#include <ginkgo/core/matrix/dense.hpp>

Inheritance diagram for gko::matrix::Dense< ValueType >:

Public Types
using	value_type = ValueType

using	index_type = int64

using	transposed_type = Dense< ValueType >

using	mat_data = matrix_data< ValueType, int64 >

using	mat_data32 = matrix_data< ValueType, int32 >

using	device_mat_data = device_matrix_data< ValueType, int64 >

using	device_mat_data32 = device_matrix_data< ValueType, int32 >

using	absolute_type = remove_complex< Dense >

using	real_type = absolute_type

using	complex_type = to_complex< Dense >

using	row_major_range = gko::range< gko::accessor::row_major< ValueType, 2 > >

Public Types inherited from gko::EnablePolymorphicAssignment< ConcreteLinOp >
using	result_type = ConcreteLinOp

Public Types inherited from gko::ConvertibleTo< ResultType >
using	result_type = ResultType

Public Types inherited from gko::DiagonalExtractable< ValueType >
using	value_type = ValueType

Public Types inherited from gko::ReadableFromMatrixData< ValueType, IndexType >
using	value_type = ValueType

using	index_type = IndexType

Public Types inherited from gko::WritableToMatrixData< ValueType, IndexType >
using	value_type = ValueType

using	index_type = IndexType

Public Types inherited from gko::EnableAbsoluteComputation< AbsoluteLinOp >
using	absolute_type = AbsoluteLinOp

Public Member Functions
void	convert_to (Dense< next_precision< ValueType > > *result) const override

void	move_to (Dense< next_precision< ValueType > > *result) override

void	convert_to (Dense< next_precision< next_precision< ValueType > > > *result) const override

void	move_to (Dense< next_precision< next_precision< ValueType > > > *result) override

void	convert_to (Coo< ValueType, int32 > *result) const override

void	move_to (Coo< ValueType, int32 > *result) override

void	convert_to (Coo< ValueType, int64 > *result) const override

void	move_to (Coo< ValueType, int64 > *result) override

void	convert_to (Csr< ValueType, int32 > *result) const override

void	move_to (Csr< ValueType, int32 > *result) override

void	convert_to (Csr< ValueType, int64 > *result) const override

void	move_to (Csr< ValueType, int64 > *result) override

void	convert_to (Ell< ValueType, int32 > *result) const override

void	move_to (Ell< ValueType, int32 > *result) override

void	convert_to (Ell< ValueType, int64 > *result) const override

void	move_to (Ell< ValueType, int64 > *result) override

void	convert_to (Fbcsr< ValueType, int32 > *result) const override

void	move_to (Fbcsr< ValueType, int32 > *result) override

void	convert_to (Fbcsr< ValueType, int64 > *result) const override

void	move_to (Fbcsr< ValueType, int64 > *result) override

void	convert_to (Hybrid< ValueType, int32 > *result) const override

void	move_to (Hybrid< ValueType, int32 > *result) override

void	convert_to (Hybrid< ValueType, int64 > *result) const override

void	move_to (Hybrid< ValueType, int64 > *result) override

void	convert_to (Sellp< ValueType, int32 > *result) const override

void	move_to (Sellp< ValueType, int32 > *result) override

void	convert_to (Sellp< ValueType, int64 > *result) const override

void	move_to (Sellp< ValueType, int64 > *result) override

void	convert_to (SparsityCsr< ValueType, int32 > *result) const override

void	move_to (SparsityCsr< ValueType, int32 > *result) override

void	convert_to (SparsityCsr< ValueType, int64 > *result) const override

void	move_to (SparsityCsr< ValueType, int64 > *result) override

void	read (const mat_data &data) override

void	read (const mat_data32 &data) override

void	read (const device_mat_data &data) override

void	read (const device_mat_data32 &data) override

void	read (device_mat_data &&data) override

void	read (device_mat_data32 &&data) override

void	write (mat_data &data) const override

void	write (mat_data32 &data) const override

std::unique_ptr< LinOp >	transpose () const override

std::unique_ptr< LinOp >	conj_transpose () const override

void	transpose (ptr_param< Dense > output) const

void	conj_transpose (ptr_param< Dense > output) const

void	fill (const ValueType value)

std::unique_ptr< Dense >	permute (ptr_param< const Permutation< int32 > > permutation, permute_mode mode=permute_mode::symmetric) const

std::unique_ptr< Dense >	permute (ptr_param< const Permutation< int64 > > permutation, permute_mode mode=permute_mode::symmetric) const

void	permute (ptr_param< const Permutation< int32 > > permutation, ptr_param< Dense > output, permute_mode mode) const

void	permute (ptr_param< const Permutation< int64 > > permutation, ptr_param< Dense > output, permute_mode mode) const

std::unique_ptr< Dense >	permute (ptr_param< const Permutation< int32 > > row_permutation, ptr_param< const Permutation< int32 > > column_permutation, bool invert=false) const

std::unique_ptr< Dense >	permute (ptr_param< const Permutation< int64 > > row_permutation, ptr_param< const Permutation< int64 > > column_permutation, bool invert=false) const

void	permute (ptr_param< const Permutation< int32 > > row_permutation, ptr_param< const Permutation< int32 > > column_permutation, ptr_param< Dense > output, bool invert=false) const

void	permute (ptr_param< const Permutation< int64 > > row_permutation, ptr_param< const Permutation< int64 > > column_permutation, ptr_param< Dense > output, bool invert=false) const

std::unique_ptr< Dense >	scale_permute (ptr_param< const ScaledPermutation< value_type, int32 > > permutation, permute_mode mode=permute_mode::symmetric) const

std::unique_ptr< Dense >	scale_permute (ptr_param< const ScaledPermutation< value_type, int64 > > permutation, permute_mode mode=permute_mode::symmetric) const

void	scale_permute (ptr_param< const ScaledPermutation< value_type, int32 > > permutation, ptr_param< Dense > output, permute_mode mode) const

void	scale_permute (ptr_param< const ScaledPermutation< value_type, int64 > > permutation, ptr_param< Dense > output, permute_mode mode) const

std::unique_ptr< Dense >	scale_permute (ptr_param< const ScaledPermutation< value_type, int32 > > row_permutation, ptr_param< const ScaledPermutation< value_type, int32 > > column_permutation, bool invert=false) const

std::unique_ptr< Dense >	scale_permute (ptr_param< const ScaledPermutation< value_type, int64 > > row_permutation, ptr_param< const ScaledPermutation< value_type, int64 > > column_permutation, bool invert=false) const

void	scale_permute (ptr_param< const ScaledPermutation< value_type, int32 > > row_permutation, ptr_param< const ScaledPermutation< value_type, int32 > > column_permutation, ptr_param< Dense > output, bool invert=false) const

void	scale_permute (ptr_param< const ScaledPermutation< value_type, int64 > > row_permutation, ptr_param< const ScaledPermutation< value_type, int64 > > column_permutation, ptr_param< Dense > output, bool invert=false) const

std::unique_ptr< LinOp >	permute (const array< int32 > *permutation_indices) const override

std::unique_ptr< LinOp >	permute (const array< int64 > *permutation_indices) const override

void	permute (const array< int32 > *permutation_indices, ptr_param< Dense > output) const

void	permute (const array< int64 > *permutation_indices, ptr_param< Dense > output) const

std::unique_ptr< LinOp >	inverse_permute (const array< int32 > *permutation_indices) const override

std::unique_ptr< LinOp >	inverse_permute (const array< int64 > *permutation_indices) const override

void	inverse_permute (const array< int32 > *permutation_indices, ptr_param< Dense > output) const

void	inverse_permute (const array< int64 > *permutation_indices, ptr_param< Dense > output) const

std::unique_ptr< LinOp >	row_permute (const array< int32 > *permutation_indices) const override

std::unique_ptr< LinOp >	row_permute (const array< int64 > *permutation_indices) const override

void	row_permute (const array< int32 > *permutation_indices, ptr_param< Dense > output) const

void	row_permute (const array< int64 > *permutation_indices, ptr_param< Dense > output) const

std::unique_ptr< Dense >	row_gather (const array< int32 > *gather_indices) const

std::unique_ptr< Dense >	row_gather (const array< int64 > *gather_indices) const

void	row_gather (const array< int32 > *gather_indices, ptr_param< LinOp > row_collection) const

void	row_gather (const array< int64 > *gather_indices, ptr_param< LinOp > row_collection) const

void	row_gather (ptr_param< const LinOp > alpha, const array< int32 > *gather_indices, ptr_param< const LinOp > beta, ptr_param< LinOp > row_collection) const

void	row_gather (ptr_param< const LinOp > alpha, const array< int64 > *gather_indices, ptr_param< const LinOp > beta, ptr_param< LinOp > row_collection) const

