#include "ROL_Vector.hpp"#include "ROL_Objective.hpp"#include "ROL_Ptr.hpp"#include "ROL_SampleGenerator.hpp"#include "ROL_ScalarController.hpp"#include "ROL_VectorController.hpp"Go to the source code of this file.
Functions | |
| virtual void | resetStorage (bool flag=true) |
| virtual void | resetStorage (UpdateType type) |
| virtual void | initialize (const Vector< Real > &x) |
| Initialize temporary variables. | |
| virtual void | setSample (const std::vector< Real > &point, const Real weight) |
| virtual Real | computeStatistic (const Ptr< const std::vector< Real > > &xstat) const |
| Compute statistic. | |
| virtual void | updateValue (Objective< Real > &obj, const Vector< Real > &x, const std::vector< Real > &xstat, Real &tol) |
| Update internal storage for value computation. | |
| virtual void | updateGradient (Objective< Real > &obj, const Vector< Real > &x, const std::vector< Real > &xstat, Real &tol) |
| Update internal risk measure storage for gradient computation. | |
| virtual 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. | |
| virtual Real | getValue (const Vector< Real > &x, const std::vector< Real > &xstat, SampleGenerator< Real > &sampler) |
| Return risk measure value. | |
| virtual 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. | |
| virtual 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. | |
Function Documentation
◆ resetStorage() [1/2]
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Definition at line 192 of file ROL_RandVarFunctional.hpp.
◆ resetStorage() [2/2]
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Definition at line 204 of file ROL_RandVarFunctional.hpp.
◆ initialize()
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Initialize temporary variables.
@param[in] x is a vector used for initializing storage
Definition at line 219 of file ROL_RandVarFunctional.hpp.
References zero.
Referenced by ROL::CompositeObjective< Real >::computeValue(), ROL::TypeB::ColemanLiAlgorithm< Real >::run(), ROL::TypeB::GradientAlgorithm< Real >::run(), ROL::TypeB::InteriorPointAlgorithm< Real >::run(), ROL::TypeB::KelleySachsAlgorithm< Real >::run(), ROL::TypeB::LinMoreAlgorithm< Real >::run(), ROL::TypeB::LSecantBAlgorithm< Real >::run(), ROL::TypeB::MoreauYosidaAlgorithm< Real >::run(), ROL::TypeB::NewtonKrylovAlgorithm< Real >::run(), ROL::TypeB::PrimalDualActiveSetAlgorithm< Real >::run(), ROL::TypeB::QuasiNewtonAlgorithm< Real >::run(), ROL::TypeB::SpectralGradientAlgorithm< Real >::run(), ROL::TypeB::TrustRegionSPGAlgorithm< Real >::run(), ROL::TypeE::AugmentedLagrangianAlgorithm< Real >::run(), ROL::TypeE::FletcherAlgorithm< Real >::run(), ROL::TypeE::StabilizedLCLAlgorithm< Real >::run(), ROL::TypeG::AugmentedLagrangianAlgorithm< Real >::run(), ROL::TypeG::InteriorPointAlgorithm< Real >::run(), ROL::TypeG::MoreauYosidaAlgorithm< Real >::run(), ROL::TypeG::StabilizedLCLAlgorithm< Real >::run(), ROL::TypeP::InexactNewtonAlgorithm< Real >::run(), ROL::TypeP::iPianoAlgorithm< Real >::run(), ROL::TypeP::ProxGradientAlgorithm< Real >::run(), ROL::TypeP::QuasiNewtonAlgorithm< Real >::run(), ROL::TypeP::SpectralGradientAlgorithm< Real >::run(), ROL::TypeP::TrustRegionAlgorithm< Real >::run(), ROL::TypeU::BundleAlgorithm< Real >::run(), and ROL::TypeU::LineSearchAlgorithm< Real >::run().
◆ setSample()
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Definition at line 237 of file ROL_RandVarFunctional.hpp.
◆ computeStatistic()
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Compute statistic.
- Parameters
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[in] xstat is a ROL::Ptr to a std::vector containing the statistic vector
Definition at line 247 of file ROL_RandVarFunctional.hpp.
Referenced by ROL::OptimizationProblem< Real >::getSolutionStatistic(), and ROL::StochasticProblem< Real >::getSolutionStatistic().
◆ updateValue()
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Update internal storage for value computation.
- Parameters
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[in] val is the value of the random variable objective function at the current sample point [in] weight is the weight associated with the current sample point
Definition at line 262 of file ROL_RandVarFunctional.hpp.
◆ updateGradient()
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Update internal risk measure storage for gradient computation.
- Parameters
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[in] val is the value of the random variable objective function at the current sample point [in] g is the gradient of the random variable objective function at the current sample point [in] weight is the weight associated with the current sample point
Definition at line 279 of file ROL_RandVarFunctional.hpp.
◆ updateHessVec()
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Update internal risk measure storage for Hessian-time-a-vector computation.
- Parameters
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[in] val is the value of the random variable objective function at the current sample point [in] g is the gradient of the random variable objective function at the current sample point [in] gv is the gradient of the random variable objective function at the current sample point applied to the vector v0 [in] hv is the Hessian of the random variable objective function at the current sample point applied to the vector v0 [in] weight is the weight associated with the current sample point
Definition at line 302 of file ROL_RandVarFunctional.hpp.
◆ getValue()
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Return risk measure value.
- Parameters
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[in] sampler is 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 \(x_0\).
Definition at line 320 of file ROL_RandVarFunctional.hpp.
Referenced by ROL::ConstraintFromObjective< Real >::applyAdjointHessian(), ROL::ConstraintFromObjective< Real >::applyAdjointJacobian(), and ROL::ConstraintFromObjective< Real >::getValue().
◆ getGradient()
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Return risk measure (sub)gradient.
- Parameters
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[out] g is the (sub)gradient of the risk measure [in] sampler is 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 \(X\).
Definition at line 339 of file ROL_RandVarFunctional.hpp.
◆ getHessVec()
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Return risk measure Hessian-times-a-vector.
- Parameters
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[out] hv is the Hessian-times-a-vector of the risk measure [in] sampler is 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 \(x_0\).
Definition at line 358 of file ROL_RandVarFunctional.hpp.
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