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

Static Public Member Functions
Taping
static void	enable_taping (const types::tape_index tape_index, const bool keep_independent_values)

static void	enable_taping (const types::tape_index tape_index, const bool keep_independent_values, const types::tape_buffer_sizes obufsize, const types::tape_buffer_sizes lbufsize, const types::tape_buffer_sizes vbufsize, const types::tape_buffer_sizes tbufsize)

static void	disable_taping (const types::tape_index active_tape_index, const bool write_tapes_to_file)

static void	print_tape_stats (std::ostream &stream, const types::tape_index tape_index)

Drivers for scalar functions (one dependent variable)
static ScalarType	value (const types::tape_index active_tape_index, const std::vector< ScalarType > &independent_variables)

static void	gradient (const types::tape_index active_tape_index, const std::vector< ScalarType > &independent_variables, Vector< ScalarType > &gradient)

static void	hessian (const types::tape_index active_tape_index, const std::vector< ScalarType > &independent_variables, FullMatrix< ScalarType > &hessian)

Drivers for vector functions (multiple dependent variables)
static void	values (const types::tape_index active_tape_index, const unsigned int n_dependent_variables, const std::vector< ScalarType > &independent_variables, Vector< ScalarType > &values)

static void	jacobian (const types::tape_index active_tape_index, const unsigned int n_dependent_variables, const std::vector< ScalarType > &independent_variables, FullMatrix< ScalarType > &jacobian)

Detailed Description

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

A driver class for taped 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.

Author: Jean-Paul Pelteret, 2017

Definition at line 156 of file ad_drivers.h.

Member Function Documentation

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapedDrivers< ADNumberType, ScalarType, T >::enable_taping	(	const types::tape_index	tape_index,
		const bool	keep_independent_values
	)

static

Enable the recording mode for a given tape.

Parameters

[in]	tape_index	The index of the tape to be written.
[in]	keep_independent_values	Determines whether the numerical values of all independent variables are recorded in the tape buffer.

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapedDrivers< ADNumberType, ScalarType, T >::enable_taping	(	const types::tape_index	tape_index,
		const bool	keep_independent_values,
		const types::tape_buffer_sizes	obufsize,
		const types::tape_buffer_sizes	lbufsize,
		const types::tape_buffer_sizes	vbufsize,
		const types::tape_buffer_sizes	tbufsize
	)

static

Enable the recording mode for a given tape, using the run-time chosen tape buffer sizes.

Parameters

[in]	tape_index	The index of the tape to be written.
[in]	keep_independent_values	Determines whether the numerical values of all independent variables are recorded in the tape buffer.
[in]	obufsize	ADOL-C operations buffer size
[in]	lbufsize	ADOL-C locations buffer size
[in]	vbufsize	ADOL-C value buffer size
[in]	tbufsize	ADOL-C Taylor buffer size

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapedDrivers< ADNumberType, ScalarType, T >::disable_taping	(	const types::tape_index	active_tape_index,
		const bool	write_tapes_to_file
	)

static

Disable the recording mode for a given tape.

Parameters

[in]	active_tape_index	The index of the (currently active) tape to be finalized and potentially written to file.
[in]	write_tapes_to_file	A flag that specified whether the tape should be written to file or kept in memory.

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapedDrivers< ADNumberType, ScalarType, T >::print_tape_stats	(	std::ostream &	stream,
		const types::tape_index	tape_index
	)

static

Prints the statistics regarding the usage of the tapes.

Parameters

[in]	stream	The output stream to which the values are to be written.
[in]	tape_index	The index of the tape to get the statistics of.

template<typename ADNumberType , typename ScalarType , typename T = void>

static ScalarType Differentiation::AD::TapedDrivers< ADNumberType, ScalarType, T >::value	(	const types::tape_index	active_tape_index,
		const std::vector< ScalarType > &	independent_variables
	)

static

Computes the value of the scalar field.

Parameters

[in]	active_tape_index	The index of the tape on which the dependent function is recorded.
[in]	independent_variables	The scalar values of the independent variables whose sensitivities were tracked.

Returns: The scalar value of the function.

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapedDrivers< ADNumberType, ScalarType, T >::gradient	(	const types::tape_index	active_tape_index,
		const std::vector< ScalarType > &	independent_variables,
		Vector< ScalarType > &	gradient
	)

static

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

Parameters

[in]	active_tape_index	The index of the tape on which the dependent function is recorded.
[in]	independent_variables	The scalar values of the independent variables whose sensitivities were tracked.
[out]	gradient	The scalar values of the dependent function's gradients. It is expected that this vector be of the correct size (with length `n_independent_variables`).

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapedDrivers< ADNumberType, ScalarType, T >::hessian	(	const types::tape_index	active_tape_index,
		const std::vector< ScalarType > &	independent_variables,
		FullMatrix< ScalarType > &	hessian
	)

static

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

Parameters

[in]	active_tape_index	The index of the tape on which the dependent function is recorded.
[in]	independent_variables	The scalar values of the independent variables whose sensitivities were tracked.
[out]	hessian	The scalar 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`).

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapedDrivers< ADNumberType, ScalarType, T >::values	(	const types::tape_index	active_tape_index,
		const unsigned int	n_dependent_variables,
		const std::vector< ScalarType > &	independent_variables,
		Vector< ScalarType > &	values
	)

static

Computes the values of the vector field.

Parameters

[in]	active_tape_index	The index of the tape on which the dependent function is recorded.
[in]	n_dependent_variables	The number of dependent variables.
[in]	independent_variables	The scalar values of the independent variables whose sensitivities were tracked.
[out]	values	The scalar values of the dependent functions. It is expected that this vector be of the correct size (with length `n_dependent_variables`).

template<typename ADNumberType , typename ScalarType , typename T = void>

static void Differentiation::AD::TapedDrivers< ADNumberType, ScalarType, T >::jacobian	(	const types::tape_index	active_tape_index,
		const unsigned int	n_dependent_variables,
		const std::vector< ScalarType > &	independent_variables,
		FullMatrix< ScalarType > &	jacobian
	)

static

Computes the Jacobian of the vector field.

The Jacobian of a vector field is in essense 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]	active_tape_index	The index of the tape on which the dependent function is recorded.
[in]	n_dependent_variables	The number of dependent variables.
[in]	independent_variables	The scalar values of the independent variables whose sensitivities were tracked.
[out]	jacobian	The scalar 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`).

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

deal.II/differentiation/ad/ad_drivers.h

Static Public Member Functions

Detailed Description

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

Member Function Documentation

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