std::unique_ptr< LinOp >	column_permute (const array< int32 > *permutation_indices) const override

std::unique_ptr< LinOp >	column_permute (const array< int64 > *permutation_indices) const override

void	column_permute (const array< int32 > *permutation_indices, ptr_param< Dense > output) const

void	column_permute (const array< int64 > *permutation_indices, ptr_param< Dense > output) const

std::unique_ptr< LinOp >	inverse_row_permute (const array< int32 > *permutation_indices) const override

std::unique_ptr< LinOp >	inverse_row_permute (const array< int64 > *permutation_indices) const override

void	inverse_row_permute (const array< int32 > *permutation_indices, ptr_param< Dense > output) const

void	inverse_row_permute (const array< int64 > *permutation_indices, ptr_param< Dense > output) const

std::unique_ptr< LinOp >	inverse_column_permute (const array< int32 > *permutation_indices) const override

std::unique_ptr< LinOp >	inverse_column_permute (const array< int64 > *permutation_indices) const override

void	inverse_column_permute (const array< int32 > *permutation_indices, ptr_param< Dense > output) const

void	inverse_column_permute (const array< int64 > *permutation_indices, ptr_param< Dense > output) const

std::unique_ptr< Diagonal< ValueType > >	extract_diagonal () const override

void	extract_diagonal (ptr_param< Diagonal< ValueType > > output) const

std::unique_ptr< absolute_type >	compute_absolute () const override

void	compute_absolute (ptr_param< absolute_type > output) const

void	compute_absolute_inplace () override

std::unique_ptr< complex_type >	make_complex () const

void	make_complex (ptr_param< complex_type > result) const

std::unique_ptr< real_type >	get_real () const

void	get_real (ptr_param< real_type > result) const

std::unique_ptr< real_type >	get_imag () const

void	get_imag (ptr_param< real_type > result) const

value_type *	get_values () noexcept

const value_type *	get_const_values () const noexcept

size_type	get_stride () const noexcept

size_type	get_num_stored_elements () const noexcept

value_type &	at (size_type row, size_type col) noexcept

value_type	at (size_type row, size_type col) const noexcept

ValueType &	at (size_type idx) noexcept

ValueType	at (size_type idx) const noexcept

void	scale (ptr_param< const LinOp > alpha)

void	inv_scale (ptr_param< const LinOp > alpha)

void	add_scaled (ptr_param< const LinOp > alpha, ptr_param< const LinOp > b)

void	sub_scaled (ptr_param< const LinOp > alpha, ptr_param< const LinOp > b)

void	compute_dot (ptr_param< const LinOp > b, ptr_param< LinOp > result) const

void	compute_dot (ptr_param< const LinOp > b, ptr_param< LinOp > result, array< char > &tmp) const

void	compute_conj_dot (ptr_param< const LinOp > b, ptr_param< LinOp > result) const

void	compute_conj_dot (ptr_param< const LinOp > b, ptr_param< LinOp > result, array< char > &tmp) const

void	compute_norm2 (ptr_param< LinOp > result) const

void	compute_norm2 (ptr_param< LinOp > result, array< char > &tmp) const

void	compute_norm1 (ptr_param< LinOp > result) const

void	compute_norm1 (ptr_param< LinOp > result, array< char > &tmp) const

void	compute_squared_norm2 (ptr_param< LinOp > result) const

void	compute_squared_norm2 (ptr_param< LinOp > result, array< char > &tmp) const

void	compute_mean (ptr_param< LinOp > result) const

void	compute_mean (ptr_param< LinOp > result, array< char > &tmp) const

std::unique_ptr< Dense >	create_submatrix (const span &rows, const span &columns, const size_type stride)

std::unique_ptr< Dense >	create_submatrix (const span &rows, const span &columns)

std::unique_ptr< real_type >	create_real_view ()

std::unique_ptr< const real_type >	create_real_view () const

Dense &	operator= (const Dense &)

Dense &	operator= (Dense &&)

	Dense (const Dense &)

	Dense (Dense &&)

Public Member Functions inherited from gko::EnableLinOp< ConcreteLinOp, PolymorphicBase >
const ConcreteLinOp *	apply (ptr_param< const LinOp > b, ptr_param< LinOp > x) const

ConcreteLinOp *	apply (ptr_param< const LinOp > b, ptr_param< LinOp > x)

const ConcreteLinOp *	apply (ptr_param< const LinOp > alpha, ptr_param< const LinOp > b, ptr_param< const LinOp > beta, ptr_param< LinOp > x) const

ConcreteLinOp *	apply (ptr_param< const LinOp > alpha, ptr_param< const LinOp > b, ptr_param< const LinOp > beta, ptr_param< LinOp > x)

Public Member Functions inherited from gko::EnableAbstractPolymorphicObject< AbstractObject, PolymorphicBase >
std::unique_ptr< AbstractObject >	create_default (std::shared_ptr< const Executor > exec) const

std::unique_ptr< AbstractObject >	create_default () const

std::unique_ptr< AbstractObject >	clone (std::shared_ptr< const Executor > exec) const

std::unique_ptr< AbstractObject >	clone () const

AbstractObject *	copy_from (const PolymorphicObject *other)

template<typename Derived >
std::enable_if_t< std::is_base_of< PolymorphicObject, std::decay_t< Derived > >::value, AbstractObject > *	copy_from (std::unique_ptr< Derived > &&other)

template<typename Derived >
std::enable_if_t< std::is_base_of< PolymorphicObject, std::decay_t< Derived > >::value, AbstractObject > *	copy_from (const std::unique_ptr< Derived > &other)

AbstractObject *	copy_from (const std::shared_ptr< const PolymorphicObject > &other)

AbstractObject *	move_from (ptr_param< PolymorphicObject > other)

AbstractObject *	clear ()

Public Member Functions inherited from gko::PolymorphicObject
PolymorphicObject &	operator= (const PolymorphicObject &)

std::unique_ptr< PolymorphicObject >	create_default (std::shared_ptr< const Executor > exec) const

std::unique_ptr< PolymorphicObject >	create_default () const

std::unique_ptr< PolymorphicObject >	clone (std::shared_ptr< const Executor > exec) const

std::unique_ptr< PolymorphicObject >	clone () const

PolymorphicObject *	copy_from (const PolymorphicObject *other)

template<typename Derived , typename Deleter >
std::enable_if_t< std::is_base_of< PolymorphicObject, std::decay_t< Derived > >::value, PolymorphicObject > *	copy_from (std::unique_ptr< Derived, Deleter > &&other)

template<typename Derived , typename Deleter >
std::enable_if_t< std::is_base_of< PolymorphicObject, std::decay_t< Derived > >::value, PolymorphicObject > *	copy_from (const std::unique_ptr< Derived, Deleter > &other)

PolymorphicObject *	copy_from (const std::shared_ptr< const PolymorphicObject > &other)

PolymorphicObject *	move_from (ptr_param< PolymorphicObject > other)

PolymorphicObject *	clear ()

std::shared_ptr< const Executor >	get_executor () const noexcept

Public Member Functions inherited from gko::log::EnableLogging< PolymorphicObject >
void	add_logger (std::shared_ptr< const Logger > logger) override

void	remove_logger (const Logger *logger) override

void	remove_logger (ptr_param< const Logger > logger)

const std::vector< std::shared_ptr< const Logger > > &	get_loggers () const override

void	clear_loggers () override

Public Member Functions inherited from gko::log::Loggable
void	remove_logger (ptr_param< const Logger > logger)

Public Member Functions inherited from gko::EnablePolymorphicAssignment< ConcreteLinOp >
void	convert_to (result_type *result) const override

void	move_to (result_type *result) override

Public Member Functions inherited from gko::ConvertibleTo< ResultType >
virtual void	convert_to (result_type *result) const =0

void	convert_to (ptr_param< result_type > result) const

virtual void	move_to (result_type *result)=0

void	move_to (ptr_param< result_type > result)

Public Member Functions inherited from gko::DiagonalExtractable< ValueType >
std::unique_ptr< LinOp >	extract_diagonal_linop () const override

Public Member Functions inherited from gko::ReadableFromMatrixData< ValueType, IndexType >
virtual void	read (const matrix_data< ValueType, IndexType > &data)=0

void	read (const matrix_assembly_data< ValueType, IndexType > &data)

virtual void	read (const device_matrix_data< ValueType, IndexType > &data)

virtual void	read (device_matrix_data< ValueType, IndexType > &&data)

Public Member Functions inherited from gko::WritableToMatrixData< ValueType, IndexType >
virtual void	write (matrix_data< ValueType, IndexType > &data) const =0

