ROL
Public Member Functions | Private Member Functions | Private Attributes | List of all members
ROL::SmoothedPOE< Real > Class Template Reference

Provides the implementation of the smoothed probability of exceedance. More...

#include <ROL_SmoothedPOE.hpp>

+ Inheritance diagram for ROL::SmoothedPOE< Real >:

Public Member Functions

 SmoothedPOE (const Real threshold, const Real eps)
 
 SmoothedPOE (ROL::ParameterList &parlist)
 
void updateValue (Objective< Real > &obj, const Vector< Real > &x, const std::vector< Real > &xstat, Real &tol)
 Update internal storage for value computation. More...
 
Real getValue (const Vector< Real > &x, const std::vector< Real > &xstat, SampleGenerator< Real > &sampler)
 Return risk measure value. More...
 
void updateGradient (Objective< Real > &obj, const Vector< Real > &x, const std::vector< Real > &xstat, Real &tol)
 Update internal risk measure storage for gradient computation. More...
 
void getGradient (Vector< Real > &g, std::vector< Real > &gstat, const Vector< Real > &x, const std::vector< Real > &xstat, SampleGenerator< Real > &sampler)
 Return risk measure (sub)gradient. More...
 
void updateHessVec (Objective< Real > &obj, const Vector< Real > &v, const std::vector< Real > &vstat, const Vector< Real > &x, const std::vector< Real > &xstat, Real &tol)
 Update internal risk measure storage for Hessian-time-a-vector computation. More...
 
void getHessVec (Vector< Real > &hv, std::vector< Real > &hvstat, const Vector< Real > &v, const std::vector< Real > &vstat, const Vector< Real > &x, const std::vector< Real > &xstat, SampleGenerator< Real > &sampler)
 Return risk measure Hessian-times-a-vector. More...
 
- Public Member Functions inherited from ROL::RandVarFunctional< Real >
virtual ~RandVarFunctional ()
 
 RandVarFunctional (void)
 
 weight_ (0)
 
void useStorage (bool storage)
 
void useHessVecStorage (bool storage)
 
virtual void setStorage (const Ptr< ScalarController< Real >> &value_storage, const Ptr< VectorController< Real >> &gradient_storage)
 
virtual void setHessVecStorage (const Ptr< ScalarController< Real >> &gradvec_storage, const Ptr< VectorController< Real >> &hessvec_storage)
 
virtual void resetStorage (bool flag=true)
 Reset internal storage. More...
 
virtual void resetStorage (UpdateType type)
 
virtual void initialize (const Vector< Real > &x)
 Initialize temporary variables. More...
 
virtual void setSample (const std::vector< Real > &point, const Real weight)
 
virtual Real computeStatistic (const Ptr< const std::vector< Real >> &xstat) const
 Compute statistic. More...
 

Private Member Functions

Real smoothHeaviside (const Real x, const int deriv=0) const
 

Private Attributes

Real threshold_
 
Real eps_
 

Additional Inherited Members

- Protected Member Functions inherited from ROL::RandVarFunctional< Real >
Real computeValue (Objective< Real > &obj, const Vector< Real > &x, Real &tol)
 
void computeGradient (Vector< Real > &g, Objective< Real > &obj, const Vector< Real > &x, Real &tol)
 
Real computeGradVec (Vector< Real > &g, Objective< Real > &obj, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
 
void computeHessVec (Vector< Real > &hv, Objective< Real > &obj, const Vector< Real > &v, const Vector< Real > &x, Real &tol)
 
- Protected Attributes inherited from ROL::RandVarFunctional< Real >
Real val_
 
Real gv_
 
Ptr< Vector< Real > > g_
 
Ptr< Vector< Real > > hv_
 
Ptr< Vector< Real > > dualVector_
 
bool firstReset_
 
std::vector< Real > point_
 
Real weight_
 

Detailed Description

template<class Real>
class ROL::SmoothedPOE< Real >

Provides the implementation of the smoothed probability of exceedance.

Let \((\Omega,\mathcal{F},\mathbb{P})\) be a complete space. Here, \(\Omega\) is the set of outcomes, \(\mathcal{F}\subseteq 2^\Omega\) is a \(\sigma\)-algebra of events and \(\mathbb{P}:\mathcal{F}\to[0,1]\) is a probability measure. Moreover, let \(\mathcal{X}\) be a class of random variables.

ROL's SmoothedPOE class inherits from ROL::RandVarFunctional which is written in a way to exploit parallel sampling.

Definition at line 32 of file ROL_SmoothedPOE.hpp.

Constructor & Destructor Documentation

template<class Real >
ROL::SmoothedPOE< Real >::SmoothedPOE ( const Real  threshold,
const Real  eps 
)
inline

Definition at line 70 of file ROL_SmoothedPOE.hpp.

template<class Real >
ROL::SmoothedPOE< Real >::SmoothedPOE ( ROL::ParameterList &  parlist)
inline

Member Function Documentation

template<class Real >
Real ROL::SmoothedPOE< Real >::smoothHeaviside ( const Real  x,
const int  deriv = 0 
) const
inlineprivate
template<class Real >
void ROL::SmoothedPOE< Real >::updateValue ( Objective< Real > &  obj,
const Vector< Real > &  x,
const std::vector< Real > &  xstat,
Real &  tol 
)
inlinevirtual

Update internal storage for value computation.

Parameters
[in]valis the value of the random variable objective function at the current sample point
[in]weightis the weight associated with the current sample point

Reimplemented from ROL::RandVarFunctional< Real >.

Definition at line 81 of file ROL_SmoothedPOE.hpp.

