Protected Member Functions

Protected Attributes

Additional Inherited Members

Detailed Description

Member Function Documentation