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SurrBasedLocalMinimizer Class Referenceabstract

Class for provably-convergent local surrogate-based optimization and nonlinear least squares. More...

Inheritance diagram for SurrBasedLocalMinimizer:
SurrBasedMinimizer Minimizer Iterator

Public Member Functions

 SurrBasedLocalMinimizer (ProblemDescDB &problem_db, Model &model, std::shared_ptr< TraitsBase > traits)
 constructor
 
 ~SurrBasedLocalMinimizer ()
 destructor
 

Protected Member Functions

void initialize_graphics (int iterator_server_id=1)
 initialize graphics customized for surrogate-based iteration More...
 
void pre_run ()
 pre-run portion of run (optional); re-implemented by Iterators which can generate all Variables (parameter sets) a priori More...
 
void core_run ()
 Performs local surrogate-based minimization by minimizing local, global, or hierarchical surrogates over a series of trust regions. 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 reset ()
 restore initial state for repeated sub-iterator executions
 
virtual SurrBasedLevelData & trust_region ()=0
 return the active SurrBasedLevelData instance
 
virtual void update_trust_region ()=0
 update the trust region bounds, strictly contained within global bounds
 
virtual void build ()=0
 build the approximation over the current trust region
 
virtual void minimize ()
 solve the approximate subproblem
 
virtual void verify ()=0
 verify the approximate iterate and update the trust region for the next approximate optimization cycle
 
virtual unsigned short converged ()=0
 return the convergence code for the truth level of the trust region hierarchy
 
void initialize_sub_model ()
 construct and initialize approxSubProbModel
 
void initialize_sub_minimizer ()
 construct and initialize approxSubProbMinimizer
 
void initialize_multipliers ()
 initialize lagrangeMult and augLagrangeMult
 
void reset_penalties ()
 reset all penalty parameters to their initial values
 
void reset_multipliers ()
 reset Lagrange multipliers to initial values for cases where they are accumulated instead of computed directly
 
void update_trust_region_data (SurrBasedLevelData &tr_data, const RealVector &parent_l_bnds, const RealVector &parent_u_bnds)
 update the trust region bounds, strictly contained within global bounds
 
void update_approx_sub_problem (SurrBasedLevelData &tr_data)
 update variables and bounds within approxSubProbModel
 
void compute_trust_region_ratio (SurrBasedLevelData &tr_data, bool check_interior=false)
 compute trust region ratio (for SBLM iterate acceptance and trust region resizing) and check for soft convergence (diminishing returns) More...
 
void hard_convergence_check (SurrBasedLevelData &tr_data, const RealVector &lower_bnds, const RealVector &upper_bnds)
 check for hard convergence (norm of projected gradient of merit function < tolerance) More...
 
void print_convergence_code (std::ostream &s, unsigned short code)
 print out the state corresponding to the code returned by converged()
 
void update_penalty (const RealVector &fns_center_truth, const RealVector &fns_star_truth)
 initialize and update the penaltyParameter More...
 
bool find_approx_response (const Variables &search_vars, Response &search_resp)
 locate an approximate response with the data_pairs cache
 
bool find_truth_response (const Variables &search_vars, Response &search_resp)
 locate a truth response with the data_pairs cache
 
bool find_response (const Variables &search_vars, Response &search_resp, const String &search_id, short set_request)
 locate a response with the data_pairs cache
 
void relax_constraints (SurrBasedLevelData &tr_data)
 relax constraints by updating bounds when current iterate is infeasible
 
- Protected Member Functions inherited from SurrBasedMinimizer
 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 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...
 
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...
 

Static Protected Member Functions

static void approx_subprob_objective_eval (const Variables &surrogate_vars, const Variables &recast_vars, const Response &surrogate_response, Response &recast_response)
 static function used to define the approximate subproblem objective. More...
 
static void approx_subprob_constraint_eval (const Variables &surrogate_vars, const Variables &recast_vars, const Response &surrogate_response, Response &recast_response)
 static function used to define the approximate subproblem constraints. More...
 
static void hom_objective_eval (int &mode, int &n, double *tau_and_x, double &f, double *grad_f, int &)
 static function used by NPSOL as the objective function in the homotopy constraint relaxation formulation. More...
 
static void hom_constraint_eval (int &mode, int &ncnln, int &n, int &nrowj, int *needc, double *tau_and_x, double *c, double *cjac, int &nstate)
 static function used by NPSOL as the constraint function in the homotopy constraint relaxation formulation. More...
 