References ROL::RandVarFunctional< Real >::computeValue(), ROL::SmoothedPOE< Real >::smoothHeaviside(), ROL::SmoothedPOE< Real >::threshold_, ROL::RandVarFunctional< Real >::val_, and ROL::RandVarFunctional< Real >::weight_.

template<class Real >
Real ROL::SmoothedPOE< Real >::getValue ( const Vector< Real > &  x,
const std::vector< Real > &  xstat,
SampleGenerator< Real > &  sampler 
)
inlinevirtual

Return risk measure value.

Parameters
[in]sampleris the ROL::SampleGenerator used to sample the objective function

Upon return, getValue returns \(\mathcal{R}(f(x_0))\) where \(f(x_0)\) denotes the random variable objective function evaluated at \(x_0\).

Reimplemented from ROL::RandVarFunctional< Real >.

Definition at line 92 of file ROL_SmoothedPOE.hpp.

References ROL::SampleGenerator< Real >::sumAll(), and ROL::RandVarFunctional< Real >::val_.

template<class Real >
void ROL::SmoothedPOE< Real >::updateGradient ( Objective< Real > &  obj,
const Vector< Real > &  x,
const std::vector< Real > &  xstat,
Real &  tol 
)
inlinevirtual

Update internal risk measure storage for gradient computation.

Parameters
[in]valis the value of the random variable objective function at the current sample point
[in]gis the gradient of the random variable objective function at the current sample point
[in]weightis the weight associated with the current sample point

Reimplemented from ROL::RandVarFunctional< Real >.

Definition at line 100 of file ROL_SmoothedPOE.hpp.

References ROL::RandVarFunctional< Real >::computeGradient(), ROL::RandVarFunctional< Real >::computeValue(), ROL::RandVarFunctional< Real >::dualVector_, ROL::RandVarFunctional< Real >::g_, ROL::SmoothedPOE< Real >::smoothHeaviside(), ROL::SmoothedPOE< Real >::threshold_, and ROL::RandVarFunctional< Real >::weight_.

template<class Real >
void ROL::SmoothedPOE< Real >::getGradient ( Vector< Real > &  g,
std::vector< Real > &  gstat,
const Vector< Real > &  x,
const std::vector< Real > &  xstat,
SampleGenerator< Real > &  sampler 
)
inlinevirtual

Return risk measure (sub)gradient.

Parameters
[out]gis the (sub)gradient of the risk measure
[in]sampleris the ROL::SampleGenerator used to sample the objective function

Upon return, getGradient returns \(\theta\in\partial\mathcal{R}(f(x_0))\) where \(f(x_0)\) denotes the random variable objective function evaluated at \(x_0\) and \(\partial\mathcal{R}(X)\) denotes the subdifferential of \(\mathcal{R}\) at \(X\).

Reimplemented from ROL::RandVarFunctional< Real >.

Definition at line 112 of file ROL_SmoothedPOE.hpp.

References ROL::RandVarFunctional< Real >::g_, and ROL::SampleGenerator< Real >::sumAll().

template<class Real >
void ROL::SmoothedPOE< Real >::updateHessVec ( Objective< Real > &  obj,
const Vector< Real > &  v,
const std::vector< Real > &  vstat,
const Vector< Real > &  x,
const std::vector< Real > &  xstat,
Real &  tol 
)
inlinevirtual

Update internal risk measure storage for Hessian-time-a-vector computation.

Parameters
[in]valis the value of the random variable objective function at the current sample point
[in]gis the gradient of the random variable objective function at the current sample point
[in]gvis the gradient of the random variable objective function at the current sample point applied to the vector v0
[in]hvis the Hessian of the random variable objective function at the current sample point applied to the vector v0
[in]weightis the weight associated with the current sample point

Reimplemented from ROL::RandVarFunctional< Real >.

Definition at line 120 of file ROL_SmoothedPOE.hpp.

References ROL::RandVarFunctional< Real >::computeGradVec(), ROL::RandVarFunctional< Real >::computeHessVec(), ROL::RandVarFunctional< Real >::computeValue(), ROL::RandVarFunctional< Real >::dualVector_, ROL::RandVarFunctional< Real >::hv_, ROL::SmoothedPOE< Real >::smoothHeaviside(), ROL::SmoothedPOE< Real >::threshold_, and ROL::RandVarFunctional< Real >::weight_.

template<class Real >
void ROL::SmoothedPOE< Real >::getHessVec ( Vector< Real > &  hv,
std::vector< Real > &  hvstat,
const Vector< Real > &  v,
const std::vector< Real > &  vstat,
const Vector< Real > &  x,
const std::vector< Real > &  xstat,
SampleGenerator< Real > &  sampler 
)
inlinevirtual

Return risk measure Hessian-times-a-vector.

Parameters
[out]hvis the Hessian-times-a-vector of the risk measure
[in]sampleris the ROL::SampleGenerator used to sample the objective function

Upon return, getHessVec returns \(\nabla^2 \mathcal{R}(f(x_0))v_0\) (if available) where \(f(x_0)\) denotes the random variable objective function evaluated at \(x_0\).

Reimplemented from ROL::RandVarFunctional< Real >.

Definition at line 141 of file ROL_SmoothedPOE.hpp.

References ROL::RandVarFunctional< Real >::hv_, and ROL::SampleGenerator< Real >::sumAll().

Member Data Documentation

template<class Real >
Real ROL::SmoothedPOE< Real >::threshold_
private
template<class Real >
Real ROL::SmoothedPOE< Real >::eps_
private

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