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Dakota  Version 6.2
Public Member Functions | Protected Member Functions | Private Attributes
CollabHybridMetaIterator Class Reference

Meta-iterator for hybrid iteration using multiple collaborating optimization and nonlinear least squares methods. More...

Inheritance diagram for CollabHybridMetaIterator:
MetaIterator Iterator

List of all members.

Public Member Functions

 CollabHybridMetaIterator (ProblemDescDB &problem_db)
 standard constructor
 CollabHybridMetaIterator (ProblemDescDB &problem_db, Model &model)
 alternate constructor
 ~CollabHybridMetaIterator ()
 destructor

Protected Member Functions

void core_run ()
 Performs the collaborative hybrid iteration.
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
IntIntPair estimate_partition_bounds ()
 estimate the minimum and maximum partition sizes that can be utilized by this Iterator
const Variablesvariables_results () const
 return the final solution from the collaborative iteration (variables)
const Responseresponse_results () const
 return the final solution from the collaborative iteration (response)

Private Attributes

String hybridCollabType
 abo or hops
StringArray methodList
 the list of method name identifiers
bool lightwtCtor
 use of lightweight Iterator construction by name
IteratorArray selectedIterators
 the set of iterators, one for each entry in methodList
ModelArray selectedModels
 the set of models, one for each iterator
Variables bestVariables
 best variables found in collaborative iteration
Response bestResponse
 best response found in collaborative iteration

Detailed Description

Meta-iterator for hybrid iteration using multiple collaborating optimization and nonlinear least squares methods.

This meta-iterator has two approaches to hybrid iteration: (1) agent-based using the ABO framework; (2) nonagent-based using the HOPSPACK framework.


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