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SNLLOptimizer Class Reference

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

Inheritance diagram for SNLLOptimizer:
Optimizer SNLLBase Minimizer Iterator

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(*user_obj_eval)(int mode, int n, const RealVector &x, double &f, RealVector &grad_f, int &result_mode), void(*user_con_eval)(int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode))
 alternate constructor for instantiations "on the fly" More...
 
 ~SNLLOptimizer ()
 destructor
 
void core_run ()
 Performs the iterations to determine the optimal solution.
 
- 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 ()
 default constructor
 
 Optimizer (ProblemDescDB &problem_db, Model &model)
 alternate constructor; accepts a model
 
 Optimizer (unsigned short method_name, Model &model)
 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)
 alternate constructor for "on the fly" instantiations
 
 ~Optimizer ()
 destructor
 
void print_results (std::ostream &s)
 
- Protected Member Functions inherited from Minimizer
 Minimizer ()
 default constructor
 
 Minimizer (ProblemDescDB &problem_db, Model &model)
 standard constructor More...
 
 Minimizer (unsigned short method_name, Model &model)
 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)
 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 Modelalgorithm_space_model () const
 
Model original_model (unsigned short recasts_left=0)
 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_allocate_best (size_t num_points)
 allocate results arrays and labels for multipoint storage
 
void archive_best (size_t index, const Variables &best_vars, const Response &best_resp)
 archive the best point into the results array
 
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...
 
Real sum_squared_residuals (size_t num_pri_fns, const RealVector &residuals, const RealVector &weights)
 return weighted sum of squared residuals
 
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
 
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
 
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)
 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)
 alternate constructor for base iterator classes constructed on the fly More...
 
 Iterator (NoDBBaseConstructor, unsigned short method_name)
 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
 
- 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, int max_iter, int max_fn_evals, 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 setting OPT++ options after the method instantiations
 

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), void(*con_eval)(int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode))
 instantiate an OPTPP_Q_NEWTON solver 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

SNLLOptimizerprevSnllOptInstance
 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
 holds initial point passed in for "user_functions" mode.
 
RealVector lowerBounds
 holds variable lower bounds passed in for "user_functions" mode.
 
RealVector upperBounds
 holds variable upper bounds passed in for "user_functions" mode.
 

Static Private Attributes

static SNLLOptimizersnllOptInstance
 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 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
 
OptimizerprevOptInstance
 pointer containing previous value of optimizerInstance
 
- 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 OptimizeroptimizerInstance
 pointer to Optimizer instance used in static member functions
 
- Static Protected Attributes inherited from SNLLBase
static MinimizeroptLSqInstance
 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 ( 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_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 ( const String &  method_string,
Model model 
)
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)  user_obj_eval,
void(*)(int mode, int n, const RealVector &x, RealVector &g, RealMatrix &grad_g, int &result_mode)  user_con_eval 
)

Member Function Documentation

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().

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().

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().

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().

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().

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

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