GP_Objective Class Reference

ROL objective function for the Gaussian Process (GP) surrogate. More...

Inherits Objective< double >.

Public Member Functions
	GP_Objective (GaussianProcess &gp_model)
	Constructor for GP_Objective. More...
double	value (const ROL::Vector< double > &p, double &tol)
	Get the value of the objective function at a point. More...
void	gradient (ROL::Vector< double > &g, const ROL::Vector< double > &p, double &tol)
	Get the gradient of the objective function at a point. More...

Private Member Functions
bool	pdiff (const std::vector< double > &pnew)
	Compute the l2 norm of the difference between new and old parameter vectors. More...
ROL::Ptr< const std::vector< double > >	getVector (const ROL::Vector< double > &vec)
	Convert a const ROL Vector to a ROL::Ptr<const std::vector> More...
ROL::Ptr< std::vector< double > >	getVector (ROL::Vector< double > &vec)
	Convert a ROL Vector to a ROL::Ptr<std::vector> More...

Private Attributes
GaussianProcess &	gp
	Pointer to the GaussianProcess surrogate.
int	nopt
	Number of optimization variables.
double	Jold
	Previously computed value of the objective function.
VectorXd	grad_old
	Previously computed gradient of the objective function.
VectorXd	pold
	Previous value of the parameter vector.

Detailed Description

ROL objective function for the Gaussian Process (GP) surrogate.

Constructor & Destructor Documentation

GP_Objective()

GP_Objective

(

GaussianProcess &

gp_model

)

Constructor for GP_Objective.

Parameters

[in]

gp_model

Reference to the GaussianProcess surrogate.

References GaussianProcess::get_num_opt_variables(), GP_Objective::gp, GP_Objective::grad_old, GP_Objective::Jold, GP_Objective::nopt, and GP_Objective::pold.

Member Function Documentation

value()

double value	(	const ROL::Vector< double > &	p,
		double &	tol
	)

Get the value of the objective function at a point.

Parameters

[in]	p	ROL vector of parameters.
[in]	tol	Tolerance for inexact evaluation (not used here).

References GP_Objective::getVector(), GP_Objective::gp, GaussianProcess::negative_marginal_log_likelihood(), GP_Objective::nopt, GP_Objective::pdiff(), GaussianProcess::set_opt_params(), and dakota::silence_unused_args().

gradient()

void gradient	(	ROL::Vector< double > &	g,
		const ROL::Vector< double > &	p,
		double &	tol
	)

Get the gradient of the objective function at a point.

Parameters

[out]	g	Gradient of the objective function.
[in]	p	ROL vector of parameters.
[in]	tol	Tolerance for inexact evaluation (not used here).

References GP_Objective::getVector(), GP_Objective::gp, GaussianProcess::negative_marginal_log_likelihood(), GP_Objective::nopt, GP_Objective::pdiff(), GaussianProcess::set_opt_params(), and dakota::silence_unused_args().

pdiff()

bool pdiff ( const std::vector< double > &  pnew )

private

Compute the l2 norm of the difference between new and old parameter vectors.

Parameters

[in]

pnew

New value of the parameter vector.

References dakota::near_zero, GP_Objective::nopt, and GP_Objective::pold.

Referenced by GP_Objective::gradient(), and GP_Objective::value().

getVector() [1/2]

ROL::Ptr<const std::vector<double> > getVector ( const ROL::Vector< double > &  vec )

inlineprivate

Convert a const ROL Vector to a ROL::Ptr<const std::vector>

Parameters

[in]

vec

const ROL vector

Referenced by GP_Objective::getVector(), GP_Objective::gradient(), and GP_Objective::value().

getVector() [2/2]

ROL::Ptr<std::vector<double> > getVector ( ROL::Vector< double > &  vec )

inlineprivate

Convert a ROL Vector to a ROL::Ptr<std::vector>

Parameters

[in]

vec

ROL vector

References GP_Objective::getVector().

---

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

• SurrogatesGPObjective.hpp
• SurrogatesGPObjective.cpp