ConcurrentStrategy Class Reference

Strategy for multi-start iteration or pareto set optimization. More...

Inheritance diagram for ConcurrentStrategy:

List of all members.

Public Member Functions
	ConcurrentStrategy (ProblemDescDB &problem_db)
	constructor
	~ConcurrentStrategy ()
	destructor
Protected Member Functions
void	run_strategy ()
	settings within the iterator or model.
void	initialize_iterator (int job_index)
	scheduling function (serve_iterators() or static_schedule_iterators())
void	pack_parameters_buffer (MPIPackBuffer &send_buffer, int job_index)
	pack a send_buffer for assigning an iterator job to a server
void	unpack_parameters_buffer (MPIUnpackBuffer &recv_buffer)
	unpack a recv_buffer for accepting an iterator job from the scheduler
void	pack_results_buffer (MPIPackBuffer &send_buffer, int job_index)
	pack a send_buffer for returning iterator results from a server
void	unpack_results_buffer (MPIUnpackBuffer &recv_buffer, int job_index)
	unpack a recv_buffer for accepting iterator results from a server
void	update_local_results (int job_index)
	update local prpResults with current iteration results
Private Member Functions
void	initialize_iterator (const RealVector &param_set)
	initialize_iterator(int) to update userDefinedModel and selectedIterator
void	print_results () const
	prints the concurrent iteration results summary (called by run_strategy())
Private Attributes
Model	userDefinedModel
	the model used by the iterator
Iterator	selectedIterator
	the iterator used by the concurrent strategy
bool	multiStartFlag
	distinguishes multi-start from Pareto-set
RealVector	initialPt
	point in the Pareto set strategy
RealVectorArray	parameterSets
	be performed.

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Detailed Description

Strategy for multi-start iteration or pareto set optimization.

This strategy maintains two concurrent iterator capabilities. First, a general capability for running an iterator multiple times from different starting points is provided (often used for multi-start optimization, but not restricted to optimization). Second, a simple capability for mapping the "pareto frontier" (the set of optimal solutions in multiobjective formulations) is provided. This pareto set is mapped through running an optimizer multiple times for different sets of multiobjective weightings.

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Member Function Documentation

void pack_parameters_buffer	(	MPIPackBuffer &	send_buffer,
		int	job_index
	)			[inline, protected, virtual]

pack a send_buffer for assigning an iterator job to a server

This virtual function redefinition is executed on the dedicated master processor for self scheduling. It is not used for peer partitions.

Reimplemented from Strategy.

void unpack_parameters_buffer

(

MPIUnpackBuffer &

recv_buffer

)

[inline, protected, virtual]

unpack a recv_buffer for accepting an iterator job from the scheduler

This virtual function redefinition is executed on an iterator server for dedicated master self scheduling. It is not used for peer partitions.

Reimplemented from Strategy.

void pack_results_buffer	(	MPIPackBuffer &	send_buffer,
		int	job_index
	)			[inline, protected, virtual]

pack a send_buffer for returning iterator results from a server

This virtual function redefinition is executed either on an iterator server for dedicated master self scheduling or on peers 2 through n for static scheduling.

Reimplemented from Strategy.

void unpack_results_buffer	(	MPIUnpackBuffer &	recv_buffer,
		int	job_index
	)			[inline, protected, virtual]

unpack a recv_buffer for accepting iterator results from a server

This virtual function redefinition is executed on an strategy master (either the dedicated master processor for self scheduling or peer 1 for static scheduling).

Reimplemented from Strategy.

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

• ConcurrentStrategy.H
• ConcurrentStrategy.C

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