Public Member Functions inherited from gko::EnableAbsoluteComputation< AbsoluteLinOp >
std::unique_ptr< LinOp >	compute_absolute_linop () const override

Public Member Functions inherited from gko::ScaledIdentityAddable
void	add_scaled_identity (ptr_param< const LinOp > const a, ptr_param< const LinOp > const b)

Static Public Member Functions
static std::unique_ptr< Dense >	create_with_config_of (ptr_param< const Dense > other)

static std::unique_ptr< Dense >	create_with_type_of (ptr_param< const Dense > other, std::shared_ptr< const Executor > exec, const dim< 2 > &size=dim< 2 >{})

static std::unique_ptr< Dense >	create_with_type_of (ptr_param< const Dense > other, std::shared_ptr< const Executor > exec, const dim< 2 > &size, size_type stride)

static std::unique_ptr< Dense >	create_with_type_of (ptr_param< const Dense > other, std::shared_ptr< const Executor > exec, const dim< 2 > &size, const dim< 2 > &local_size, size_type stride)

static std::unique_ptr< Dense >	create_view_of (ptr_param< Dense > other)

static std::unique_ptr< const Dense >	create_const_view_of (ptr_param< const Dense > other)

static std::unique_ptr< Dense >	create (std::shared_ptr< const Executor > exec, const dim< 2 > &size={}, size_type stride=0)

static std::unique_ptr< Dense >	create (std::shared_ptr< const Executor > exec, const dim< 2 > &size, array< value_type > values, size_type stride)

template<typename InputValueType >
static std::unique_ptr< Dense >	create (std::shared_ptr< const Executor > exec, const dim< 2 > &size, std::initializer_list< InputValueType > values, size_type stride)
	create(std::shared_ptr<const Executor>,const dim<2>&, array<value_type>, size_type)

static std::unique_ptr< const Dense >	create_const (std::shared_ptr< const Executor > exec, const dim< 2 > &size, gko::detail::const_array_view< ValueType > &&values, size_type stride)

Protected Member Functions
	Dense (std::shared_ptr< const Executor > exec, const dim< 2 > &size={}, size_type stride=0)

	Dense (std::shared_ptr< const Executor > exec, const dim< 2 > &size, array< value_type > values, size_type stride)

virtual std::unique_ptr< Dense >	create_with_same_config () const

virtual std::unique_ptr< Dense >	create_with_type_of_impl (std::shared_ptr< const Executor > exec, const dim< 2 > &size, size_type stride) const

virtual std::unique_ptr< Dense >	create_view_of_impl ()

virtual std::unique_ptr< const Dense >	create_const_view_of_impl () const

template<typename IndexType >
void	convert_impl (Coo< ValueType, IndexType > *result) const

template<typename IndexType >
void	convert_impl (Csr< ValueType, IndexType > *result) const

template<typename IndexType >
void	convert_impl (Ell< ValueType, IndexType > *result) const

template<typename IndexType >
void	convert_impl (Fbcsr< ValueType, IndexType > *result) const

template<typename IndexType >
void	convert_impl (Hybrid< ValueType, IndexType > *result) const

template<typename IndexType >
void	convert_impl (Sellp< ValueType, IndexType > *result) const

template<typename IndexType >
void	convert_impl (SparsityCsr< ValueType, IndexType > *result) const

virtual void	scale_impl (const LinOp *alpha)

virtual void	inv_scale_impl (const LinOp *alpha)

virtual void	add_scaled_impl (const LinOp alpha, const LinOp b)

virtual void	sub_scaled_impl (const LinOp alpha, const LinOp b)

virtual void	compute_dot_impl (const LinOp b, LinOp result) const

virtual void	compute_conj_dot_impl (const LinOp b, LinOp result) const

virtual void	compute_norm2_impl (LinOp *result) const

virtual void	compute_norm1_impl (LinOp *result) const

virtual void	compute_squared_norm2_impl (LinOp *result) const

virtual void	compute_mean_impl (LinOp *result) const

void	resize (gko::dim< 2 > new_size)

virtual std::unique_ptr< Dense >	create_submatrix_impl (const span &rows, const span &columns, const size_type stride)

void	apply_impl (const LinOp b, LinOp x) const override

void	apply_impl (const LinOp alpha, const LinOp b, const LinOp beta, LinOp x) const override

size_type	linearize_index (size_type row, size_type col) const noexcept

size_type	linearize_index (size_type idx) const noexcept

template<typename IndexType >
void	permute_impl (const Permutation< IndexType > permutation, permute_mode mode, Dense output) const

template<typename IndexType >
void	permute_impl (const Permutation< IndexType > row_permutation, const Permutation< IndexType > col_permutation, bool invert, Dense *output) const

template<typename IndexType >
void	scale_permute_impl (const ScaledPermutation< ValueType, IndexType > permutation, permute_mode mode, Dense output) const

template<typename IndexType >
void	scale_permute_impl (const ScaledPermutation< ValueType, IndexType > row_permutation, const ScaledPermutation< ValueType, IndexType > column_permutation, bool invert, Dense *output) const

template<typename OutputType , typename IndexType >
void	row_gather_impl (const array< IndexType > row_idxs, Dense< OutputType > row_collection) const

template<typename OutputType , typename IndexType >
void	row_gather_impl (const Dense< ValueType > alpha, const array< IndexType > row_idxs, const Dense< ValueType > beta, Dense< OutputType > row_collection) const

Protected Member Functions inherited from gko::EnableLinOp< ConcreteLinOp, PolymorphicBase >
ConcreteLinOp *	self () noexcept

const ConcreteLinOp *	self () const noexcept

Protected Member Functions inherited from gko::EnablePolymorphicObject< ConcreteObject, PolymorphicBase >
std::unique_ptr< PolymorphicObject >	create_default_impl (std::shared_ptr< const Executor > exec) const override

PolymorphicObject *	copy_from_impl (const PolymorphicObject *other) override

PolymorphicObject *	copy_from_impl (std::unique_ptr< PolymorphicObject > other) override

PolymorphicObject *	move_from_impl (PolymorphicObject *other) override

PolymorphicObject *	move_from_impl (std::unique_ptr< PolymorphicObject > other) override

PolymorphicObject *	clear_impl () override

Protected Member Functions inherited from gko::PolymorphicObject
	PolymorphicObject (std::shared_ptr< const Executor > exec)

	PolymorphicObject (const PolymorphicObject &other)

Protected Member Functions inherited from gko::log::EnableLogging< PolymorphicObject >
void	log (Params &&... params) const

Friends
class	EnablePolymorphicObject< Dense, LinOp >

class	Coo< ValueType, int32 >

class	Coo< ValueType, int64 >

class	Csr< ValueType, int32 >

class	Csr< ValueType, int64 >

class	Diagonal< ValueType >

class	Ell< ValueType, int32 >

class	Ell< ValueType, int64 >

class	Fbcsr< ValueType, int32 >

class	Fbcsr< ValueType, int64 >

class	Hybrid< ValueType, int32 >

class	Hybrid< ValueType, int64 >

class	Sellp< ValueType, int32 >

class	Sellp< ValueType, int64 >

class	SparsityCsr< ValueType, int32 >

class	SparsityCsr< ValueType, int64 >

class	Dense< to_complex< ValueType > >

class	experimental::distributed::Vector< ValueType >

class	Dense< previous_precision< ValueType > >

class	Dense< previous_precision< previous_precision< ValueType > > >

Additional Inherited Members
Protected Attributes inherited from gko::log::EnableLogging< PolymorphicObject >
std::vector< std::shared_ptr< const Logger > >	loggers_

Detailed Description

template<typename ValueType = default_precision>
class gko::matrix::Dense< ValueType >

Dense is a matrix format which explicitly stores all values of the matrix.

The values are stored in row-major format (values belonging to the same row appear consecutive in the memory). Optionally, rows can be padded for better memory access.

Template Parameters

ValueType precision of matrix elements

Note: While this format is not very useful for storing sparse matrices, it is often suitable to store vectors, and sets of vectors.

Constructor & Destructor Documentation

◆ Dense() [1/2]

template<typename ValueType = default_precision>

gko::matrix::Dense< ValueType >::Dense ( const Dense< ValueType > & )

Copy-constructs a Dense matrix. Inherits executor and dimensions, but copies data without padding.

◆ Dense() [2/2]

template<typename ValueType = default_precision>

gko::matrix::Dense< ValueType >::Dense ( Dense< ValueType > && )

Move-constructs a Dense matrix. Inherits executor, dimensions and data with padding. The moved-from object is empty (0x0 with empty Array).

Member Function Documentation

◆ add_scaled()

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::add_scaled	(	ptr_param< const LinOp >	alpha,
		ptr_param< const LinOp >	b
	)

Adds b scaled by alpha to the matrix (aka: BLAS axpy).

