Base Stochastic Optimization Problem. More...

#include <base_stochastic.h>

Inheritance diagram for pagmo::problem::base_stochastic:

Public Member Functions
	base_stochastic (int, unsigned int=0u)
	Constructor from global dimension and random seed. More...

	base_stochastic (int, int, int, int, int, const double &, unsigned int)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base_stochastic (int, int, int, int, int, const std::vector< double > &, unsigned int)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance vector. More...

unsigned int	get_seed () const
	Gets the pseudo random generator seed. More...

void	set_seed (unsigned int) const
	Sets the pseudo random generator seed. More...

Public Member Functions inherited from pagmo::problem::base
	base (int, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (int, int, int, int, int, const std::vector< double > &)
	Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (const double &, const double &, int, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from values for lower and upper bounds, global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

	base (const decision_vector &, const decision_vector &, int=0, int=1, int=0, int=0, const double &=0)
	Constructor from upper/lower bounds, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

template<std::size_t N>
	base (const double(&v1)[N], const double(&v2)[N], int ni=0, int nf=1, int nc=0, int nic=0, const double &c_tol=0)
	Constructor from raw arrays, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

template<class Iterator1 , class Iterator2 >
	base (Iterator1 start1, Iterator1 end1, Iterator2 start2, Iterator2 end2, int ni=0, int nf=1, int nc=0, int nic=0, const double &c_tol=0)
	Constructor from iterators, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance. More...

virtual	~base ()
	Trivial destructor. More...

virtual base_ptr	clone () const =0
	Clone method. More...

std::string	human_readable () const
	Return human readable representation of the problem. More...

virtual std::string	human_readable_extra () const
	Extra information in human readable format. More...

bool	operator== (const base &) const
	Equality operator. More...

bool	operator!= (const base &) const
	Inequality operator. More...

bool	is_compatible (const base &) const
	Compatibility operator. More...

bool	compare_x (const decision_vector &, const decision_vector &) const
	Compare decision vectors. More...

bool	verify_x (const decision_vector &) const
	Verify compatibility of decision vector x with problem. More...

bool	compare_fc (const fitness_vector &, const constraint_vector &, const fitness_vector &, const constraint_vector &) const
	Simultaneous fitness-constraint comparison. More...

virtual void	pre_evolution (population &) const
	Pre-evolution hook. More...

virtual void	post_evolution (population &) const
	Post-evolution hook. More...

virtual void	set_sparsity (int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Sets the sparsity pattern of the gradient. More...

const decision_vector &	get_lb () const
	Lower bounds getter. More...

const decision_vector &	get_ub () const
	Upper bounds getter. More...

void	set_bounds (const decision_vector &, const decision_vector &)
	Bounds setter from pagmo::decision_vector. More...

template<class Iterator1 , class Iterator2 >
void	set_bounds (Iterator1 start1, Iterator1 end1, Iterator2 start2, Iterator2 end2)
	Bounds setter from iterators. More...

template<std::size_t N>
void	set_bounds (const double(&v1)[N], const double(&v2)[N])
	Bounds setter from raw arrays. More...

void	set_bounds (const double &, const double &)
	Set bounds to specified values. More...

void	set_bounds (int, const double &, const double &)
	Set bounds to specified values. More...

void	set_lb (const decision_vector &)
	Set lower bounds from pagmo::decision_vector. More...

void	set_lb (int, const double &)
	Set specific lower bound to value. More...

void	set_lb (const double &)
	Set all lower bounds to value. More...

template<class Iterator >
void	set_lb (Iterator start, Iterator end)
	Lower bounds setter from iterators. More...

template<std::size_t N>
void	set_lb (const double(&v)[N])
	Lower bounds setter from raw array. More...

void	set_ub (const decision_vector &)
	Set upper bounds from pagmo::decision_vector. More...

void	set_ub (int, const double &)
	Set specific upper bound to value. More...

