Class to solve linear equations with total variation (TV) regularization using the alternating direction method of multipliers (ADMM) [2]. More...

#include <num_collect/regularization/tv_admm.h>

Inheritance diagram for num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >:

Collaboration diagram for num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >:

Public Types
using	base_type = iterative_regularized_solver_base<this_type, Data>
	Type of the base class.
using	coeff_type = Coeff
	Type of coefficient matrices.
using	derivative_matrix_type = DerivativeMatrix
	Type of matrices to compute derivatives.
using	this_type = tv_admm<Coeff, DerivativeMatrix, Data>
	This type.
Public Types inherited from num_collect::regularization::regularized_solver_base< tv_admm< Coeff, DerivativeMatrix, Data >, Data >
using	data_type
	Type of data.
using	scalar_type
	Type of scalars.

Public Member Functions
	tv_admm ()
	Constructor.
void	calculate_data_for (const data_type &solution, data_type &data) const
	Calculate data for a solution.
void	change_data (const data_type &data)
	Change data.
void	compute (const Coeff &coeff, const DerivativeMatrix &derivative_matrix, const Data &data)
	Compute internal parameters.
void	configure_iteration_logger (logging::iterations::iteration_logger< this_type > &iteration_logger) const
	Configure an iteration logger.
auto	data_size () const -> index_type
	Get the size of data.
void	init (const scalar_type &param, data_type &solution)
	Initialize.
auto	is_stop_criteria_satisfied (const data_type &solution) const -> bool
	Determine if stopping criteria of the algorithm are satisfied.
void	iterate (const scalar_type &param, data_type &solution)
	Iterate the algorithm once.
auto	iterations () const noexcept -> index_type
	Get the number of iterations.
auto	max_iterations () const -> index_type
	Get the maximum number of iterations.
auto	max_iterations (index_type value) -> tv_admm &
	Set the maximum number of iterations.
auto	param_search_region () const -> std::pair< scalar_type, scalar_type >
	Get the default region to search for the optimal regularization parameter.
auto	regularization_term (const data_type &solution) const -> scalar_type
	Calculate the regularization term.
auto	residual_norm (const data_type &solution) const -> scalar_type
	Calculate the squared norm of the residual.
auto	residual_norm_rate () const -> scalar_type
	Get the rate of the last residual norm.
auto	tol_update_rate () const -> scalar_type
	Get the tolerance of update rate of the solution.
auto	tol_update_rate (scalar_type value) -> tv_admm &
	Set the tolerance of update rate of the solution.
auto	update_rate () const noexcept -> scalar_type
	Get the rate of the norm of the update of the solution in the last iteration.
Public Member Functions inherited from num_collect::regularization::iterative_regularized_solver_base< tv_admm< Coeff, DerivativeMatrix, Data >, Data >
void	init (const scalar_type &param, data_type &solution)
	Initialize.
auto	is_stop_criteria_satisfied (const data_type &solution) const -> bool
	Determine if stopping criteria of the algorithm are satisfied.
void	iterate (const scalar_type &param, data_type &solution)
	Iterate the algorithm once.
void	solve (const scalar_type &param, data_type &solution)
	Solve for a regularization parameter.
Public Member Functions inherited from num_collect::regularization::implicit_regularized_solver_base< tv_admm< Coeff, DerivativeMatrix, Data >, Data >
void	calculate_data_for (const data_type &solution, data_type &data) const
	Calculate data for a solution.
void	change_data (const data_type &data)
	Change data.
auto	regularization_term (const data_type &solution) const -> scalar_type
	Calculate the regularization term.
auto	residual_norm (const data_type &solution) const -> scalar_type
	Calculate the squared norm of the residual.
Public Member Functions inherited from num_collect::regularization::regularized_solver_base< tv_admm< Coeff, DerivativeMatrix, Data >, Data >
auto	data_size () const -> index_type
	Get the size of data.
auto	param_search_region () const -> std::pair< scalar_type, scalar_type >
	Get the default region to search for the optimal regularization parameter.
void	solve (const scalar_type &param, data_type &solution)
	Solve for a regularization parameter.
Public Member Functions inherited from num_collect::logging::logging_mixin
	logging_mixin (log_tag_view tag)
	Constructor.
template<typename Child>
void	configure_child_algorithm_logger_if_exists (Child &child)
	Configure a logger of a child algorithm if exists.
auto	logger () const noexcept -> const num_collect::logging::logger &
	Access to the logger.
auto	logger () noexcept -> num_collect::logging::logger &
	Access to the logger.
Public Member Functions inherited from num_collect::logging::iterations::iteration_logger_mixin< tv_admm< Coeff, DerivativeMatrix, Data > >
	iteration_logger_mixin ()=default
	Constructor.
void	configure_iteration_logger (num_collect::logging::iterations::iteration_logger< tv_admm< Coeff, DerivativeMatrix, Data > > &iteration_logger) const
	Configure an iteration logger.
auto	initialize_iteration_logger () -> num_collect::logging::iterations::iteration_logger< tv_admm< Coeff, DerivativeMatrix, Data > > &
	Get the iteration logger.

