Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T > Struct Template Reference

#include <deal.II/differentiation/ad/ad_drivers.h>

Inheritance diagram for Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >:

Public Member Functions
void	allow_dependent_variable_marking ()
void	prevent_dependent_variable_marking ()
bool	is_dependent_variable_marking_allowed () const
Drivers for scalar functions
ScalarType	value (const std::vector< ADNumberType > &dependent_variables) const
void	gradient (const std::vector< ADNumberType > &independent_variables, const std::vector< ADNumberType > &dependent_variables, Vector< ScalarType > &gradient) const
void	hessian (const std::vector< ADNumberType > &independent_variables, const std::vector< ADNumberType > &dependent_variables, FullMatrix< ScalarType > &hessian) const
Drivers for vector functions
void	values (const std::vector< ADNumberType > &dependent_variables, Vector< ScalarType > &values) const
void	jacobian (const std::vector< ADNumberType > &independent_variables, const std::vector< ADNumberType > &dependent_variables, FullMatrix< ScalarType > &jacobian) const

Static Public Member Functions
Configuration
static void	initialize_global_environment (const unsigned int n_independent_variables)

Detailed Description

template<typename ADNumberType, typename ScalarType, typename T = void>
struct Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType, T >

A prototype driver class for tapeless auto-differentiable numbers.

It is intended that this class be specialized for the valid combinations of auto-differentiable numbers and output scalar number types.

Template Parameters

ADNumberType	A type corresponding to a supported auto-differentiable number.
ScalarType	A real or complex floating point number type that is the scalar value type used for input to, and output from, operations performed with auto-differentiable numbers.
T	An arbitrary type resulting from the application of the SFINAE idiom to selectively specialize this class.

Definition at line 516 of file ad_drivers.h.

Member Function Documentation

initialize_global_environment()

template<typename ADNumberType , typename ScalarType >

void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType >::initialize_global_environment ( const unsigned int n_independent_variables )

static

In the event that the tapeless mode requires a priori knowledge of how many directional derivatives might need to be computed, this function informs the auto-differention library of what this number is.

Parameters

[in]

n_independent_variables

The number of independent variables that will be used for the entire duration of the simulation.

Warning

For ADOL-C tapeless numbers, the value given to n_independent_variables should be the maximum number of independent variables that will be used for the entire duration of the simulation. This is important in the context of, for example, hp-FEM and for multiple constitutive models with a different number of fields from which a linearization must be computed.

Definition at line 1430 of file ad_drivers.cc.

allow_dependent_variable_marking()

template<typename ADNumberType , typename ScalarType >

void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType >::allow_dependent_variable_marking

(

)

Operation status Set a flag that states that we can safely mark dependent variables within the current phase of operations.

Definition at line 1438 of file ad_drivers.cc.

prevent_dependent_variable_marking()

template<typename ADNumberType , typename ScalarType >

void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType >::prevent_dependent_variable_marking

(

)

Set a flag that states that we cannot safely mark dependent variables within the current phase of operations.

Definition at line 1446 of file ad_drivers.cc.

is_dependent_variable_marking_allowed()

template<typename ADNumberType , typename ScalarType >

bool Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType >::is_dependent_variable_marking_allowed

(

)

const

Query a flag as to whether or not dependent variables can be marked within the current phase of operations.

Definition at line 1454 of file ad_drivers.cc.

value()

template<typename ADNumberType , typename ScalarType >

ScalarType Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType >::value

(

const std::vector< ADNumberType > &

dependent_variables

)

const

Compute the value of the scalar field.

Parameters

[in]

dependent_variables

The dependent variables whose values are to be extracted.

Returns

The scalar value of the function.

Definition at line 1464 of file ad_drivers.cc.

gradient()

template<typename ADNumberType , typename ScalarType >

void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType >::gradient	(	const std::vector< ADNumberType > &	independent_variables,
		const std::vector< ADNumberType > &	dependent_variables,
		Vector< ScalarType > &	gradient ) const

Compute the gradient of the scalar field with respect to all independent variables.

Parameters

[in]	independent_variables	The independent variables whose sensitivities were tracked.
[in]	dependent_variables	The (single) dependent variable whose gradients are to be extracted.
[out]	gradient	The values of the dependent function's gradients. It is expected that this vector be of the correct size (with length n_independent_variables).

Definition at line 1474 of file ad_drivers.cc.

hessian()

template<typename ADNumberType , typename ScalarType >

void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType >::hessian	(	const std::vector< ADNumberType > &	independent_variables,
		const std::vector< ADNumberType > &	dependent_variables,
		FullMatrix< ScalarType > &	hessian ) const

Compute the Hessian of the scalar field with respect to all independent variables.

Parameters

[in]	independent_variables	The independent variables whose sensitivities were tracked.
[in]	dependent_variables	The (single) dependent variable whose Hessians are to be extracted.
[out]	hessian	The values of the dependent function's Hessian. It is expected that this matrix be of the correct size (with dimensions n_independent_variables \(\times\)n_independent_variables).

Definition at line 1485 of file ad_drivers.cc.

values()

template<typename ADNumberType , typename ScalarType >

void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType >::values	(	const std::vector< ADNumberType > &	dependent_variables,
		Vector< ScalarType > &	values ) const

Compute the values of the vector field.

Parameters

[in]	dependent_variables	The dependent variables whose Hessians are to be extracted.
[out]	values	The component values of the dependent functions. It is expected that this vector be of the correct size (with length n_dependent_variables).

Definition at line 1496 of file ad_drivers.cc.

jacobian()

template<typename ADNumberType , typename ScalarType >

void Differentiation::AD::TapelessDrivers< ADNumberType, ScalarType >::jacobian	(	const std::vector< ADNumberType > &	independent_variables,
		const std::vector< ADNumberType > &	dependent_variables,
		FullMatrix< ScalarType > &	jacobian ) const

Compute the Jacobian of the vector field.

The Jacobian of a vector field is in essence the gradient of each dependent variable with respect to all independent variables. This operation is therefore analogous to the gradient() operation performed on a collection of scalar valued fields.

Parameters

[in]	independent_variables	The independent variables whose sensitivities were tracked.
[in]	dependent_variables	The dependent variables whose Jacobian are to be extracted.
[out]	jacobian	The component values of the dependent functions' Jacobian. It is expected that this matrix be of the correct size (with dimensions n_dependent_variables \(\times\)n_independent_variables).

Definition at line 1506 of file ad_drivers.cc.

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

• include/deal.II/differentiation/ad/ad_drivers.h
• source/differentiation/ad/ad_drivers.cc