Protected Attributes

Model approxSubProbModel
 the approximate sub-problem formulation solved on each approximate minimization cycle: may be a shallow copy of iteratedModel, or may involve a RecastModel recursion applied to iteratedModel
 
short approxSubProbObj
 type of approximate subproblem objective: ORIGINAL_OBJ, LAGRANGIAN_OBJ, or AUGMENTED_LAGRANGIAN_OBJ
 
short approxSubProbCon
 type of approximate subproblem constraints: NO_CON, LINEARIZED_CON, or ORIGINAL_CON
 
bool recastSubProb
 flag to indicate when approxSubProbModel involves a RecastModel recursion
 
short meritFnType
 type of merit function used in trust region ratio logic: PENALTY_MERIT, ADAPTIVE_PENALTY_MERIT, LAGRANGIAN_MERIT, or AUGMENTED_LAGRANGIAN_MERIT
 
short acceptLogic
 type of iterate acceptance test logic: FILTER or TR_RATIO
 
short trConstraintRelax
 type of trust region constraint relaxation for infeasible starting points: NO_RELAX or HOMOTOPY
 
int minimizeCycles
 counter for number of minimization cycles that have accumulated prior to convergence at the minimizeIndex level (used for ramping penalties)
 
int penaltyIterOffset
 iteration offset used to update the scaling of the penalty parameter for adaptive_penalty merit functions
 
RealVector origTrustRegionFactor
 original user specification for trust region initial_size
 
Real minTrustRegionFactor
 a soft convergence control: stop SBLM when the trust region factor is reduced below the value of minTrustRegionFactor
 
Real trRatioContractValue
 trust region ratio min value: contract tr if ratio below this value
 
Real trRatioExpandValue
 trust region ratio sufficient value: expand tr if ratio above this value
 
Real gammaContract
 trust region contraction factor
 
Real gammaExpand
 trust region expansion factor
 
unsigned short softConvLimit
 convergence control limiting the number of consecutive iterations that fail to achieve sufficient decrease. If exceeded by softConvCount, stop.
 
short truthSetRequest
 derivative order of truth data used within the SBLM process
 
short approxSetRequest
 derivative order of surrogate data used within the SBLM process
 
short correctionType
 flags the use of surrogate correction techniques at the center of each trust region
 
RealVector initialPoint
 starting point prior to sequence of SBLM iterations
 
RealVector globalLowerBnds
 Global lower bounds.
 
RealVector globalUpperBnds
 Global Upper bounds.
 
RealVector nonlinIneqLowerBndsSlack
 individual violations of nonlinear inequality constraint lower bounds
 
RealVector nonlinIneqUpperBndsSlack
 individual violations of nonlinear inequality constraint upper bounds
 
RealVector nonlinEqTargetsSlack
 individual violations of nonlinear equality constraint targets
 
Real tau
 constraint relaxation parameter
 
Real alpha
 constraint relaxation parameter backoff parameter (multiplier)
 
- Protected Attributes inherited from SurrBasedMinimizer
Iterator approxSubProbMinimizer
 the minimizer used on the surrogate model to solve the approximate subproblem on each surrogate-based iteration
 
size_t globalIterCount
 global iteration counter corresponding to number of surrogate-based minimizations
 
RealVector lagrangeMult
 Lagrange multipliers for basic Lagrangian calculations.
 
RealVector augLagrangeMult
 Lagrange multipliers for augmented Lagrangian calculations.
 
Real penaltyParameter
 the penalization factor for violated constraints used in quadratic penalty calculations; increased in update_penalty()
 
RealVector origNonlinIneqLowerBnds
 original nonlinear inequality constraint lower bounds (no relaxation)
 
RealVector origNonlinIneqUpperBnds
 original nonlinear inequality constraint upper bounds (no relaxation)
 
RealVector origNonlinEqTargets
 original nonlinear equality constraint targets (no relaxation)
 
Real eta
 constant used in etaSequence updates
 
Real alphaEta
 power for etaSequence updates when updating penalty
 
Real betaEta
 power for etaSequence updates when updating multipliers
 
Real etaSequence
 decreasing sequence of allowable constraint violation used in augmented Lagrangian updates (refer to Conn, Gould, and Toint, section 14.4)
 
size_t miPLIndex
 index for the active ParallelLevel within ParallelConfiguration::miPLIters
 
- Protected Attributes inherited from Minimizer
size_t numFunctions
 number of response functions
 
size_t numContinuousVars
 number of active continuous vars
 
size_t numDiscreteIntVars
 number of active discrete integer vars
 
size_t numDiscreteStringVars
 number of active discrete string vars
 
size_t numDiscreteRealVars
 number of active discrete real vars
 
Real constraintTol
 optimizer/least squares constraint tolerance
 
Real bigRealBoundSize
 cutoff value for inequality constraint and continuous variable bounds
 
int bigIntBoundSize
 cutoff value for discrete variable bounds
 
size_t numNonlinearIneqConstraints
 number of nonlinear inequality constraints
 
size_t numNonlinearEqConstraints
 number of nonlinear equality constraints
 
size_t numLinearIneqConstraints
 number of linear inequality constraints
 
size_t numLinearEqConstraints
 number of linear equality constraints
 
size_t numNonlinearConstraints
 total number of nonlinear constraints
 
size_t numLinearConstraints
 total number of linear constraints
 
size_t numConstraints
 total number of linear and nonlinear constraints
 
bool optimizationFlag
 flag for use where optimization and NLS must be distinguished
 
size_t numUserPrimaryFns
 number of objective functions or least squares terms in the inbound model; always initialize at Minimizer, even if overridden later
 
size_t numIterPrimaryFns
 number of objective functions or least squares terms in iterator's view, after transformations; always initialize at Minimizer, even if overridden later
 
bool boundConstraintFlag
 convenience flag for denoting the presence of user-specified bound constraints. Used for method selection and error checking.
 
bool speculativeFlag
 flag for speculative gradient evaluations
 
bool calibrationDataFlag
 flag indicating whether user-supplied calibration data is active
 
ExperimentData expData
 Container for experimental data to which to calibrate model using least squares or other formulations which minimize SSE.
 
size_t numExperiments
 number of experiments
 
size_t numTotalCalibTerms
 number of total calibration terms (sum over experiments of number of experimental data per experiment, including field data)
 
Model dataTransformModel
 Shallow copy of the data transformation model, when present (cached in case further wrapped by other transformations)
 
bool scaleFlag
 whether Iterator-level scaling is active
 
Model scalingModel
 Shallow copy of the scaling transformation model, when present (cached in case further wrapped by other transformations)
 
MinimizerprevMinInstance
 pointer containing previous value of minimizerInstance
 
bool vendorNumericalGradFlag
 convenience flag for gradient_type == numerical && method_source == vendor
 
std::shared_ptr< TPLDataTransferdataTransferHandler
 Emerging helper class for handling data transfers to/from Dakota and the underlying TPL.
 
- Protected Attributes inherited from Iterator
ProblemDescDBprobDescDB
 class member reference to the problem description database More...
 
ParallelLibraryparallelLib
 class member reference to the parallel library
 
ParConfigLIter methodPCIter
 the active ParallelConfiguration used by this Iterator instance
 
Model iteratedModel
 the model to be iterated (for iterators and meta-iterators employing a single model instance)
 
size_t myModelLayers
 number of Models locally (in Iterator or derived classes) wrapped around the initially passed in Model
 
unsigned short methodName
 name of the iterator (the user's method spec)
 
Real convergenceTol
 iteration convergence tolerance
 
size_t maxIterations
 maximum number of iterations for the method
 
size_t maxFunctionEvals
 maximum number of fn evaluations for the method
 
int maxEvalConcurrency
 maximum number of concurrent model evaluations More...
 
ActiveSet activeSet
 the response data requirements on each function evaluation
 
size_t numFinalSolutions
 number of solutions to retain in best variables/response arrays
 
VariablesArray bestVariablesArray
 collection of N best solution variables found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)
 
ResponseArray bestResponseArray
 collection of N best solution responses found during the study; always in context of Model originally passed to the Iterator (any in-flight Recasts must be undone)
 
bool subIteratorFlag
 flag indicating if this Iterator is a sub-iterator (NestedModel::subIterator or DataFitSurrModel::daceIterator)
 
short outputLevel
 output verbosity level: {SILENT,QUIET,NORMAL,VERBOSE,DEBUG}_OUTPUT
 
bool summaryOutputFlag
 flag for summary output (evaluation stats, final results); default true, but false for on-the-fly (helper) iterators and sub-iterator use cases
 
ResultsManagerresultsDB
 reference to the global iterator results database
 
EvaluationStore & evaluationsDB
 reference to the global evaluation database
 
EvaluationsDBState evaluationsDBState
 State of evaluations DB for this iterator.
 
ResultsNames resultsNames
 valid names for iterator results
 
std::shared_ptr< TraitsBasemethodTraits
 pointer that retains shared ownership of a TraitsBase object, or child thereof
 
bool topLevel
 Whether this is the top level iterator.
 
bool exportSurrogate = false
 whether to export final surrogates
 
String surrExportPrefix
 base filename for exported surrogates
 
unsigned short surrExportFormat = NO_MODEL_FORMAT
 (bitwise) format(s) to export
 

Static Protected Attributes

static SurrBasedLocalMinimizersblmInstance
 pointer to SBLM instance used in static member functions
 

Additional Inherited Members

- Static Public Member Functions inherited from Minimizer
static Real sum_squared_residuals (size_t num_pri_fns, const RealVector &residuals, const RealVector &weights)
 return weighted sum of squared residuals
 
static void print_residuals (size_t num_terms, const RealVector &best_terms, const RealVector &weights, size_t num_best, size_t best_index, std::ostream &s)
 print num_terms residuals and misfit for final results
 
static void print_model_resp (size_t num_pri_fns, const RealVector &best_fns, size_t num_best, size_t best_index, std::ostream &s)
 print the original user model resp in the case of data transformations
 

Detailed Description

Class for provably-convergent local surrogate-based optimization and nonlinear least squares.

This minimizer uses a SurrogateModel to perform minimization based on local, global, or hierarchical surrogates. It achieves provable convergence through the use of a sequence of trust regions and the application of surrogate corrections at the trust region centers.

Member Function Documentation

void initialize_graphics ( int  iterator_server_id = 1)
protectedvirtual

initialize graphics customized for surrogate-based iteration

Surrogate-based local (data-fit) specializes graphics to output trust region centers. See OutputManager::add_tabular_data in DataFitSurrBasedLocalMinimizer. Other children don't do any output

Reimplemented from Iterator.

References Model::create_2d_plots(), Model::create_tabular_datastream(), OutputManager::graph2DFlag, OutputManager::graphics(), OutputManager::graphics_counter(), Iterator::iteratedModel, Iterator::methodName, ParallelLibrary::output_manager(), Iterator::parallelLib, Graphics::set_x_labels2d(), OutputManager::tabular_counter_label(), OutputManager::tabularDataFlag, and Model::truth_model().

void pre_run ( )
protectedvirtual

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

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

Reimplemented from Iterator.

References Model::continuous_lower_bounds(), Model::continuous_upper_bounds(), Model::continuous_variables(), SurrBasedLocalMinimizer::converged(), Dakota::copy_data(), SurrBasedLocalMinimizer::globalLowerBnds, SurrBasedLocalMinimizer::globalUpperBnds, SurrBasedLocalMinimizer::initialPoint, Iterator::iteratedModel, and SurrBasedLocalMinimizer::reset().

void core_run ( )
protectedvirtual

Performs local surrogate-based minimization by minimizing local, global, or hierarchical surrogates over a series of trust regions.

Trust region-based strategy to perform surrogate-based optimization in subregions (trust regions) of the parameter space. The minimizer operates on approximations in lieu of the more expensive simulation-based response functions. The size of the trust region is adapted according to the agreement between the approximations and the true response functions.

Reimplemented from Iterator.

References SurrBasedLocalMinimizer::build(), SurrBasedLocalMinimizer::converged(), SurrBasedLocalMinimizer::minimize(), SurrBasedLocalMinimizer::sblmInstance, SurrBasedLocalMinimizer::update_trust_region(), and SurrBasedLocalMinimizer::verify().

void post_run ( std::ostream &  s)
protectedvirtual

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

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

Reimplemented from Minimizer.

References SurrBasedLocalMinimizer::converged(), SurrBasedMinimizer::globalIterCount, Minimizer::post_run(), and SurrBasedLocalMinimizer::print_convergence_code().

void compute_trust_region_ratio ( SurrBasedLevelData &  tr_data,
bool  check_interior = false 
)
protected

compute trust region ratio (for SBLM iterate acceptance and trust region resizing) and check for soft convergence (diminishing returns)

Assess acceptance of SBLM iterate (trust region ratio or filter) and compute soft convergence metrics (number of consecutive failures, min trust region size, etc.) to assess whether the convergence rate has decreased to a point where the process should be terminated (diminishing returns).