void	set_ub (const double &)
	Set all upper bounds to value. More...

template<class Iterator >
void	set_ub (Iterator start, Iterator end)
	Upper bounds setter from iterators. More...

template<std::size_t N>
void	set_ub (const double(&v)[N])
	Upper bounds setter from raw array. More...

unsigned int	get_fevals () const
	Return number of function evaluations. More...

unsigned int	get_cevals () const
	Return number of constraints function evaluations. More...

size_type	get_dimension () const
	Return global dimension. More...

size_type	get_i_dimension () const
	Return integer dimension. More...

f_size_type	get_f_dimension () const
	Return fitness dimension. More...

c_size_type	get_c_dimension () const
	Return global constraints dimension. More...

c_size_type	get_ic_dimension () const
	Return inequality constraints dimension. More...

const std::vector< double > &	get_c_tol () const
	Return constraints tolerance. More...

double	get_diameter () const
	Get the diameter of the problem. More...

virtual std::string	get_name () const
	Get problem's name. More...

constraint_vector	compute_constraints (const decision_vector &) const
	Compute constraints and return constraint vector. More...

void	compute_constraints (constraint_vector &, const decision_vector &) const
	Compute constraints and write them into contraint vector. More...

bool	compare_constraints (const constraint_vector &, const constraint_vector &) const
	Compare constraint vectors. More...

bool	test_constraint (const constraint_vector &, const c_size_type &) const
	Test i-th constraint of c (using tolerance information). More...

bool	feasibility_x (const decision_vector &) const
	Test feasibility of decision vector. More...

bool	feasibility_c (const constraint_vector &) const
	Test feasibility of constraint vector. More...

fitness_vector	objfun (const decision_vector &) const
	Return fitness of pagmo::decision_vector. More...

void	objfun (fitness_vector &, const decision_vector &) const
	Write fitness of pagmo::decision_vector into pagmo::fitness_vector. More...

bool	compare_fitness (const fitness_vector &, const fitness_vector &) const
	Compare fitness vectors. More...

void	reset_caches () const
	Reset internal caches. More...

const std::vector< constraint_vector > &	get_best_c (void) const
	Get the best known constraint vector. More...

const std::vector< decision_vector > &	get_best_x (void) const
	Get the best known decision vector. More...

const std::vector< fitness_vector > &	get_best_f (void) const
	Get the best known fitness vector. More...

void	set_best_x (const std::vector< decision_vector > &)
	Sets the best known decision vectors. More...

Protected Attributes
rng_double	m_drng
	Random number generator for double-precision floating point values.

rng_uint32	m_urng
	Random number generator for unsigned integer values.

unsigned int	m_seed
	Seed of the random number generator.

Friends
class	boost::serialization::access

Additional Inherited Members
Public Types inherited from pagmo::problem::base
typedef decision_vector::size_type	size_type
	Problem's size type: the same as pagmo::decision_vector's size type.

typedef fitness_vector::size_type	f_size_type
	Fitness' size type: the same as pagmo::fitness_vector's size type.

typedef constraint_vector::size_type	c_size_type
	Constraints' size type: the same as pagmo::constraint_vector's size type.

Static Public Attributes inherited from pagmo::problem::base
static const std::size_t	cache_capacity = 5
	Capacity of the internal caches.

Protected Member Functions inherited from pagmo::problem::base
virtual bool	equality_operator_extra (const base &) const
	Extra requirements for equality. More...

virtual bool	compare_fc_impl (const fitness_vector &, const constraint_vector &, const fitness_vector &, const constraint_vector &) const
	Implementation of simultaneous fitness-constraint comparison. More...

void	estimate_sparsity (const decision_vector &, int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Heuristics to estimate the sparsity pattern of the problem. More...

void	estimate_sparsity (int &lenG, std::vector< int > &iGfun, std::vector< int > &jGvar) const
	Heuristics to estimate the sparsity pattern of the problem. More...

virtual void	compute_constraints_impl (constraint_vector &, const decision_vector &) const
	Implementation of constraint computation. More...

virtual bool	compare_constraints_impl (const constraint_vector &, const constraint_vector &) const
	Implementation of constraint vector comparison. More...

virtual bool	compare_fitness_impl (const fitness_vector &, const fitness_vector &) const
	Implementation of fitness vectors comparison. More...

virtual void	objfun_impl (fitness_vector &f, const decision_vector &x) const =0
	Objective function implementation. More...