Private Attributes
const coeff_type *	coeff_ {nullptr}
	Coefficient matrix to compute data vector.
linear::impl::operator_conjugate_gradient< data_type >	conjugate_gradient_ {}
	Conjugate gradient solver.
const data_type *	data_ {nullptr}
	Data vector.
data_type	derivative_ {}
	Derivative.
scalar_type	derivative_constraint_coeff_
	Coefficient of the constraint for the derivative.
const derivative_matrix_type *	derivative_matrix_ {nullptr}
	Matrix to compute derivative.
index_type	iterations_ {}
	Number of iterations.
data_type	lagrange_multiplier_ {}
	Lagrange multiplier.
data_type	lagrange_multiplier_update_ {}
	Update of lagrange multiplier.
index_type	max_iterations_ {default_max_iterations}
	Maximum number of iterations.
data_type	previous_derivative_ {}
	Previous derivative.
data_type	previous_solution_ {}
	Previous solution.
scalar_type	rate_of_cg_tol_rate_to_tol_update_rate_
	Rate of the ratio of the rate of tolerance in CG method to the tolerance of update rate of the solution.
data_type	residual_ {}
	Residual vector.
data_type	temp_data_ {}
	Temporary vector with the size of data.
data_type	temp_derivative_ {}
	Temporary vector with the size of the derivative.
data_type	temp_solution_ {}
	Temporary vector for the update of the solution.
scalar_type	tol_update_rate_ {default_tol_update_rate}
	Tolerance of update rate of the solution.
scalar_type	update_rate_ {}
	Rate of norm of the update of the solution in the last iteration.

Static Private Attributes
static constexpr auto	default_derivative_constraint_coeff
	Default coefficient of the constraint for the derivative.
static constexpr index_type	default_max_iterations = 10000
	Default maximum number of iterations.
static constexpr auto	default_rate_of_cg_tol_rate_to_tol_update_rate
	Default value of the ratio of the rate of tolerance in CG method to the tolerance of update rate of the solution.
static constexpr auto	default_tol_update_rate
	Default tolerance of update rate of the solution.

Additional Inherited Members
Protected Member Functions inherited from num_collect::regularization::iterative_regularized_solver_base< tv_admm< Coeff, DerivativeMatrix, Data >, Data >
	iterative_regularized_solver_base (logging::log_tag_view tag)
	Constructor.
Protected Member Functions inherited from num_collect::regularization::implicit_regularized_solver_base< tv_admm< Coeff, DerivativeMatrix, Data >, Data >
	implicit_regularized_solver_base (logging::log_tag_view tag)
	Constructor.
auto	derived () noexcept -> tv_admm< Coeff, DerivativeMatrix, Data > &
	Access derived object.
Protected Member Functions inherited from num_collect::regularization::regularized_solver_base< tv_admm< Coeff, DerivativeMatrix, Data >, Data >
	regularized_solver_base (logging::log_tag_view tag)
	Constructor.
auto	derived () noexcept -> tv_admm< Coeff, DerivativeMatrix, Data > &
	Access derived object.

Detailed Description

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>
requires ((base::concepts::sparse_matrix<Coeff> || base::concepts::dense_matrix<Coeff>) && (base::concepts::sparse_matrix<DerivativeMatrix> || base::concepts::dense_matrix<DerivativeMatrix>))
class num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >

Class to solve linear equations with total variation (TV) regularization using the alternating direction method of multipliers (ADMM) [2].

Template Parameters

Coeff	Type of coefficient matrices.
DerivativeMatrix	Type of matrices to compute derivatives.
Data	Type of data vectors.

