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LeastSq Class Reference

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

Inheritance diagram for LeastSq:
Minimizer Iterator NL2SOLLeastSq NLSSOLLeastSq SNLLLeastSq

Protected Member Functions

 LeastSq (std::shared_ptr< TraitsBase > traits)
 default constructor
 
 LeastSq (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits)
 standard constructor More...
 
 LeastSq (unsigned short method_name, Model &model, std::shared_ptr< TraitsBase > traits)
 alternate "on the fly" constructor
 
 ~LeastSq ()
 destructor
 
void initialize_run ()
 
void post_run (std::ostream &s)
 
void finalize_run ()
 utility function to perform common operations following post_run(); deallocation and resetting of instance pointers More...
 
void print_results (std::ostream &s, short results_state=FINAL_RESULTS)
 
void get_confidence_intervals (const Variables &native_vars, const Response &iter_resp)
 Calculate confidence intervals on estimated parameters. 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 Modelalgorithm_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...
 
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 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 Attributes

size_t numLeastSqTerms
 number of least squares terms
 
LeastSqprevLSqInstance
 pointer containing previous value of leastSqInstance
 
bool weightFlag
 flag indicating whether weighted least squares is active
 
RealVector confBoundsLower
 lower bounds for confidence intervals on calibration parameters
 
RealVector confBoundsUpper
 upper bounds for confidence intervals on calibration parameters
 
RealVector bestIterPriFns
 storage for iterator best primary functions (which shouldn't be stored in bestResponseArray when there are transformations)
 
bool retrievedIterPriFns
 whether final primary iterator space functions have been retrieved (possibly by a derived class)
 
- 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

static LeastSqleastSqInstance
 pointer to LeastSq instance used in static member functions
 
- Static Protected Attributes inherited from Minimizer
static MinimizerminimizerInstance
 pointer to Minimizer used in static member functions
 

Private Member Functions

void weight_model ()
 Wrap iteratedModel in a RecastModel that weights the residuals. More...
 
void archive_best_results ()
 top-level archival method
 

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< TPLDataTransferget_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...
 

Detailed Description

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

The LeastSq class provides common data and functionality for least squares solvers (including NL2OL, NLSSOLLeastSq, and SNLLLeastSq.

Constructor & Destructor Documentation

LeastSq ( ProblemDescDB problem_db,
Model model,
std::shared_ptr< TraitsBase traits 
)
protected

Member Function Documentation

void initialize_run ( )
protectedvirtual

This function should be invoked (or reimplemented) by any derived implementations of initialize_run() (which would otherwise hide it).

Reimplemented from Iterator.

Reimplemented in SNLLLeastSq.

References LeastSq::bestIterPriFns, Minimizer::initialize_run(), Iterator::iteratedModel, LeastSq::leastSqInstance, Iterator::myModelLayers, LeastSq::prevLSqInstance, LeastSq::retrievedIterPriFns, and Model::update_from_subordinate_model().

Referenced by SNLLLeastSq::initialize_run().

void post_run ( std::ostream &  s)
protectedvirtual
void finalize_run ( )
inlineprotectedvirtual

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

Optional: perform finalization phases of run sequence, like deallocating memory and resetting instance pointers. Commonly used in sub-iterator executions. This is a virtual function; when re-implementing, a derived class must call its nearest parent's finalize_run(), typically after performing its own implementation steps.

Reimplemented from Iterator.

Reimplemented in SNLLLeastSq.

References Minimizer::finalize_run(), LeastSq::leastSqInstance, and LeastSq::prevLSqInstance.

Referenced by SNLLLeastSq::finalize_run().

void print_results ( std::ostream &  s,
short  results_state = FINAL_RESULTS 
)
protectedvirtual
void get_confidence_intervals ( const Variables native_vars,
const Response iter_resp 
)
protected

Calculate confidence intervals on estimated parameters.

Calculate individual confidence intervals for each parameter, based on a linear approximation of the nonlinear model. native_cv are needed for transformations and final reporting. iter_resp must contain the final differenced, scaled, weighted residuals and gradients.

References LeastSq::confBoundsLower, LeastSq::confBoundsUpper, Variables::continuous_variables(), Response::copy(), Response::function_gradients_view(), Response::function_values(), Model::model_rep(), Minimizer::numContinuousVars, LeastSq::numLeastSqTerms, ScalingModel::response_modify_s2n(), Minimizer::scaleFlag, Minimizer::scalingModel, and Minimizer::vendorNumericalGradFlag.

Referenced by LeastSq::post_run().

void weight_model ( )
private

Wrap iteratedModel in a RecastModel that weights the residuals.

Setup Recast for weighting model. The weighting transformation doesn't resize, and makes no vars, active set or secondary mapping. All indices are one-to-one mapped (no change in counts).

References Dakota::abort_handler(), Model::assign_rep(), Iterator::iteratedModel, Iterator::myModelLayers, Iterator::outputLevel, and Model::primary_response_fn_weights().

Referenced by LeastSq::LeastSq().


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