Parameters

alpha	If alpha is 1x1 Dense matrix, the entire matrix is scaled by alpha. If it is a Dense row vector of values, then i-th column of the matrix is scaled with the i-th element of alpha (the number of columns of alpha has to match the number of columns of the matrix).
b	a matrix of the same dimension as this

◆ add_scaled_impl()

template<typename ValueType = default_precision>

virtual void gko::matrix::Dense< ValueType >::add_scaled_impl	(	const LinOp *	alpha,
		const LinOp *	b
	)

protectedvirtual

◆ at() [1/4]

template<typename ValueType = default_precision>

ValueType gko::matrix::Dense< ValueType >::at ( size_type idx ) const

inlinenoexcept

Returns a single element of the matrix.

Useful for iterating across all elements of the matrix. However, it is less efficient than the two-parameter variant of this method.

Parameters

idx	a linear index of the requested element (ignoring the stride)

Note: the method has to be called on the same Executor the matrix is stored at (e.g. trying to call this method on a GPU matrix from the OMP results in a runtime error)

References gko::array< ValueType >::get_const_data().

◆ at() [2/4]

template<typename ValueType = default_precision>

ValueType & gko::matrix::Dense< ValueType >::at ( size_type idx )

inlinenoexcept

Returns a single element of the matrix.

Useful for iterating across all elements of the matrix. However, it is less efficient than the two-parameter variant of this method.

Parameters

idx	a linear index of the requested element (ignoring the stride)

Note: the method has to be called on the same Executor the matrix is stored at (e.g. trying to call this method on a GPU matrix from the OMP results in a runtime error)

References gko::array< ValueType >::get_data().

◆ at() [3/4]

template<typename ValueType = default_precision>

value_type gko::matrix::Dense< ValueType >::at	(	size_type	row,
		size_type	col
	)		const

inlinenoexcept

Returns a single element of the matrix.

Parameters

row	the row of the requested element
col	the column of the requested element

Note: the method has to be called on the same Executor the matrix is stored at (e.g. trying to call this method on a GPU matrix from the OMP results in a runtime error)

References gko::array< ValueType >::get_const_data().

◆ at() [4/4]

template<typename ValueType = default_precision>

value_type & gko::matrix::Dense< ValueType >::at	(	size_type	row,
		size_type	col
	)

inlinenoexcept

Returns a single element of the matrix.

Parameters

row	the row of the requested element
col	the column of the requested element

Note: the method has to be called on the same Executor the matrix is stored at (e.g. trying to call this method on a GPU matrix from the OMP results in a runtime error)

References gko::array< ValueType >::get_data().

◆ column_permute() [1/4]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::column_permute ( const array< int32 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the column permutation of the Permutable object. In the resulting LinOp, the column i contains the input column perm[i].

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(A P^T\).

Parameters

permutation_indices the array of indices containing the permutation order perm.

Returns: a pointer to the new column permuted object

Implements gko::Permutable< int32 >.

◆ column_permute() [2/4]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::column_permute	(	const array< int32 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

Writes the column-permuted matrix into the given output matrix.

Parameters

permutation_indices	The array containing permutation indices. It must have `this->get_size()[1]` elements.
output	The output matrix. It must have the dimensions `this->get_size()`

See also: Dense::column_permute(const array<int32>*)

◆ column_permute() [3/4]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::column_permute ( const array< int64 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the column permutation of the Permutable object. In the resulting LinOp, the column i contains the input column perm[i].

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(A P^T\).

Parameters

permutation_indices the array of indices containing the permutation order perm.

Returns: a pointer to the new column permuted object

Implements gko::Permutable< int64 >.

◆ column_permute() [4/4]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::column_permute	(	const array< int64 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

◆ compute_absolute() [1/2]

template<typename ValueType = default_precision>

std::unique_ptr< absolute_type > gko::matrix::Dense< ValueType >::compute_absolute ( ) const

overridevirtual

Gets the AbsoluteLinOp

Returns: a pointer to the new absolute object

Implements gko::EnableAbsoluteComputation< AbsoluteLinOp >.

◆ compute_absolute() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_absolute ( ptr_param< absolute_type > output ) const

Writes the absolute values of this matrix into an existing matrix.

Parameters

output The output matrix. Its size must match the size of this matrix.

See also: Dense::compute_absolute()

◆ compute_absolute_inplace()

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_absolute_inplace ( )

overridevirtual

Compute absolute inplace on each element.

Implements gko::AbsoluteComputable.

◆ compute_conj_dot() [1/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_conj_dot	(	ptr_param< const LinOp >	b,
		ptr_param< LinOp >	result
	)		const

Computes the column-wise dot product of conj(this matrix) and b.

Parameters

b	a Dense matrix of same dimension as this
result	a Dense row vector, used to store the dot product (the number of column in the vector must match the number of columns of this)

◆ compute_conj_dot() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_conj_dot	(	ptr_param< const LinOp >	b,
		ptr_param< LinOp >	result,
		array< char > &	tmp
	)		const

Computes the column-wise dot product of conj(this matrix) and b.

Parameters

b	a Dense matrix of same dimension as this
result	a Dense row vector, used to store the dot product (the number of column in the vector must match the number of columns of this)
tmp	the temporary storage to use for partial sums during the reduction computation. It may be resized and/or reset to the correct executor.

◆ compute_conj_dot_impl()

template<typename ValueType = default_precision>

virtual void gko::matrix::Dense< ValueType >::compute_conj_dot_impl	(	const LinOp *	b,
		LinOp *	result
	)		const

protectedvirtual

◆ compute_dot() [1/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_dot	(	ptr_param< const LinOp >	b,
		ptr_param< LinOp >	result
	)		const

Computes the column-wise dot product of this matrix and b.

Parameters

b	a Dense matrix of same dimension as this
result	a Dense row vector, used to store the dot product (the number of column in the vector must match the number of columns of this)

◆ compute_dot() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_dot	(	ptr_param< const LinOp >	b,
		ptr_param< LinOp >	result,
		array< char > &	tmp
	)		const

Computes the column-wise dot product of this matrix and b.

Parameters

b	a Dense matrix of same dimension as this
result	a Dense row vector, used to store the dot product (the number of column in the vector must match the number of columns of this)
tmp	the temporary storage to use for partial sums during the reduction computation. It may be resized and/or reset to the correct executor.

◆ compute_dot_impl()

template<typename ValueType = default_precision>

virtual void gko::matrix::Dense< ValueType >::compute_dot_impl	(	const LinOp *	b,
		LinOp *	result
	)		const

protectedvirtual

◆ compute_mean() [1/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_mean ( ptr_param< LinOp > result ) const

Computes the column-wise arithmetic mean of this matrix.

Parameters

result a Dense row vector, used to store the mean (the number of columns in the vector must match the number of columns of this)

◆ compute_mean() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_mean	(	ptr_param< LinOp >	result,
		array< char > &	tmp
	)		const

Computes the column-wise arithmetic mean of this matrix.

Parameters

result	a Dense row vector, used to store the mean (the number of columns in the vector must match the number of columns of this)
tmp	the temporary storage to use for partial sums during the reduction computation. It may be resized and/or reset to the correct executor.

◆ compute_mean_impl()

template<typename ValueType = default_precision>

virtual void gko::matrix::Dense< ValueType >::compute_mean_impl ( LinOp * result ) const

protectedvirtual

◆ compute_norm1() [1/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_norm1 ( ptr_param< LinOp > result ) const

Computes the column-wise (L^1) norm of this matrix.

Parameters

result a Dense row vector, used to store the norm (the number of columns in the vector must match the number of columns of this)

◆ compute_norm1() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_norm1	(	ptr_param< LinOp >	result,
		array< char > &	tmp
	)		const

Computes the column-wise (L^1) norm of this matrix.

Parameters

result	a Dense row vector, used to store the norm (the number of columns in the vector must match the number of columns of this)
tmp	the temporary storage to use for partial sums during the reduction computation. It may be resized and/or reset to the correct executor.

◆ compute_norm1_impl()

template<typename ValueType = default_precision>

virtual void gko::matrix::Dense< ValueType >::compute_norm1_impl ( LinOp * result ) const

protectedvirtual

◆ compute_norm2() [1/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_norm2 ( ptr_param< LinOp > result ) const

Computes the column-wise Euclidean (L^2) norm of this matrix.

Parameters

result a Dense row vector, used to store the norm (the number of columns in the vector must match the number of columns of this)

◆ compute_norm2() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_norm2	(	ptr_param< LinOp >	result,
		array< char > &	tmp
	)		const

Computes the column-wise Euclidean (L^2) norm of this matrix.