This class minimizes the following evaluation function:

\[\| A \boldsymbol{x} - \boldsymbol{y} \|_2^2 + \lambda \| D \boldsymbol{x} \|_1 \]

where variables are defined as follows:

\(A\) is a coefficient matrix.
\(\boldsymbol{x}\) is a solution vector.
\(\boldsymbol{y}\) is a data vector.
\(\lambda\) is a regularization parameter.
\(D\) is a matrix to compute derivatives.

Examples: regularization/image_denoising_tv_admm.cpp.

Definition at line 77 of file tv_admm.h.

Member Typedef Documentation

◆ base_type

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

using num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::base_type = iterative_regularized_solver_base<this_type, Data>

Type of the base class.

Definition at line 84 of file tv_admm.h.

◆ coeff_type

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

using num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::coeff_type = Coeff

Type of coefficient matrices.

Definition at line 90 of file tv_admm.h.

◆ derivative_matrix_type

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

using num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::derivative_matrix_type = DerivativeMatrix

Type of matrices to compute derivatives.

Definition at line 93 of file tv_admm.h.

◆ this_type

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

using num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::this_type = tv_admm<Coeff, DerivativeMatrix, Data>

This type.

Definition at line 81 of file tv_admm.h.

Constructor & Destructor Documentation

◆ tv_admm()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::tv_admm ( )

inline

Constructor.

Definition at line 98 of file tv_admm.h.

Member Function Documentation

◆ calculate_data_for()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

void num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::calculate_data_for	(	const data_type &	solution,
		data_type &	data ) const

inline

Calculate data for a solution.

Parameters

[in]	solution	Solution.
[out]	data	Data.

Definition at line 240 of file tv_admm.h.

◆ change_data()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

void num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::change_data ( const data_type & data )

inline

Change data.

Parameters

[in] data New data.

Definition at line 237 of file tv_admm.h.

◆ compute()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

void num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::compute	(	const Coeff &	coeff,
		const DerivativeMatrix &	derivative_matrix,
		const Data &	data )

inline

Compute internal parameters.

Parameters

[in]	coeff	Coefficient matrix.
[in]	data	Data vector.
[in]	derivative_matrix	Matrix to compute derivative.

Note: Pointers to the arguments are saved in this object, so don't destruct those arguments.; Call this before init.

Examples: regularization/image_denoising_tv_admm.cpp.

Definition at line 113 of file tv_admm.h.

◆ configure_iteration_logger()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

void num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::configure_iteration_logger ( logging::iterations::iteration_logger< this_type > & iteration_logger ) const

inline

Configure an iteration logger.

Parameters

[in] iteration_logger Iteration logger.

Definition at line 213 of file tv_admm.h.

◆ data_size()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::data_size ( ) const -> index_type

inlinenodiscard

Get the size of data.

Returns: Size of data.

Definition at line 245 of file tv_admm.h.

◆ init()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

void num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::init	(	const scalar_type &	param,
		data_type &	solution )

inline

Initialize.

Parameters

[in]	param	Regularization parameter.
[in,out]	solution	Solution. (Assumed to be the initial solution.)

Warning: Any required initializations (with compute functions) are assumed to have been done.

Definition at line 122 of file tv_admm.h.

◆ is_stop_criteria_satisfied()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::is_stop_criteria_satisfied ( const data_type & solution ) const -> bool

inlinenodiscard

Determine if stopping criteria of the algorithm are satisfied.

Parameters

[in] solution Solution.

Returns: If stopping criteria of the algorithm are satisfied.

Definition at line 205 of file tv_admm.h.

◆ iterate()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

void num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::iterate	(	const scalar_type &	param,
		data_type &	solution )

inline

Iterate the algorithm once.

Parameters

[in]	param	Regularization parameter.
[in,out]	solution	Solution. (Assumed to be the last solution.)

Warning: Any required initializations (with init functions) are assumed to have been done.

Definition at line 156 of file tv_admm.h.

◆ iterations()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::iterations ( ) const -> index_type

inlinenodiscardnoexcept

Get the number of iterations.

Returns: Value.

Definition at line 271 of file tv_admm.h.

◆ max_iterations() [1/2]

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::max_iterations ( ) const -> index_type

inlinenodiscard

Get the maximum number of iterations.

Returns: Value.

Definition at line 299 of file tv_admm.h.

◆ max_iterations() [2/2]

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::max_iterations ( index_type value ) -> tv_admm&

inline

Set the maximum number of iterations.

Parameters

[in] value Value.

Returns: This object.

Definition at line 309 of file tv_admm.h.

◆ param_search_region()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::param_search_region ( ) const -> std::pair<scalar_type, scalar_type>

inlinenodiscard

Get the default region to search for the optimal regularization parameter.

Returns: Pair of minimum and maximum regularization parameters.

Definition at line 248 of file tv_admm.h.

◆ regularization_term()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::regularization_term ( const data_type & solution ) const -> scalar_type

inlinenodiscard

Calculate the regularization term.

Parameters

[in] solution Solution.

Returns: Result.

Definition at line 231 of file tv_admm.h.

◆ residual_norm()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::residual_norm ( const data_type & solution ) const -> scalar_type

inlinenodiscard

Calculate the squared norm of the residual.

Parameters

[in] solution Solution.

Returns: Result.

Definition at line 225 of file tv_admm.h.

◆ residual_norm_rate()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::residual_norm_rate ( ) const -> scalar_type

inlinenodiscard

Get the rate of the last residual norm.

Returns: Rate of the residual norm.

Definition at line 290 of file tv_admm.h.

◆ tol_update_rate() [1/2]

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::tol_update_rate ( ) const -> scalar_type

inlinenodiscard

Get the tolerance of update rate of the solution.

Returns: Value.

Definition at line 321 of file tv_admm.h.

◆ tol_update_rate() [2/2]

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::tol_update_rate ( scalar_type value ) -> tv_admm&

inline

Set the tolerance of update rate of the solution.

Parameters

[in] value Value.

Returns: This object.

Definition at line 331 of file tv_admm.h.

◆ update_rate()

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::update_rate ( ) const -> scalar_type

inlinenodiscardnoexcept

Get the rate of the norm of the update of the solution in the last iteration.

Returns: Value.

Definition at line 281 of file tv_admm.h.

Member Data Documentation

◆ coeff_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

const coeff_type* num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::coeff_ {nullptr}

private

Coefficient matrix to compute data vector.

Definition at line 341 of file tv_admm.h.

◆ conjugate_gradient_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

linear::impl::operator_conjugate_gradient<data_type> num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::conjugate_gradient_ {}

private

Conjugate gradient solver.

Definition at line 383 of file tv_admm.h.

◆ data_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

const data_type* num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::data_ {nullptr}

private

Data vector.

Definition at line 347 of file tv_admm.h.

◆ default_derivative_constraint_coeff

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::default_derivative_constraint_coeff

staticconstexprprivate

Initial value:

=

static_cast<scalar_type>(1)

num_collect::regularization::regularized_solver_base< tv_admm< Coeff, DerivativeMatrix, Data >, Data >::scalar_type

typename Eigen::NumTraits< typename data_type::Scalar >::Real scalar_type

Definition regularized_solver_base.h:46

Default coefficient of the constraint for the derivative.

Definition at line 386 of file tv_admm.h.

◆ default_max_iterations

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

index_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::default_max_iterations = 10000

staticconstexprprivate

Default maximum number of iterations.

Definition at line 394 of file tv_admm.h.

◆ default_rate_of_cg_tol_rate_to_tol_update_rate

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::default_rate_of_cg_tol_rate_to_tol_update_rate

staticconstexprprivate

Initial value:

=

static_cast<scalar_type>(1e-2)

Default value of the ratio of the rate of tolerance in CG method to the tolerance of update rate of the solution.

Definition at line 407 of file tv_admm.h.

◆ default_tol_update_rate

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

auto num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::default_tol_update_rate

staticconstexprprivate

Initial value:

=

static_cast<scalar_type>(1e-4)

Default tolerance of update rate of the solution.

Definition at line 400 of file tv_admm.h.

◆ derivative_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

data_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::derivative_ {}

private

Derivative.

Definition at line 353 of file tv_admm.h.

◆ derivative_constraint_coeff_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

scalar_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::derivative_constraint_coeff_

private

Initial value:

{

default_derivative_constraint_coeff}

num_collect::regularization::tv_admm::default_derivative_constraint_coeff

static constexpr auto default_derivative_constraint_coeff

Default coefficient of the constraint for the derivative.

Definition tv_admm.h:386

Coefficient of the constraint for the derivative.

Definition at line 390 of file tv_admm.h.

◆ derivative_matrix_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

const derivative_matrix_type* num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::derivative_matrix_ {nullptr}

private

Matrix to compute derivative.

Definition at line 344 of file tv_admm.h.

◆ iterations_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

index_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::iterations_ {}

private

Number of iterations.

Definition at line 350 of file tv_admm.h.

◆ lagrange_multiplier_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

data_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::lagrange_multiplier_ {}

private

Lagrange multiplier.

Definition at line 356 of file tv_admm.h.

◆ lagrange_multiplier_update_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

data_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::lagrange_multiplier_update_ {}

private

Update of lagrange multiplier.

Definition at line 374 of file tv_admm.h.

◆ max_iterations_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

index_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::max_iterations_ {default_max_iterations}

private

Maximum number of iterations.

Definition at line 397 of file tv_admm.h.

◆ previous_derivative_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

data_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::previous_derivative_ {}

private

Previous derivative.

Definition at line 371 of file tv_admm.h.

◆ previous_solution_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

data_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::previous_solution_ {}

private

Previous solution.

Definition at line 368 of file tv_admm.h.

◆ rate_of_cg_tol_rate_to_tol_update_rate_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

scalar_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::rate_of_cg_tol_rate_to_tol_update_rate_

private

Initial value:

{

default_rate_of_cg_tol_rate_to_tol_update_rate}

num_collect::regularization::tgv2_admm::default_rate_of_cg_tol_rate_to_tol_update_rate

static constexpr auto default_rate_of_cg_tol_rate_to_tol_update_rate

Default value of the ratio of the rate of tolerance in CG method to the tolerance of update rate of t...

Definition tgv2_admm.h:675

Rate of the ratio of the rate of tolerance in CG method to the tolerance of update rate of the solution.

Definition at line 411 of file tv_admm.h.

◆ residual_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

data_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::residual_ {}

private

Residual vector.

Definition at line 377 of file tv_admm.h.

◆ temp_data_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

data_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::temp_data_ {}

private

Temporary vector with the size of data.

Definition at line 362 of file tv_admm.h.

◆ temp_derivative_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

data_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::temp_derivative_ {}

private

Temporary vector with the size of the derivative.

Definition at line 365 of file tv_admm.h.

◆ temp_solution_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

data_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::temp_solution_ {}

private

Temporary vector for the update of the solution.

Definition at line 359 of file tv_admm.h.

◆ tol_update_rate_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

scalar_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::tol_update_rate_ {default_tol_update_rate}

private

Tolerance of update rate of the solution.

Definition at line 404 of file tv_admm.h.

◆ update_rate_

template<typename Coeff, typename DerivativeMatrix, base::concepts::dense_vector Data>

scalar_type num_collect::regularization::tv_admm< Coeff, DerivativeMatrix, Data >::update_rate_ {}

private

Rate of norm of the update of the solution in the last iteration.

Definition at line 380 of file tv_admm.h.

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

include/num_collect/regularization/tv_admm.h

Public Types

Public Member Functions

Private Attributes

Static Private Attributes

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ base_type

◆ coeff_type

◆ derivative_matrix_type

◆ this_type

Constructor & Destructor Documentation

◆ tv_admm()

Member Function Documentation

◆ calculate_data_for()

◆ change_data()

◆ compute()

◆ configure_iteration_logger()

◆ data_size()

◆ init()

◆ is_stop_criteria_satisfied()

◆ iterate()

◆ iterations()

◆ max_iterations() [1/2]

◆ max_iterations() [2/2]

◆ param_search_region()

◆ regularization_term()

◆ residual_norm()

◆ residual_norm_rate()

◆ tol_update_rate() [1/2]

◆ tol_update_rate() [2/2]

◆ update_rate()

Member Data Documentation

◆ coeff_

◆ conjugate_gradient_

◆ data_

◆ default_derivative_constraint_coeff

◆ default_max_iterations

◆ default_rate_of_cg_tol_rate_to_tol_update_rate

◆ default_tol_update_rate

◆ derivative_

◆ derivative_constraint_coeff_

◆ derivative_matrix_

◆ iterations_

◆ lagrange_multiplier_

◆ lagrange_multiplier_update_

◆ max_iterations_

◆ previous_derivative_

◆ previous_solution_

◆ rate_of_cg_tol_rate_to_tol_update_rate_

◆ residual_

◆ temp_data_

◆ temp_derivative_

◆ temp_solution_

◆ tol_update_rate_

◆ update_rate_