Abstract base class for scalar-valued functions \(f\colon [0, 1]^d \to \mathbb{R}\) together with their gradients \(\nabla f\colon [0, 1]^d \to \mathbb{R}^d\) (e.g., gradients of objective functions in optimization). More...

#include <ScalarFunctionGradient.hpp>

Inheritance diagram for sgpp::base::ScalarFunctionGradient:

Public Member Functions
virtual void	clone (std::unique_ptr< ScalarFunctionGradient > &clone) const =0
	Pure virtual method for cloning the gradient.

virtual void	eval (const DataMatrix &x, DataVector &value, DataMatrix &gradient)
	Convenience method for calculating \(f(\vec{x})\) together with \(\nabla f(\vec{x})\) for multiple \(\vec{x}\).

virtual double	eval (const DataVector &x, DataVector &gradient)=0
	Pure virtual method for calculating \(f(\vec{x})\) together with \(\nabla f(\vec{x})\).

size_t	getNumberOfParameters () const

	ScalarFunctionGradient (size_t d)
	Constructor.

virtual	~ScalarFunctionGradient ()
	Destructor.

Protected Attributes
size_t	d
	dimension of the domain

Detailed Description

Abstract base class for scalar-valued functions \(f\colon [0, 1]^d \to \mathbb{R}\) together with their gradients \(\nabla f\colon [0, 1]^d \to \mathbb{R}^d\) (e.g., gradients of objective functions in optimization).

Constructor & Destructor Documentation

◆ ScalarFunctionGradient()

sgpp::base::ScalarFunctionGradient::ScalarFunctionGradient ( size_t d )

inlineexplicit

Constructor.

Parameters

d	dimension of the domain

◆ ~ScalarFunctionGradient()

virtual sgpp::base::ScalarFunctionGradient::~ScalarFunctionGradient ( )

inlinevirtual

Destructor.

Member Function Documentation

◆ clone()

virtual void sgpp::base::ScalarFunctionGradient::clone ( std::unique_ptr< ScalarFunctionGradient > & clone ) const

pure virtual

Pure virtual method for cloning the gradient.

It should generate a pointer to the cloned object and it's used for parallel computations (the eval() method might not be thread-safe).

Parameters

[out] clone pointer to cloned object

Implemented in sgpp::base::ComponentScalarFunctionGradient, sgpp::base::InterpolantScalarFunctionGradient, sgpp::base::ScaledScalarFunctionGradient, and sgpp::base::WrapperScalarFunctionGradient.

◆ eval() [1/2]

virtual void sgpp::base::ScalarFunctionGradient::eval	(	const DataMatrix &	x,
		DataVector &	value,
		DataMatrix &	gradient
	)

inlinevirtual

Convenience method for calculating \(f(\vec{x})\) together with \(\nabla f(\vec{x})\) for multiple \(\vec{x}\).

Parameters

	x	matrix \(\vec{x} \in [0, 1]^{N \times d}\) of evaluation points (row-wise)
[out]	value	vector of size \(N\), where the \(k\)-th entry is \(f(\vec{x}_k)\) (where \(\vec{x}_k\) is the \(k\)-th row of \(x\))
[out]	gradient	matrix of size \(N \times d\) where the \(k\)-th row is \(\nabla f(\vec{x}_k)\)

References d, eval(), sgpp::base::DataMatrix::getNrows(), sgpp::base::DataMatrix::getRow(), sgpp::base::DataMatrix::resize(), and sgpp::base::DataMatrix::setRow().

Referenced by python.uq.analysis.asgc.ASGCAnalysis.ASGCAnalysis::estimateDensity().