Parameters

result	a Dense row vector, used to store the norm (the number of columns in the vector must match the number of columns of this)
tmp	the temporary storage to use for partial sums during the reduction computation. It may be resized and/or reset to the correct executor.

◆ compute_norm2_impl()

template<typename ValueType = default_precision>

virtual void gko::matrix::Dense< ValueType >::compute_norm2_impl ( LinOp * result ) const

protectedvirtual

◆ compute_squared_norm2() [1/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_squared_norm2 ( ptr_param< LinOp > result ) const

Computes the square of the column-wise Euclidean (L^2) norm of this matrix.

Parameters

result a Dense row vector, used to store the norm (the number of columns in the vector must match the number of columns of this)

◆ compute_squared_norm2() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::compute_squared_norm2	(	ptr_param< LinOp >	result,
		array< char > &	tmp
	)		const

Computes the square of the column-wise Euclidean (L^2) norm of this matrix.

Parameters

result	a Dense row vector, used to store the norm (the number of columns in the vector must match the number of columns of this)
tmp	the temporary storage to use for partial sums during the reduction computation. It may be resized and/or reset to the correct executor.

◆ compute_squared_norm2_impl()

template<typename ValueType = default_precision>

virtual void gko::matrix::Dense< ValueType >::compute_squared_norm2_impl ( LinOp * result ) const

protectedvirtual

◆ conj_transpose() [1/2]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::conj_transpose ( ) const

overridevirtual

Returns a LinOp representing the conjugate transpose of the Transposable object.

Returns: a pointer to the new conjugate transposed object

Implements gko::Transposable.

◆ conj_transpose() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::conj_transpose ( ptr_param< Dense< ValueType > > output ) const

Writes the conjugate-transposed matrix into the given output matrix.

Parameters

output The output matrix. It must have the dimensions gko::transpose(this->get_size())

◆ create() [1/3]

template<typename ValueType = default_precision>

static std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create	(	std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size,
		array< value_type >	values,
		size_type	stride
	)

static

Creates a Dense matrix from an already allocated (and initialized) array.

Parameters

exec	Executor associated to the matrix
size	size of the matrix
values	array of matrix values
stride	stride of the rows (i.e. offset between the first elements of two consecutive rows, expressed as the number of matrix elements)

Note: If values is not an rvalue, not an array of ValueType, or is on the wrong executor, an internal copy will be created, and the original array data will not be used in the matrix.

Returns: A smart pointer to the newly created matrix.

◆ create() [2/3]

template<typename ValueType = default_precision>

template<typename InputValueType >

static std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create	(	std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size,
		std::initializer_list< InputValueType >	values,
		size_type	stride
	)

inlinestatic

create(std::shared_ptr<const Executor>,const dim<2>&, array<value_type>, size_type)

References gko::matrix::Dense< ValueType >::create().

◆ create() [3/3]

template<typename ValueType = default_precision>

static std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create	(	std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size = `{}`,
		size_type	stride = `0`
	)

static

Creates an uninitialized Dense matrix of the specified size.

Parameters

exec	Executor associated to the matrix
size	size of the matrix
stride	stride of the rows (i.e. offset between the first elements of two consecutive rows, expressed as the number of matrix elements). If it is set to 0, size[1] will be used instead.

Returns: A smart pointer to the newly created matrix.

Referenced by gko::matrix::Dense< ValueType >::create(), gko::matrix::Dense< ValueType >::create_view_of_impl(), gko::matrix::Dense< ValueType >::create_with_same_config(), gko::matrix::Dense< ValueType >::create_with_type_of_impl(), gko::preconditioner::Ic< LSolverType, IndexType >::set_cache_to(), gko::preconditioner::Ilu< LSolverType, USolverType, ReverseApply, IndexType >::set_cache_to(), and gko::experimental::reorder::ScaledReordered< ValueType, IndexType >::set_cache_to().

◆ create_const()

template<typename ValueType = default_precision>

static std::unique_ptr< const Dense > gko::matrix::Dense< ValueType >::create_const	(	std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size,
		gko::detail::const_array_view< ValueType > &&	values,
		size_type	stride
	)

static

Creates a constant (immutable) Dense matrix from a constant array.

Parameters

exec	the executor to create the matrix on
size	the dimensions of the matrix
values	the value array of the matrix
stride	the row-stride of the matrix

Returns: A smart pointer to the constant matrix wrapping the input array (if it resides on the same executor as the matrix) or a copy of the array on the correct executor.

Referenced by gko::matrix::Dense< ValueType >::create_const_view_of_impl().

◆ create_const_view_of()

template<typename ValueType = default_precision>

static std::unique_ptr< const Dense > gko::matrix::Dense< ValueType >::create_const_view_of ( ptr_param< const Dense< ValueType > > other )

inlinestatic

Creates a immutable Dense matrix, where the underlying array is a view of another Dense matrix' array.

Parameters

other The other matrix on which to create the view

Returns: A immutable Dense matrix that is a view of other

Referenced by gko::make_const_dense_view().

◆ create_const_view_of_impl()

template<typename ValueType = default_precision>

virtual std::unique_ptr< const Dense > gko::matrix::Dense< ValueType >::create_const_view_of_impl ( ) const

inlineprotectedvirtual

Creates a immutable Dense matrix where the underlying array is a view of this' array.

Returns: A immutable Dense matrix that is a view of this.

References gko::matrix::Dense< ValueType >::create_const(), gko::matrix::Dense< ValueType >::get_const_values(), gko::PolymorphicObject::get_executor(), gko::matrix::Dense< ValueType >::get_num_stored_elements(), gko::matrix::Dense< ValueType >::get_stride(), and gko::make_const_array_view().

◆ create_real_view() [1/2]

template<typename ValueType = default_precision>

std::unique_ptr< real_type > gko::matrix::Dense< ValueType >::create_real_view ( )

Create a real view of the (potentially) complex original matrix. If the original matrix is real, nothing changes. If the original matrix is complex, the result is created by viewing the complex matrix with as real with a reinterpret_cast with twice the number of columns and double the stride.

◆ create_real_view() [2/2]

template<typename ValueType = default_precision>

std::unique_ptr< const real_type > gko::matrix::Dense< ValueType >::create_real_view ( ) const

Create a real view of the (potentially) complex original matrix. If the original matrix is real, nothing changes. If the original matrix is complex, the result is created by viewing the complex matrix with as real with a reinterpret_cast with twice the number of columns and double the stride.

◆ create_submatrix() [1/2]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_submatrix	(	const span &	rows,
		const span &	columns
	)

inline

Create a submatrix from the original matrix.

Parameters

rows	row span
columns	column span

References gko::matrix::columns, gko::matrix::Dense< ValueType >::create_submatrix(), gko::matrix::Dense< ValueType >::get_stride(), and gko::matrix::rows.

◆ create_submatrix() [2/2]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_submatrix	(	const span &	rows,
		const span &	columns,
		const size_type	stride
	)

inline

Create a submatrix from the original matrix. Warning: defining stride for this create_submatrix method might cause wrong memory access. Better use the create_submatrix(rows, columns) method instead.

Parameters

rows	row span
columns	column span
stride	stride of the new submatrix.

References gko::matrix::columns, and gko::matrix::Dense< ValueType >::create_submatrix_impl().

Referenced by gko::matrix::Dense< ValueType >::create_submatrix().

◆ create_submatrix_impl()

template<typename ValueType = default_precision>

virtual std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_submatrix_impl	(	const span &	rows,
		const span &	columns,
		const size_type	stride
	)

protectedvirtual

Note: Other implementations of dense should override this function instead of create_submatrix(const span, const span, const size_type).

Referenced by gko::matrix::Dense< ValueType >::create_submatrix().

◆ create_view_of()

template<typename ValueType = default_precision>

static std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_view_of ( ptr_param< Dense< ValueType > > other )

inlinestatic

Creates a Dense matrix, where the underlying array is a view of another Dense matrix' array.

Parameters

other The other matrix on which to create the view

Returns: A Dense matrix that is a view of other

Referenced by gko::make_dense_view().

◆ create_view_of_impl()

template<typename ValueType = default_precision>

virtual std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_view_of_impl ( )

inlineprotectedvirtual

Creates a Dense matrix where the underlying array is a view of this' array.

Returns: A Dense matrix that is a view of this.

References gko::matrix::Dense< ValueType >::create(), gko::PolymorphicObject::get_executor(), gko::matrix::Dense< ValueType >::get_num_stored_elements(), gko::matrix::Dense< ValueType >::get_stride(), gko::matrix::Dense< ValueType >::get_values(), and gko::make_array_view().

