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Dakota  Version 6.2
Classes | Public Member Functions | Private Member Functions | Private Attributes
NomadOptimizer Class Reference

Wrapper class for NOMAD Optimizer. More...

Inheritance diagram for NomadOptimizer:
Optimizer Minimizer Iterator

List of all members.

Classes

class  Evaluator
 NOMAD-based Evaluator class. More...

Public Member Functions

 NomadOptimizer (ProblemDescDB &problem_db, Model &model)
 Constructor.
 NomadOptimizer (Model &model)
 alternate constructor for Iterator instantiations without DB
 ~NomadOptimizer ()
 Destructor.
void find_optimum ()
 Calls the NOMAD solver.

Private Member Functions

void load_parameters (Model &model, NOMAD::Parameters &p)
 Convenience function for Parameter loading.

Private Attributes

int numTotalVars
 Total across all types of variables.
int numNomadNonlinearIneqConstraints
 Number of nonlinear inequality constraints after put into the format required by NOMAD.
int randomSeed
 Algorithm control parameters passed to NOMAD.
int maxBlackBoxEvals
int maxIterations
 maximum number of iterations for the iterator
Real epsilon
Real vns
std::string outputFormat
 Output control parameters passed to NOMAD.
std::string historyFile
bool displayAll
int numHops
 Parameters needed for categorical neighbor construction.
BitArray discreteSetIntCat
BitArray discreteSetRealCat
RealMatrixArray discreteSetIntAdj
RealMatrixArray discreteSetRealAdj
RealMatrixArray discreteSetStrAdj
RealMatrixArray categoricalAdjacency
NOMAD::Point initialPoint
 Pointer to Nomad initial point.
NOMAD::Point upperBound
 Pointer to Nomad upper bounds.
NOMAD::Point lowerBound
 Pointer to Nomad lower bounds.
std::vector< int > constraintMapIndices
 map from Dakota constraint number to Nomad constraint number
std::vector< double > constraintMapMultipliers
 multipliers for constraint transformations
std::vector< double > constraintMapOffsets
 offsets for constraint transformations

Detailed Description

Wrapper class for NOMAD Optimizer.

NOMAD (is a Nonlinear Optimization by Mesh Adaptive Direct search) is a simulation-based optimization package designed to efficiently explore a design space using Mesh Adaptive Search.

Mesh Adaptive Direct Search uses Meshes, discretizations of the domain space of variables. It generates multiple meshes, and as its name implies, it also adapts the refinement of the meshes in order to find the best solution of a problem.

The objective of each iteration is to find points in a mesh that improves the current solution. If a better solution is not found, the next iteration is done over a finer mesh.

Each iteration is composed of two steps: Search and Poll. The Search step finds any point in the mesh in an attempt to find an improvement; while the Poll step generates trial mesh points surrounding the current best current solution.

The NomadOptimizer is a wrapper for the NOMAD library. It features the following attributes: max_function_evaluations, display_format, display_all_evaluations, function_precision, max_iterations.


Constructor & Destructor Documentation

NomadOptimizer ( ProblemDescDB problem_db,
Model model 
)

Member Function Documentation

void load_parameters ( Model model,
NOMAD::Parameters &  p 
) [private]

Convenience function for Parameter loading.

This function takes the Parameters provided by the user in the DAKOTA model.

Parameters:
modelNOMAD Model object Variables for the stuff that must go in the parameters. Will be filled by calling load_parameters after the constructor to capture model recasts.

References Dakota::_NPOS, Dakota::abort_handler(), Minimizer::bigIntBoundSize, Minimizer::bigRealBoundSize, NomadOptimizer::constraintMapIndices, NomadOptimizer::constraintMapMultipliers, NomadOptimizer::constraintMapOffsets, Model::continuous_lower_bounds(), Model::continuous_upper_bounds(), Model::continuous_variables(), Model::discrete_int_lower_bounds(), Model::discrete_int_sets(), Model::discrete_int_upper_bounds(), Model::discrete_int_variables(), Model::discrete_real_lower_bounds(), Model::discrete_real_upper_bounds(), Model::discrete_real_variables(), Model::discrete_set_int_values(), Model::discrete_set_real_values(), Model::discrete_set_string_values(), Model::discrete_string_variables(), NomadOptimizer::initialPoint, Iterator::iteratedModel, NomadOptimizer::lowerBound, Model::nonlinear_eq_constraint_targets(), Model::nonlinear_ineq_constraint_lower_bounds(), Model::nonlinear_ineq_constraint_upper_bounds(), Minimizer::numContinuousVars, Minimizer::numDiscreteIntVars, Minimizer::numDiscreteRealVars, Minimizer::numDiscreteStringVars, NomadOptimizer::numNomadNonlinearIneqConstraints, Minimizer::numNonlinearEqConstraints, Minimizer::numNonlinearIneqConstraints, NomadOptimizer::numTotalVars, Dakota::set_value_to_index(), and NomadOptimizer::upperBound.

Referenced by NomadOptimizer::find_optimum().


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