Detailed Description

Base Stochastic Optimization Problem.

A stochastic optimization problem is a problem that seeks to optimize $J(\mathbf x) = E_s(\mathbf x,s)$ , i.e. the expected value of some stochastic quantity dependant from the doecision vector.

These types of problems, in PaGMO, need to iherit from this base class and reimplement the pagmo::problem::base class virtual method objfun_impl starting with the following lines setting the rngs seed: m_drng.seed(m_seed); m_urng.seed(m_seed);

Look at pagmo::problem::inventory and pagmo::problem::spheres for a typical example.

Optimization techniques (pagmo::algorithm) that want to deal with these types of problems need to take care to change appropriately the seed during the optimization process as to avoid overfitting (that is to avoid solving the problem only for one pseudo random sequence, and not for any). This is done by a call to the change_seed() method.

See pagmo::algorithm::pso_stochastic for a good example of such techniques.

Author: Dario Izzo (dario.nosp@m..izz.nosp@m.o@gma.nosp@m.il.c.nosp@m.om)

Definition at line 56 of file base_stochastic.h.

Constructor & Destructor Documentation

pagmo::problem::base_stochastic::base_stochastic	(	int	dim,
		unsigned int	seed = `0u`
	)

Constructor from global dimension and random seed.

Lower and upper bounds are set to 0 and 1 respectively. The problem built is unconstrained, single objective and with no integer dimension.

Parameters

[in]	dim	global dimension of the problem.
[in]	seed	random number generator seed

Definition at line 39 of file base_stochastic.cpp.

pagmo::problem::base_stochastic::base_stochastic	(	int	n,
		int	ni,
		int	nf,
		int	nc,
		int	nic,
		const double &	c_tol,
		unsigned int	seed
	)

Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance.

Parameters

[in]	n	global dimension of the problem.
[in]	ni	dimension of the combinatorial part of the problem.
[in]	nf	dimension of the fitness vector of the problem.
[in]	nc	global number of constraints.
[in]	nic	number of inequality constraints.
[in]	c_tol	constraints tolerance (equal for all constraints)
[in]	seed	random number generator seed

Definition at line 53 of file base_stochastic.cpp.

pagmo::problem::base_stochastic::base_stochastic	(	int	n,
		int	ni,
		int	nf,
		int	nc,
		int	nic,
		const std::vector< double > &	c_tol,
		unsigned int	seed
	)

Constructor from global dimension, integer dimension, fitness dimension, global constraints dimension, inequality constraints dimension and constraints tolerance vector.

Parameters

[in]	n	global dimension of the problem.
[in]	ni	dimension of the combinatorial part of the problem.
[in]	nf	dimension of the fitness vector of the problem.
[in]	nc	global number of constraints.
[in]	nic	number of inequality constraints.
[in]	c_tol	constraints tolerance std::vector
[in]	seed	random number generator seed

Definition at line 67 of file base_stochastic.cpp.

Member Function Documentation

unsigned int pagmo::problem::base_stochastic::get_seed ( ) const

Gets the pseudo random generator seed.

Returns: random number generator seed

Definition at line 89 of file base_stochastic.cpp.

void pagmo::problem::base_stochastic::set_seed ( unsigned int seed ) const

Sets the pseudo random generator seed.

Parameters

[in] seed random number generator seed

Definition at line 77 of file base_stochastic.cpp.

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

Public Member Functions

Protected Attributes

Friends

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation