#include <ROL_RiskNeutralObjective.hpp>

Inheritance diagram for ROL::RiskNeutralObjective< Real >:

Public Member Functions
	RiskNeutralObjective (const Ptr< Objective< Real >> &pObj, const Ptr< SampleGenerator< Real >> &vsampler, const Ptr< SampleGenerator< Real >> &gsampler, const Ptr< SampleGenerator< Real >> &hsampler, const bool storage=true)

	RiskNeutralObjective (const Ptr< Objective< Real >> &pObj, const Ptr< SampleGenerator< Real >> &vsampler, const Ptr< SampleGenerator< Real >> &gsampler, const bool storage=true)

	RiskNeutralObjective (const Ptr< Objective< Real >> &pObj, const Ptr< SampleGenerator< Real >> &sampler, const bool storage=true)

void	update (const Vector< Real > &x, UpdateType type, int iter=-1)
	Update objective function. More...

void	update (const Vector< Real > &x, bool flag=true, int iter=-1)
	Update objective function. More...

Real	value (const Vector< Real > &x, Real &tol)
	Compute value. More...

void	gradient (Vector< Real > &g, const Vector< Real > &x, Real &tol)
	Compute gradient. More...

void	hessVec (Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
	Apply Hessian approximation to vector. More...

void	precond (Vector< Real > &Pv, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
	Apply preconditioner to vector. More...

Public Member Functions inherited from ROL::Objective< Real >
virtual	~Objective ()

	Objective ()

virtual Real	dirDeriv (const Vector< Real > &x, const Vector< Real > &d, Real &tol)
	Compute directional derivative. More...

virtual void	invHessVec (Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
	Apply inverse Hessian approximation to vector. More...

virtual void	prox (Vector< Real > &Pv, const Vector< Real > &v, Real t, Real &tol)

virtual std::vector < std::vector< Real > >	checkGradient (const Vector< Real > &x, const Vector< Real > &d, const bool printToStream=true, std::ostream &outStream=std::cout, const int numSteps=ROL_NUM_CHECKDERIV_STEPS, const int order=1)
	Finite-difference gradient check. More...

virtual std::vector < std::vector< Real > >	checkGradient (const Vector< Real > &x, const Vector< Real > &g, const Vector< Real > &d, const bool printToStream=true, std::ostream &outStream=std::cout, const int numSteps=ROL_NUM_CHECKDERIV_STEPS, const int order=1)
	Finite-difference gradient check. More...

virtual std::vector < std::vector< Real > >	checkGradient (const Vector< Real > &x, const Vector< Real > &d, const std::vector< Real > &steps, const bool printToStream=true, std::ostream &outStream=std::cout, const int order=1)
	Finite-difference gradient check with specified step sizes. More...

virtual std::vector < std::vector< Real > >	checkGradient (const Vector< Real > &x, const Vector< Real > &g, const Vector< Real > &d, const std::vector< Real > &steps, const bool printToStream=true, std::ostream &outStream=std::cout, const int order=1)
	Finite-difference gradient check with specified step sizes. More...

virtual std::vector < std::vector< Real > >	checkHessVec (const Vector< Real > &x, const Vector< Real > &v, const bool printToStream=true, std::ostream &outStream=std::cout, const int numSteps=ROL_NUM_CHECKDERIV_STEPS, const int order=1)
	Finite-difference Hessian-applied-to-vector check. More...

virtual std::vector < std::vector< Real > >	checkHessVec (const Vector< Real > &x, const Vector< Real > &hv, const Vector< Real > &v, const bool printToStream=true, std::ostream &outStream=std::cout, const int numSteps=ROL_NUM_CHECKDERIV_STEPS, const int order=1)
	Finite-difference Hessian-applied-to-vector check. More...

virtual std::vector < std::vector< Real > >	checkHessVec (const Vector< Real > &x, const Vector< Real > &v, const std::vector< Real > &steps, const bool printToStream=true, std::ostream &outStream=std::cout, const int order=1)
	Finite-difference Hessian-applied-to-vector check with specified step sizes. More...

virtual std::vector < std::vector< Real > >	checkHessVec (const Vector< Real > &x, const Vector< Real > &hv, const Vector< Real > &v, const std::vector< Real > &steps, const bool printToStream=true, std::ostream &outStream=std::cout, const int order=1)
	Finite-difference Hessian-applied-to-vector check with specified step sizes. More...

virtual std::vector< Real >	checkHessSym (const Vector< Real > &x, const Vector< Real > &v, const Vector< Real > &w, const bool printToStream=true, std::ostream &outStream=std::cout)
	Hessian symmetry check. More...

virtual std::vector< Real >	checkHessSym (const Vector< Real > &x, const Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &w, const bool printToStream=true, std::ostream &outStream=std::cout)
	Hessian symmetry check. More...

virtual void	setParameter (const std::vector< Real > &param)