◆ create_with_config_of()

template<typename ValueType = default_precision>

static std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_with_config_of ( ptr_param< const Dense< ValueType > > other )

inlinestatic

Creates a Dense matrix with the same size and stride as another Dense matrix.

Parameters

other The other matrix whose configuration needs to copied.

◆ create_with_same_config()

template<typename ValueType = default_precision>

virtual std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_with_same_config ( ) const

inlineprotectedvirtual

Creates a Dense matrix with the same size and stride as the callers matrix.

Returns: a Dense matrix with the same size and stride as the caller.

References gko::matrix::Dense< ValueType >::create(), gko::PolymorphicObject::get_executor(), and gko::matrix::Dense< ValueType >::get_stride().

◆ create_with_type_of() [1/3]

template<typename ValueType = default_precision>

static std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_with_type_of	(	ptr_param< const Dense< ValueType > >	other,
		std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size,
		const dim< 2 > &	local_size,
		size_type	stride
	)

inlinestatic

Parameters

local_size	Unused
stride	The stride of the new matrix.

Note: This is an overload to stay consistent with gko::experimental::distributed::Vector

◆ create_with_type_of() [2/3]

template<typename ValueType = default_precision>

static std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_with_type_of	(	ptr_param< const Dense< ValueType > >	other,
		std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size,
		size_type	stride
	)

inlinestatic

Parameters

stride The stride of the new matrix.

Note: This is an overload which allows full parameter specification.

◆ create_with_type_of() [3/3]

template<typename ValueType = default_precision>

static std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_with_type_of	(	ptr_param< const Dense< ValueType > >	other,
		std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size = `dim<2>{}`
	)

inlinestatic

Creates a Dense matrix with the same type as another Dense matrix but on a different executor and with a different size.

Parameters

other	The other matrix whose type we target.
exec	The executor of the new matrix.
size	The size of the new matrix.
stride	The stride of the new matrix.

Returns: a Dense matrix with the type of other.

◆ create_with_type_of_impl()

template<typename ValueType = default_precision>

virtual std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::create_with_type_of_impl	(	std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size,
		size_type	stride
	)		const

inlineprotectedvirtual

Creates a Dense matrix with the same type as the callers matrix.

Parameters

size	size of the matrix

Returns: a Dense matrix with the same type as the caller.

References gko::matrix::Dense< ValueType >::create().

◆ extract_diagonal() [1/2]

template<typename ValueType = default_precision>

std::unique_ptr< Diagonal< ValueType > > gko::matrix::Dense< ValueType >::extract_diagonal ( ) const

overridevirtual

Extracts the diagonal entries of the matrix into a vector.

Parameters

diag	the vector into which the diagonal will be written

Implements gko::DiagonalExtractable< ValueType >.

◆ extract_diagonal() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::extract_diagonal ( ptr_param< Diagonal< ValueType > > output ) const

Writes the diagonal of this matrix into an existing diagonal matrix.

Parameters

output The output matrix. Its size must match the size of this matrix's diagonal.

See also: Dense::extract_diagonal()

◆ fill()

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::fill ( const ValueType value )

Fill the dense matrix with a given value.

Parameters

value the value to be filled

◆ get_const_values()

template<typename ValueType = default_precision>

const value_type * gko::matrix::Dense< ValueType >::get_const_values ( ) const

inlinenoexcept

Returns a pointer to the array of values of the matrix.

Returns: the pointer to the array of values

Note: This is the constant version of the function, which can be significantly more memory efficient than the non-constant version, so always prefer this version.

References gko::array< ValueType >::get_const_data().

Referenced by gko::matrix::Dense< ValueType >::create_const_view_of_impl().

◆ get_imag() [1/2]

template<typename ValueType = default_precision>

std::unique_ptr< real_type > gko::matrix::Dense< ValueType >::get_imag ( ) const

Creates a new real matrix and extracts the imaginary part of the original matrix into that.

◆ get_imag() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::get_imag ( ptr_param< real_type > result ) const

Extracts the imaginary part of the original matrix into a given real matrix.

◆ get_num_stored_elements()

template<typename ValueType = default_precision>

size_type gko::matrix::Dense< ValueType >::get_num_stored_elements ( ) const

inlinenoexcept

Returns the number of elements explicitly stored in the matrix.

Returns: the number of elements explicitly stored in the matrix

References gko::array< ValueType >::get_size().

Referenced by gko::matrix::Dense< ValueType >::create_const_view_of_impl(), and gko::matrix::Dense< ValueType >::create_view_of_impl().

◆ get_real() [1/2]

template<typename ValueType = default_precision>

std::unique_ptr< real_type > gko::matrix::Dense< ValueType >::get_real ( ) const

Creates a new real matrix and extracts the real part of the original matrix into that.

◆ get_real() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::get_real ( ptr_param< real_type > result ) const

Extracts the real part of the original matrix into a given real matrix.

◆ get_stride()

template<typename ValueType = default_precision>

size_type gko::matrix::Dense< ValueType >::get_stride ( ) const

inlinenoexcept

Returns the stride of the matrix.

Returns: the stride of the matrix.

Referenced by gko::matrix::Dense< ValueType >::create_const_view_of_impl(), gko::matrix::Dense< ValueType >::create_submatrix(), gko::matrix::Dense< ValueType >::create_view_of_impl(), and gko::matrix::Dense< ValueType >::create_with_same_config().

◆ get_values()

template<typename ValueType = default_precision>

value_type * gko::matrix::Dense< ValueType >::get_values ( )

inlinenoexcept

Returns a pointer to the array of values of the matrix.

Returns: the pointer to the array of values

References gko::array< ValueType >::get_data().

Referenced by gko::matrix::Dense< ValueType >::create_view_of_impl().

◆ inv_scale()

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::inv_scale ( ptr_param< const LinOp > alpha )

Scales the matrix with the inverse of a scalar.

Parameters

alpha If alpha is 1x1 Dense matrix, the entire matrix is scaled by 1 / alpha. If it is a Dense row vector of values, then i-th column of the matrix is scaled with the inverse of the i-th element of alpha (the number of columns of alpha has to match the number of columns of the matrix).

◆ inv_scale_impl()

template<typename ValueType = default_precision>

virtual void gko::matrix::Dense< ValueType >::inv_scale_impl ( const LinOp * alpha )

protectedvirtual

◆ inverse_column_permute() [1/4]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::inverse_column_permute ( const array< int32 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the row permutation of the inverse permuted object. In the resulting LinOp, the column perm[i] contains the input column i.

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(A P^{-T}\).

Parameters

permutation_indices the array of indices containing the permutation order perm.

Returns: a pointer to the new inverse permuted object

Implements gko::Permutable< int32 >.

◆ inverse_column_permute() [2/4]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::inverse_column_permute	(	const array< int32 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

Writes the inverse column-permuted matrix into the given output matrix.

Parameters

permutation_indices	The array containing permutation indices. It must have `this->get_size()[1]` elements.
output	The output matrix. It must have the dimensions `this->get_size()`

See also: Dense::inverse_column_permute(const array<int32>*)

◆ inverse_column_permute() [3/4]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::inverse_column_permute ( const array< int64 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the row permutation of the inverse permuted object. In the resulting LinOp, the column perm[i] contains the input column i.

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(A P^{-T}\).

Parameters

permutation_indices the array of indices containing the permutation order perm.

Returns: a pointer to the new inverse permuted object

Implements gko::Permutable< int64 >.

◆ inverse_column_permute() [4/4]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::inverse_column_permute	(	const array< int64 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

◆ inverse_permute() [1/4]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::inverse_permute ( const array< int32 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the symmetric inverse row and column permutation of the Permutable object. In the resulting LinOp, the entry at location (perm[i],perm[j]) contains the input value (i,j).

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(P^{-1} A P^{-T}\).

Parameters

permutation_indices the array of indices containing the permutation order.

Returns: a pointer to the new permuted object

Reimplemented from gko::Permutable< int32 >.

◆ inverse_permute() [2/4]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::inverse_permute	(	const array< int32 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

Writes the inverse symmetrically permuted matrix into the given output matrix.

Parameters

permutation_indices	The array containing permutation indices. It must have `this->get_size()[0]` elements.
output	The output matrix. It must have the dimensions `this->get_size()`

See also: Dense::inverse_permute(const array<int32>*)

◆ inverse_permute() [3/4]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::inverse_permute ( const array< int64 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the symmetric inverse row and column permutation of the Permutable object. In the resulting LinOp, the entry at location (perm[i],perm[j]) contains the input value (i,j).

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(P^{-1} A P^{-T}\).

Parameters

permutation_indices the array of indices containing the permutation order.

Returns: a pointer to the new permuted object

Reimplemented from gko::Permutable< int64 >.

