SG++-Doxygen-Documentation
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Sparse grid interpolant Hessian of a vector-valued function. More...
#include <InterpolantVectorFunctionHessian.hpp>
Public Member Functions | |
void | clone (std::unique_ptr< VectorFunctionHessian > &clone) const override |
void | eval (const base::DataVector &x, base::DataVector &value, base::DataMatrix &gradient, std::vector< base::DataMatrix > &hessian) override |
Evaluation of the function and its gradient. More... | |
const base::DataMatrix & | getAlpha () const |
InterpolantVectorFunctionHessian (base::Grid &grid, const base::DataMatrix &alpha) | |
Constructor. More... | |
void | setAlpha (const base::DataMatrix &alpha) |
~InterpolantVectorFunctionHessian () override | |
Destructor. More... | |
Public Member Functions inherited from sgpp::optimization::VectorFunctionHessian | |
size_t | getNumberOfComponents () const |
size_t | getNumberOfParameters () const |
VectorFunctionHessian (size_t d, size_t m) | |
Constructor. More... | |
virtual | ~VectorFunctionHessian () |
Destructor. More... | |
Protected Attributes | |
base::DataMatrix | alpha |
coefficient matrix More... | |
base::Grid & | grid |
sparse grid More... | |
std::unique_ptr< base::OperationEvalHessian > | opEvalHessian |
pointer to evaluation operation More... | |
Protected Attributes inherited from sgpp::optimization::VectorFunctionHessian | |
size_t | d |
dimension of the domain More... | |
size_t | m |
number of components More... | |
Sparse grid interpolant Hessian of a vector-valued function.
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inline |
Constructor.
Do not destruct the grid before the InterpolantVectorFunctionGradient object!
grid | sparse grid |
alpha | coefficient matrix (j-th column contains hierarchical surplusses \(\alpha_{\cdot,j}\) of \(g_j\)) |
Referenced by clone().
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inlineoverride |
Destructor.
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inlineoverridevirtual |
[out] | clone | pointer to cloned object |
Implements sgpp::optimization::VectorFunctionHessian.
References alpha, grid, and InterpolantVectorFunctionHessian().
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inlineoverridevirtual |
Evaluation of the function and its gradient.
[in] | x | evaluation point \(\vec{x} \in [0, 1]^d\) |
[out] | value | \(g(\vec{x})\) |
[out] | gradient | Jacobian \(\nabla g(\vec{x}) \in \mathbb{R}^{m \times d}\) |
[out] | hessian | \(m\)-vector of Hessians \(\nabla^2 g_i(\vec{x}) \in \mathbb{R}^{d \times d}\) |
Implements sgpp::optimization::VectorFunctionHessian.
References alpha, sgpp::optimization::VectorFunctionHessian::d, opEvalHessian, and sgpp::base::DataVector::setAll().
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inline |
References alpha.
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inline |
alpha | coefficient matrix |
References alpha.
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protected |
coefficient matrix
Referenced by clone(), eval(), getAlpha(), python.learner.Classifier.Classifier::refineGrid(), and setAlpha().
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protected |
sparse grid
Referenced by clone(), python.uq.learner.Interpolant.Interpolant::doLearningIteration(), python.learner.Classifier.Classifier::evalError(), python.uq.learner.Interpolant.Interpolant::evalError(), python.uq.learner.SimulationLearner.SimulationLearner::getCollocationNodes(), python.uq.learner.SimulationLearner.SimulationLearner::getGrid(), python.uq.learner.SimulationLearner.SimulationLearner::getLearner(), python.uq.learner.Regressor.Regressor::learnData(), python.uq.learner.Regressor.Regressor::learnDataWithFolding(), python.uq.learner.Regressor.Regressor::learnDataWithTest(), python.learner.Classifier.Classifier::refineGrid(), python.learner.Regressor.Regressor::refineGrid(), python.uq.learner.Regressor.Regressor::refineGrid(), python.uq.learner.SimulationLearner.SimulationLearner::refineGrid(), python.learner.Classifier.Classifier::updateResults(), python.learner.Regressor.Regressor::updateResults(), and python.uq.learner.Regressor.Regressor::updateResults().
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protected |
pointer to evaluation operation
Referenced by eval().