Private Member Functions
void	initialize (const Vector< Real > &x)

void	getValue (Real &val, const Vector< Real > &x, const std::vector< Real > &param, Real &tol)

void	getGradient (Vector< Real > &g, const Vector< Real > &x, const std::vector< Real > &param, Real &tol)

void	getHessVec (Vector< Real > &hv, const Vector< Real > &v, const Vector< Real > &x, const std::vector< Real > &param, Real &tol)

Private Attributes
Ptr< Objective< Real > >	ParametrizedObjective_

Ptr< SampleGenerator< Real > >	ValueSampler_

Ptr< SampleGenerator< Real > >	GradientSampler_

Ptr< SampleGenerator< Real > >	HessianSampler_

Real	value_

Ptr< Vector< Real > >	gradient_

Ptr< Vector< Real > >	pointDual_

Ptr< Vector< Real > >	sumDual_

bool	firstUpdate_

bool	storage_

Ptr< ScalarController< Real > >	value_storage_

Ptr< VectorController< Real > >	gradient_storage_

Additional Inherited Members
Protected Member Functions inherited from ROL::Objective< Real >
const std::vector< Real >	getParameter (void) const

Detailed Description

template<class Real>
class ROL::RiskNeutralObjective< Real >

Definition at line 22 of file ROL_RiskNeutralObjective.hpp.

Constructor & Destructor Documentation

template<class Real >

ROL::RiskNeutralObjective< Real >::RiskNeutralObjective	(	const Ptr< Objective< Real >> &	pObj,
		const Ptr< SampleGenerator< Real >> &	vsampler,
		const Ptr< SampleGenerator< Real >> &	gsampler,
		const Ptr< SampleGenerator< Real >> &	hsampler,
		const bool	storage = `true`
	)

inline

Definition at line 89 of file ROL_RiskNeutralObjective.hpp.

References ROL::RiskNeutralObjective< Real >::gradient_storage_, and ROL::RiskNeutralObjective< Real >::value_storage_.

template<class Real >

ROL::RiskNeutralObjective< Real >::RiskNeutralObjective	(	const Ptr< Objective< Real >> &	pObj,
		const Ptr< SampleGenerator< Real >> &	vsampler,
		const Ptr< SampleGenerator< Real >> &	gsampler,
		const bool	storage = `true`
	)

inline

Definition at line 101 of file ROL_RiskNeutralObjective.hpp.

References ROL::RiskNeutralObjective< Real >::gradient_storage_, and ROL::RiskNeutralObjective< Real >::value_storage_.

template<class Real >

ROL::RiskNeutralObjective< Real >::RiskNeutralObjective	(	const Ptr< Objective< Real >> &	pObj,
		const Ptr< SampleGenerator< Real >> &	sampler,
		const bool	storage = `true`
	)

inline

Definition at line 112 of file ROL_RiskNeutralObjective.hpp.

References ROL::RiskNeutralObjective< Real >::gradient_storage_, and ROL::RiskNeutralObjective< Real >::value_storage_.

Member Function Documentation

template<class Real >

void ROL::RiskNeutralObjective< Real >::initialize ( const Vector< Real > & x )

inlineprivate

Definition at line 42 of file ROL_RiskNeutralObjective.hpp.

References ROL::Vector< Real >::dual(), ROL::RiskNeutralObjective< Real >::firstUpdate_, ROL::RiskNeutralObjective< Real >::gradient_, ROL::RiskNeutralObjective< Real >::pointDual_, and ROL::RiskNeutralObjective< Real >::sumDual_.

Referenced by ROL::RiskNeutralObjective< Real >::gradient(), ROL::RiskNeutralObjective< Real >::hessVec(), ROL::RiskNeutralObjective< Real >::update(), and ROL::RiskNeutralObjective< Real >::value().

template<class Real >

void ROL::RiskNeutralObjective< Real >::getValue	(	Real &	val,
		const Vector< Real > &	x,
		const std::vector< Real > &	param,
		Real &	tol
	)

inlineprivate

Definition at line 51 of file ROL_RiskNeutralObjective.hpp.

References ROL::RiskNeutralObjective< Real >::ParametrizedObjective_, ROL::RiskNeutralObjective< Real >::storage_, and ROL::RiskNeutralObjective< Real >::value_storage_.

Referenced by ROL::RiskNeutralObjective< Real >::value().

template<class Real >

void ROL::RiskNeutralObjective< Real >::getGradient	(	Vector< Real > &	g,
		const Vector< Real > &	x,
		const std::vector< Real > &	param,
		Real &	tol
	)

inlineprivate

Definition at line 66 of file ROL_RiskNeutralObjective.hpp.

References ROL::RiskNeutralObjective< Real >::gradient_storage_, ROL::RiskNeutralObjective< Real >::ParametrizedObjective_, and ROL::RiskNeutralObjective< Real >::storage_.

Referenced by ROL::RiskNeutralObjective< Real >::gradient().

template<class Real >

void ROL::RiskNeutralObjective< Real >::getHessVec	(	Vector< Real > &	hv,
		const Vector< Real > &	v,
		const Vector< Real > &	x,
		const std::vector< Real > &	param,
		Real &	tol
	)

inlineprivate

Definition at line 81 of file ROL_RiskNeutralObjective.hpp.

References ROL::RiskNeutralObjective< Real >::ParametrizedObjective_.

Referenced by ROL::RiskNeutralObjective< Real >::hessVec().

template<class Real >

void ROL::RiskNeutralObjective< Real >::update	(	const Vector< Real > &	x,
		UpdateType	type,
		int	iter = `-1`
	)

inlinevirtual

Update objective function.

This function updates the objective function at new iterations.

Parameters

[in]	x	is the new iterate.
[in]	type	is the type of update requested.
[in]	iter	is the outer algorithm iterations count.

Reimplemented from ROL::Objective< Real >.

Definition at line 122 of file ROL_RiskNeutralObjective.hpp.

template<class Real >

void ROL::RiskNeutralObjective< Real >::update	(	const Vector< Real > &	x,
		bool	flag = `true`,
		int	iter = `-1`
	)

inlinevirtual

Update objective function.

This function updates the objective function at new iterations.

Parameters

[in]	x	is the new iterate.
[in]	flag	is true if the iterate has changed.
[in]	iter	is the outer algorithm iterations count.

Reimplemented from ROL::Objective< Real >.

Definition at line 139 of file ROL_RiskNeutralObjective.hpp.

template<class Real >

Real ROL::RiskNeutralObjective< Real >::value	(	const Vector< Real > &	x,
		Real &	tol
	)

inlinevirtual

Compute value.

This function returns the objective function value.

Parameters

[in]	x	is the current iterate.
[in]	tol	is a tolerance for inexact objective function computation.

Implements ROL::Objective< Real >.

Definition at line 158 of file ROL_RiskNeutralObjective.hpp.

References ROL::RiskNeutralObjective< Real >::getValue(), ROL::RiskNeutralObjective< Real >::initialize(), ROL::RiskNeutralObjective< Real >::value_, and ROL::RiskNeutralObjective< Real >::ValueSampler_.

template<class Real >

void ROL::RiskNeutralObjective< Real >::gradient	(	Vector< Real > &	g,
		const Vector< Real > &	x,
		Real &	tol
	)

inlinevirtual

Compute gradient.

This function returns the objective function gradient.

Parameters

[out]	g	is the gradient.
[in]	x	is the current iterate.
[in]	tol	is a tolerance for inexact objective function computation.

The default implementation is a finite-difference approximation based on the function value. This requires the definition of a basis \(\{\phi_i\}\) for the optimization vectors x and the definition of a basis \(\{\psi_j\}\) for the dual optimization vectors (gradient vectors g). The bases must be related through the Riesz map, i.e., \( R \{\phi_i\} = \{\psi_j\}\), and this must be reflected in the implementation of the ROL::Vector::dual() method.

Reimplemented from ROL::Objective< Real >.

Definition at line 179 of file ROL_RiskNeutralObjective.hpp.

References ROL::RiskNeutralObjective< Real >::getGradient(), ROL::RiskNeutralObjective< Real >::gradient_, ROL::RiskNeutralObjective< Real >::GradientSampler_, ROL::RiskNeutralObjective< Real >::initialize(), ROL::RiskNeutralObjective< Real >::pointDual_, ROL::Vector< Real >::set(), ROL::RiskNeutralObjective< Real >::sumDual_, and ROL::Vector< Real >::zero().

template<class Real >

void ROL::RiskNeutralObjective< Real >::hessVec	(	Vector< Real > &	hv,
		const Vector< Real > &	v,
		const Vector< Real > &	x,
		Real &	tol
	)

inlinevirtual

Apply Hessian approximation to vector.

This function applies the Hessian of the objective function to the vector \(v\).

Parameters

[out]	hv	is the the action of the Hessian on \(v\).
[in]	v	is the direction vector.
[in]	x	is the current iterate.
[in]	tol	is a tolerance for inexact objective function computation.

Reimplemented from ROL::Objective< Real >.

Definition at line 205 of file ROL_RiskNeutralObjective.hpp.

References ROL::RiskNeutralObjective< Real >::getHessVec(), ROL::RiskNeutralObjective< Real >::HessianSampler_, ROL::RiskNeutralObjective< Real >::initialize(), ROL::RiskNeutralObjective< Real >::pointDual_, ROL::RiskNeutralObjective< Real >::sumDual_, and ROL::Vector< Real >::zero().

template<class Real >

void ROL::RiskNeutralObjective< Real >::precond	(	Vector< Real > &	Pv,
		const Vector< Real > &	v,
		const Vector< Real > &	x,
		Real &	tol
	)

inlinevirtual

Apply preconditioner to vector.

This function applies a preconditioner for the Hessian of the objective function to the vector \(v\).

Parameters

[out]	Pv	is the action of the Hessian preconditioner on \(v\).
[in]	v	is the direction vector.
[in]	x	is the current iterate.
[in]	tol	is a tolerance for inexact objective function computation.