◆ inverse_permute() [4/4]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::inverse_permute	(	const array< int64 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

◆ inverse_row_permute() [1/4]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::inverse_row_permute ( const array< int32 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the row permutation of the inverse permuted object. In the resulting LinOp, the row perm[i] contains the input row i.

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(P^{-1} A\).

Parameters

permutation_indices the array of indices containing the permutation order perm.

Returns: a pointer to the new inverse permuted object

Implements gko::Permutable< int32 >.

◆ inverse_row_permute() [2/4]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::inverse_row_permute	(	const array< int32 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

Writes the inverse row-permuted matrix into the given output matrix.

Parameters

permutation_indices	The array containing permutation indices. It must have `this->get_size()[0]` elements.
output	The output matrix. It must have the dimensions `this->get_size()`

See also: Dense::inverse_row_permute(const array<int32>*)

◆ inverse_row_permute() [3/4]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::inverse_row_permute ( const array< int64 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the row permutation of the inverse permuted object. In the resulting LinOp, the row perm[i] contains the input row i.

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(P^{-1} A\).

Parameters

permutation_indices the array of indices containing the permutation order perm.

Returns: a pointer to the new inverse permuted object

Implements gko::Permutable< int64 >.

◆ inverse_row_permute() [4/4]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::inverse_row_permute	(	const array< int64 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

◆ make_complex() [1/2]

template<typename ValueType = default_precision>

std::unique_ptr< complex_type > gko::matrix::Dense< ValueType >::make_complex ( ) const

Creates a complex copy of the original matrix. If the original matrix was real, the imaginary part of the result will be zero.

◆ make_complex() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::make_complex ( ptr_param< complex_type > result ) const

Writes a complex copy of the original matrix to a given complex matrix. If the original matrix was real, the imaginary part of the result will be zero.

◆ operator=() [1/2]

template<typename ValueType = default_precision>

Dense & gko::matrix::Dense< ValueType >::operator= ( const Dense< ValueType > & )

Copy-assigns a Dense matrix. Preserves the executor, reallocates the matrix with minimal stride if the dimensions don't match, then copies the data over, ignoring padding.

◆ operator=() [2/2]

template<typename ValueType = default_precision>

Dense & gko::matrix::Dense< ValueType >::operator= ( Dense< ValueType > && )

Move-assigns a Dense matrix. Preserves the executor, moves the data over preserving size and stride. Leaves the moved-from object in an empty state (0x0 with empty Array).

◆ permute() [1/12]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::permute ( const array< int32 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the symmetric row and column permutation of the Permutable object. In the resulting LinOp, the entry at location (i,j) contains the input value (perm[i],perm[j]).

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(P A P^T\).

Parameters

permutation_indices the array of indices containing the permutation order.

Returns: a pointer to the new permuted object

Reimplemented from gko::Permutable< int32 >.

◆ permute() [2/12]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::permute	(	const array< int32 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

Writes the symmetrically permuted matrix into the given output matrix.

Parameters

permutation_indices	The array containing permutation indices. It must have `this->get_size()[0]` elements.
output	The output matrix. It must have the dimensions `this->get_size()`

See also: Dense::permute(const array<int32>*)

◆ permute() [3/12]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::permute ( const array< int64 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the symmetric row and column permutation of the Permutable object. In the resulting LinOp, the entry at location (i,j) contains the input value (perm[i],perm[j]).

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(P A P^T\).

Parameters

permutation_indices the array of indices containing the permutation order.

Returns: a pointer to the new permuted object

Reimplemented from gko::Permutable< int64 >.

◆ permute() [4/12]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::permute	(	const array< int64 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

◆ permute() [5/12]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::permute	(	ptr_param< const Permutation< int32 > >	permutation,
		permute_mode	mode = `permute_mode::symmetric`
	)		const

Creates a permuted copy \(A'\) of this matrix \(A\) with the given permutation \(P\). By default, this computes a symmetric permutation (permute_mode::symmetric). For the effect of the different permutation modes, see permute_mode.

Parameters

permutation	The input permutation.
mode	The permutation mode. If permute_mode::inverse is set, we use the inverse permutation \(P^{-1}\) instead of \(P\). If permute_mode::rows is set, the rows will be permuted. If permute_mode::columns is set, the columns will be permuted.

Returns: The permuted matrix.

◆ permute() [6/12]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::permute	(	ptr_param< const Permutation< int32 > >	permutation,
		ptr_param< Dense< ValueType > >	output,
		permute_mode	mode
	)		const

Overload of permute(ptr_param<const Permutation<int32>>, permute_mode) that writes the permuted copy into an existing Dense matrix.

Parameters

output the output matrix.

◆ permute() [7/12]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::permute	(	ptr_param< const Permutation< int32 > >	row_permutation,
		ptr_param< const Permutation< int32 > >	column_permutation,
		bool	invert = `false`
	)		const

Creates a non-symmetrically permuted copy \(A'\) of this matrix \(A\) with the given row and column permutations \(P\) and \(Q\). The operation will compute \(A'(i, j) = A(p[i], q[j])\), or \(A' = P A Q^T\) if invert is false, and \(A'(p[i], q[j]) = A(i,j)\), or \(A' = P^{-1} A Q^{-T}\) if invert is true.

Parameters

row_permutation	The permutation \(P\) to apply to the rows
column_permutation	The permutation \(Q\) to apply to the columns
invert	If set to `false`, uses the input permutations, otherwise uses their inverses \(P^{-1}, Q^{-1}\)

Returns: The permuted matrix.

◆ permute() [8/12]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::permute	(	ptr_param< const Permutation< int32 > >	row_permutation,
		ptr_param< const Permutation< int32 > >	column_permutation,
		ptr_param< Dense< ValueType > >	output,
		bool	invert = `false`
	)		const

Overload of permute(ptr_param<const Permutation<int32>>, ptr_param<const Permutation<int32>>, permute_mode) that writes the permuted copy into an existing Dense matrix.

Parameters

output the output matrix.

◆ permute() [9/12]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::permute	(	ptr_param< const Permutation< int64 > >	permutation,
		permute_mode	mode = `permute_mode::symmetric`
	)		const

◆ permute() [10/12]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::permute	(	ptr_param< const Permutation< int64 > >	permutation,
		ptr_param< Dense< ValueType > >	output,
		permute_mode	mode
	)		const

◆ permute() [11/12]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::permute	(	ptr_param< const Permutation< int64 > >	row_permutation,
		ptr_param< const Permutation< int64 > >	column_permutation,
		bool	invert = `false`
	)		const

◆ permute() [12/12]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::permute	(	ptr_param< const Permutation< int64 > >	row_permutation,
		ptr_param< const Permutation< int64 > >	column_permutation,
		ptr_param< Dense< ValueType > >	output,
		bool	invert = `false`
	)		const

◆ resize()

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::resize ( gko::dim< 2 > new_size )

protected

Resizes the matrix to the given size.

If the new size matches the current size, the stride will be left unchanged, otherwise it will be set to the number of columns.

Parameters

new_size the new matrix dimensions

◆ row_gather() [1/6]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::row_gather ( const array< int32 > * gather_indices ) const

Create a Dense matrix consisting of the given rows from this matrix.

Parameters

gather_indices pointer to an array containing row indices from this matrix. It may contain duplicates.

Returns: Dense matrix on the same executor with the same number of columns and gather_indices->get_size() rows containing the gathered rows from this matrix: output(i,j) = input(gather_indices(i), j)

◆ row_gather() [2/6]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::row_gather	(	const array< int32 > *	gather_indices,
		ptr_param< LinOp >	row_collection
	)		const

Copies the given rows from this matrix into row_collection

Parameters

gather_indices	pointer to an array containing row indices from this matrix. It may contain duplicates.
row_collection	pointer to a LinOp that will store the gathered rows: `row_collection(i,j) = input(gather_indices(i), j)` It must have the same number of columns as this matrix and `gather_indices->get_size()` rows.

◆ row_gather() [3/6]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::row_gather ( const array< int64 > * gather_indices ) const

Create a Dense matrix consisting of the given rows from this matrix.

Parameters

gather_indices pointer to an array containing row indices from this matrix. It may contain duplicates.

Returns: Dense matrix on the same executor with the same number of columns and gather_indices->get_size() rows containing the gathered rows from this matrix: output(i,j) = input(gather_indices(i), j)

◆ row_gather() [4/6]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::row_gather	(	const array< int64 > *	gather_indices,
		ptr_param< LinOp >	row_collection
	)		const

◆ row_gather() [5/6]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::row_gather	(	ptr_param< const LinOp >	alpha,
		const array< int32 > *	gather_indices,
		ptr_param< const LinOp >	beta,
		ptr_param< LinOp >	row_collection
	)		const

Copies the given rows from this matrix into row_collection with scaling

Parameters

alpha	scaling the result of row gathering
gather_indices	pointer to an array containing row indices from this matrix. It may contain duplicates.
beta	scaling the input row_collection
row_collection	pointer to a LinOp that will store the gathered rows: `row_collection(i,j) = input(gather_indices(i), j)` It must have the same number of columns as this matrix and `gather_indices->get_size()` rows.