References SurrBasedLocalMinimizer::acceptLogic, SurrBasedLocalMinimizer::approxSubProbObj, SurrBasedMinimizer::augmented_lagrangian_merit(), SurrBasedMinimizer::constraint_violation(), Minimizer::constraintTol, Iterator::convergenceTol, SurrBasedMinimizer::etaSequence, Response::function_values(), SurrBasedLocalMinimizer::gammaContract, SurrBasedLocalMinimizer::gammaExpand, SurrBasedLocalMinimizer::globalLowerBnds, SurrBasedLocalMinimizer::globalUpperBnds, SurrBasedMinimizer::initialize_filter(), Response::is_null(), Iterator::iteratedModel, SurrBasedMinimizer::lagrangian_merit(), SurrBasedLocalMinimizer::meritFnType, Minimizer::numContinuousVars, Minimizer::numNonlinearConstraints, Minimizer::objective(), SurrBasedMinimizer::origNonlinEqTargets, SurrBasedMinimizer::origNonlinIneqLowerBnds, SurrBasedMinimizer::origNonlinIneqUpperBnds, Iterator::outputLevel, SurrBasedMinimizer::penalty_merit(), Model::primary_response_fn_sense(), Model::primary_response_fn_weights(), Response::reset(), SurrBasedLocalMinimizer::softConvLimit, SurrBasedLocalMinimizer::trRatioContractValue, SurrBasedLocalMinimizer::trRatioExpandValue, SurrBasedMinimizer::update_augmented_lagrange_multipliers(), SurrBasedMinimizer::update_filter(), and SurrBasedLocalMinimizer::update_penalty().

void hard_convergence_check ( SurrBasedLevelData &  tr_data,
const RealVector &  lower_bnds,
const RealVector &  upper_bnds 
)
protected
void update_penalty ( const RealVector &  fns_center_truth,
const RealVector &  fns_star_truth 
)
protected

initialize and update the penaltyParameter

Scaling of the penalty value is important to avoid rejecting SBLM iterates which must increase the objective to achieve a reduction in constraint violation. In the basic penalty case, the penalty is ramped exponentially based on the iteration counter. In the adaptive case, the ratio of relative change between center and star points for the objective and constraint violation values is used to rescale penalty values.

References SurrBasedMinimizer::alphaEta, SurrBasedLocalMinimizer::approxSubProbObj, SurrBasedMinimizer::constraint_violation(), Minimizer::constraintTol, SurrBasedMinimizer::eta, SurrBasedMinimizer::etaSequence, Iterator::iteratedModel, SurrBasedLocalMinimizer::meritFnType, SurrBasedLocalMinimizer::minimizeCycles, Minimizer::objective(), Iterator::outputLevel, SurrBasedLocalMinimizer::penaltyIterOffset, SurrBasedMinimizer::penaltyParameter, Model::primary_response_fn_sense(), and Model::primary_response_fn_weights().

Referenced by SurrBasedLocalMinimizer::compute_trust_region_ratio().

void approx_subprob_objective_eval ( const Variables surrogate_vars,
const Variables recast_vars,
const Response surrogate_response,
Response recast_response 
)
staticprotected
void approx_subprob_constraint_eval ( const Variables surrogate_vars,
const Variables recast_vars,
const Response surrogate_response,
Response recast_response 
)
staticprotected
void hom_objective_eval ( int &  mode,
int &  n,
double *  tau_and_x,
double &  f,
double *  grad_f,
int &   
)
staticprotected

static function used by NPSOL as the objective function in the homotopy constraint relaxation formulation.

NPSOL objective functions evaluator for solution of homotopy constraint relaxation parameter optimization. This constrained optimization problem performs the update of the tau parameter in the homotopy heuristic approach used to relax the constraints in the original problem .

Referenced by SurrBasedLocalMinimizer::relax_constraints().

void hom_constraint_eval ( int &  mode,
int &  ncnln,
int &  n,
int &  nrowj,
int *  needc,
double *  tau_and_x,
double *  c,
double *  cjac,
int &  nstate 
)
staticprotected

static function used by NPSOL as the constraint function in the homotopy constraint relaxation formulation.

NPSOL constraint functions evaluator for solution of homotopy constraint relaxation parameter optimization. This constrained optimization problem performs the update of the tau parameter in the homotopy heuristic approach used to relax the constraints in the original problem.

References Response::active_set(), SurrBasedLocalMinimizer::approxSubProbModel, Model::continuous_variables(), Model::current_response(), Model::evaluate(), Response::function_gradients(), Response::function_values(), SurrBasedLocalMinimizer::nonlinEqTargetsSlack, SurrBasedLocalMinimizer::nonlinIneqLowerBndsSlack, SurrBasedLocalMinimizer::nonlinIneqUpperBndsSlack, Minimizer::numNonlinearEqConstraints, Minimizer::numNonlinearIneqConstraints, ActiveSet::request_vector(), Model::response_size(), SurrBasedLocalMinimizer::sblmInstance, and SurrBasedLocalMinimizer::tau.

Referenced by SurrBasedLocalMinimizer::relax_constraints().


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