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EffGlobalMinimizer Class Reference
Inheritance diagram for EffGlobalMinimizer:
SurrBasedMinimizer Minimizer Iterator

Public Member Functions

 EffGlobalMinimizer (ProblemDescDB &problem_db, Model &model)
 standard constructor
 
 ~EffGlobalMinimizer ()
 alternate constructor for instantiations "on the fly"
 
void core_run ()
 destructor
 
const Modelalgorithm_space_model () const
 
void declare_sources ()
 Declare sources to the evaluations database.
 

Private Member Functions

void minimize_surrogates_on_model ()
 called by minimize_surrogates for setUpType == "model"
 
void get_best_sample ()
 called by minimize_surrogates for setUpType == "user_functions" More...
 
void initialize ()
 initialize
 
Real get_augmented_lagrangian (const RealVector &mean, const RealVector &c_vars, const Real &eif_star)
 augmented Lagrangian augmented Lagrangian
 
void check_convergence (const Real &eif_star, const RealVector &c_vars, RealVector prev_cv_star, unsigned short eif_convergence_cntr, unsigned short dist_convergence_cntr)
 check convergence if EGO has converged
 
void debug_print_values ()
 print mean and variance if debug flag is ON
 
void debug_print_counter (unsigned short globalIterCount, const Real &eif_star, Real distCStar, unsigned short dist_convergence_cntr)
 print counter if debug flag is ON
 
void debug_plots ()
 
Real expected_improvement (const RealVector &means, const RealVector &variances)
 expected improvement function for the GP
 
RealVector expected_violation (const RealVector &means, const RealVector &variances)
 expected violation function for the constraint functions
 
void update_penalty ()
 initialize and update the penaltyParameter
 

Static Private Member Functions

static void EIF_objective_eval (const Variables &sub_model_vars, const Variables &recast_vars, const Response &sub_model_response, Response &recast_response)
 static function used as the objective function in the Expected Improvement (EIF) problem formulation for PMA More...
 

Private Attributes

String setUpType
 controls iteration mode: "model" (normal usage) or "user_functions" (user-supplied functions mode for "on the fly" instantiations).
 
Real distanceTol
 convergence tolerance on distance between predicted best points
 
Real convergenceTol
 convergence tolerances in objectives
 
Model fHatModel
 GP model of response, one approximation per response function.
 
Model eifModel
 recast model which assimilates mean and variance to solve the max(EIF) sub-problem
 
Real meritFnStar
 minimum penalized response from among true function evaluations
 
RealVector truthFnStar
 true function values corresponding to the minimum penalized response
 
RealVector varStar
 point that corresponds to the optimal value meritFnStar
 
short dataOrder
 order of the data used for surrogate construction, in ActiveSet request vector 3-bit format; user may override responses spec
 
Real distCStar
 declare check convergence variables relative distance change in input measured in L2
 
int BatchSizeAcquisition
 declare batch size for BatchSizeAcquisition sampling point located at maximum acquisition function
 
int BatchSizeExploration
 declare batch size for BatchSizeExploration sampling point located at maximum posterior variance
 

Static Private Attributes

static EffGlobalMinimizereffGlobalInstance
 pointer to the active object instance used within the static evaluator functions in order to avoid the need for static data
 

Additional Inherited Members

- Static Public Member Functions inherited from Minimizer
static Real sum_squared_residuals (size_t num_pri_fns, const RealVector &residuals, const RealVector &weights)
 return weighted sum of squared residuals
 
static void print_residuals (size_t num_terms, const RealVector &best_terms, const RealVector &weights, size_t num_best, size_t best_index, std::ostream &s)
 print num_terms residuals and misfit for final results
 
static void print_model_resp (size_t num_pri_fns, const RealVector &best_fns, size_t num_best, size_t best_index, std::ostream &s)
 print the original user model resp in the case of data transformations
 
- 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...
 
- Static Protected Member Functions inherited from Iterator
static void gnewton_set_recast (const Variables &recast_vars, const ActiveSet &recast_set, ActiveSet &sub_model_set)
 conversion of request vector values for the Gauss-Newton Hessian approximation More...
 
- Protected Attributes inherited from SurrBasedMinimizer
Iterator approxSubProbMinimizer
 the minimizer used on the surrogate model to solve the approximate subproblem on each surrogate-based iteration
 
size_t globalIterCount
 global iteration counter corresponding to number of surrogate-based minimizations
 
RealVector lagrangeMult
 Lagrange multipliers for basic Lagrangian calculations.
 
RealVector augLagrangeMult
 Lagrange multipliers for augmented Lagrangian calculations.
 
Real penaltyParameter
 the penalization factor for violated constraints used in quadratic penalty calculations; increased in update_penalty()
 
RealVector origNonlinIneqLowerBnds
 original nonlinear inequality constraint lower bounds (no relaxation)
 
RealVector origNonlinIneqUpperBnds
 original nonlinear inequality constraint upper bounds (no relaxation)
 
RealVector origNonlinEqTargets
 original nonlinear equality constraint targets (no relaxation)
 
Real eta
 constant used in etaSequence updates
 
Real alphaEta
 power for etaSequence updates when updating penalty
 
Real betaEta
 power for etaSequence updates when updating multipliers
 
Real etaSequence
 decreasing sequence of allowable constraint violation used in augmented Lagrangian updates (refer to Conn, Gould, and Toint, section 14.4)
 
size_t miPLIndex
 index for the active ParallelLevel within ParallelConfiguration::miPLIters
 
- Static Protected Attributes inherited from Minimizer
static MinimizerminimizerInstance
 pointer to Minimizer used in static member functions
 

Detailed Description

The EffGlobalMinimizer class provides an implementation of the Efficient Global Optimization algorithm developed by Jones, Schonlau, & Welch as well as adaptation of the concept to nonlinear least squares.

Member Function Documentation

const Model & algorithm_space_model ( ) const
inlinevirtual

default definition that gets redefined in selected derived Minimizers

Reimplemented from Minimizer.

References EffGlobalMinimizer::fHatModel.

void get_best_sample ( )
private
void EIF_objective_eval ( const Variables sub_model_vars,
const Variables recast_vars,
const Response sub_model_response,
Response recast_response 
)
staticprivate

static function used as the objective function in the Expected Improvement (EIF) problem formulation for PMA

To maximize expected improvement, the approxSubProbMinimizer will minimize -(expected_improvement).

References Response::active_set_request_vector(), Model::approximation_variances(), EffGlobalMinimizer::effGlobalInstance, EffGlobalMinimizer::expected_improvement(), EffGlobalMinimizer::fHatModel, Response::function_value(), and Response::function_values().

Referenced by EffGlobalMinimizer::minimize_surrogates_on_model().


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