◆ row_gather() [6/6]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::row_gather	(	ptr_param< const LinOp >	alpha,
		const array< int64 > *	gather_indices,
		ptr_param< const LinOp >	beta,
		ptr_param< LinOp >	row_collection
	)		const

◆ row_permute() [1/4]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::row_permute ( const array< int32 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the row permutation of the Permutable object. In the resulting LinOp, the row i contains the input row perm[i].

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(P A\).

Parameters

permutation_indices the array of indices containing the permutation order.

Returns: a pointer to the new permuted object

Implements gko::Permutable< int32 >.

◆ row_permute() [2/4]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::row_permute	(	const array< int32 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

Writes the row-permuted matrix into the given output matrix.

Parameters

permutation_indices	The array containing permutation indices. It must have `this->get_size()[0]` elements.
output	The output matrix. It must have the dimensions `this->get_size()`

See also: Dense::row_permute(const array<int32>*)

◆ row_permute() [3/4]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::row_permute ( const array< int64 > * permutation_indices ) const

overridevirtual

Returns a LinOp representing the row permutation of the Permutable object. In the resulting LinOp, the row i contains the input row perm[i].

From the linear algebra perspective, with \(P_{ij} = \delta_{i \pi(i)}\), this represents the operation \(P A\).

Parameters

permutation_indices the array of indices containing the permutation order.

Returns: a pointer to the new permuted object

Implements gko::Permutable< int64 >.

◆ row_permute() [4/4]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::row_permute	(	const array< int64 > *	permutation_indices,
		ptr_param< Dense< ValueType > >	output
	)		const

◆ scale()

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::scale ( ptr_param< const LinOp > alpha )

Scales the matrix with a scalar (aka: BLAS scal).

Parameters

alpha If alpha is 1x1 Dense matrix, the entire matrix is scaled by alpha. If it is a Dense row vector of values, then i-th column of the matrix is scaled with the i-th element of alpha (the number of columns of alpha has to match the number of columns of the matrix).

◆ scale_impl()

template<typename ValueType = default_precision>

virtual void gko::matrix::Dense< ValueType >::scale_impl ( const LinOp * alpha )

protectedvirtual

◆ scale_permute() [1/8]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::scale_permute	(	ptr_param< const ScaledPermutation< value_type, int32 > >	permutation,
		permute_mode	mode = `permute_mode::symmetric`
	)		const

Creates a scaled and permuted copy of this matrix. For an explanation of the permutation modes, see permute(ptr_param<const Permutation<index_type>>, permute_mode)

Parameters

permutation	The scaled permutation.
mode	The permutation mode.

Returns: The permuted matrix.

◆ scale_permute() [2/8]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::scale_permute	(	ptr_param< const ScaledPermutation< value_type, int32 > >	permutation,
		ptr_param< Dense< ValueType > >	output,
		permute_mode	mode
	)		const

Overload of scale_permute(ptr_param<const ScaledPermutation<value_type, int32>>, permute_mode) that writes the permuted copy into an existing Dense matrix.

Parameters

output the output matrix.

◆ scale_permute() [3/8]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::scale_permute	(	ptr_param< const ScaledPermutation< value_type, int32 > >	row_permutation,
		ptr_param< const ScaledPermutation< value_type, int32 > >	column_permutation,
		bool	invert = `false`
	)		const

Creates a scaled and permuted copy of this matrix. For an explanation of the parameters, see permute(ptr_param<const Permutation<index_type>>, ptr_param<const Permutation<index_type>>, permute_mode)

Parameters

row_permutation	The scaled row permutation.
column_permutation	The scaled column permutation.
invert	If set to `false`, uses the input permutations, otherwise uses their inverses \(P^{-1}, Q^{-1}\)

Returns: The permuted matrix.

◆ scale_permute() [4/8]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::scale_permute	(	ptr_param< const ScaledPermutation< value_type, int32 > >	row_permutation,
		ptr_param< const ScaledPermutation< value_type, int32 > >	column_permutation,
		ptr_param< Dense< ValueType > >	output,
		bool	invert = `false`
	)		const

Overload of scale_permute(ptr_param<const ScaledPermutation<value_type, int32>>, ptr_param<const ScaledPermutation<value_type, int32>>, bool) that writes the permuted copy into an existing Dense matrix.

Parameters

output the output matrix.

◆ scale_permute() [5/8]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::scale_permute	(	ptr_param< const ScaledPermutation< value_type, int64 > >	permutation,
		permute_mode	mode = `permute_mode::symmetric`
	)		const

◆ scale_permute() [6/8]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::scale_permute	(	ptr_param< const ScaledPermutation< value_type, int64 > >	permutation,
		ptr_param< Dense< ValueType > >	output,
		permute_mode	mode
	)		const

◆ scale_permute() [7/8]

template<typename ValueType = default_precision>

std::unique_ptr< Dense > gko::matrix::Dense< ValueType >::scale_permute	(	ptr_param< const ScaledPermutation< value_type, int64 > >	row_permutation,
		ptr_param< const ScaledPermutation< value_type, int64 > >	column_permutation,
		bool	invert = `false`
	)		const

◆ scale_permute() [8/8]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::scale_permute	(	ptr_param< const ScaledPermutation< value_type, int64 > >	row_permutation,
		ptr_param< const ScaledPermutation< value_type, int64 > >	column_permutation,
		ptr_param< Dense< ValueType > >	output,
		bool	invert = `false`
	)		const

◆ sub_scaled()

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::sub_scaled	(	ptr_param< const LinOp >	alpha,
		ptr_param< const LinOp >	b
	)

Subtracts b scaled by alpha from the matrix (aka: BLAS axpy).

Parameters

alpha	If alpha is 1x1 Dense matrix, b is scaled by alpha. If it is a Dense row vector of values, then i-th column of b is scaled with the i-th element of alpha (the number of columns of alpha has to match the number of columns of the matrix).
b	a matrix of the same dimension as this

◆ sub_scaled_impl()

template<typename ValueType = default_precision>

virtual void gko::matrix::Dense< ValueType >::sub_scaled_impl	(	const LinOp *	alpha,
		const LinOp *	b
	)

protectedvirtual

◆ transpose() [1/2]

template<typename ValueType = default_precision>

std::unique_ptr< LinOp > gko::matrix::Dense< ValueType >::transpose ( ) const

overridevirtual

Returns a LinOp representing the transpose of the Transposable object.

Returns: a pointer to the new transposed object

Implements gko::Transposable.

◆ transpose() [2/2]

template<typename ValueType = default_precision>

void gko::matrix::Dense< ValueType >::transpose ( ptr_param< Dense< ValueType > > output ) const

Writes the transposed matrix into the given output matrix.

Parameters

output The output matrix. It must have the dimensions gko::transpose(this->get_size())

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

ginkgo/core/base/dense_cache.hpp
ginkgo/core/matrix/dense.hpp

Dense< ValueType > Class Template Reference

Dense&lt; ValueType &gt; Class Template Reference#

Public Types

Public Member Functions

Static Public Member Functions

Protected Member Functions

Friends

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ Dense() [1/2]

◆ Dense() [2/2]

Member Function Documentation

◆ add_scaled()

◆ add_scaled_impl()

◆ at() [1/4]

◆ at() [2/4]

◆ at() [3/4]

◆ at() [4/4]

◆ column_permute() [1/4]

◆ column_permute() [2/4]

◆ column_permute() [3/4]

◆ column_permute() [4/4]

◆ compute_absolute() [1/2]

◆ compute_absolute() [2/2]

◆ compute_absolute_inplace()

◆ compute_conj_dot() [1/2]

◆ compute_conj_dot() [2/2]

◆ compute_conj_dot_impl()

◆ compute_dot() [1/2]

◆ compute_dot() [2/2]

◆ compute_dot_impl()

◆ compute_mean() [1/2]

◆ compute_mean() [2/2]

◆ compute_mean_impl()

◆ compute_norm1() [1/2]

◆ compute_norm1() [2/2]

◆ compute_norm1_impl()

◆ compute_norm2() [1/2]

◆ compute_norm2() [2/2]

◆ compute_norm2_impl()

◆ compute_squared_norm2() [1/2]

◆ compute_squared_norm2() [2/2]

◆ compute_squared_norm2_impl()

◆ conj_transpose() [1/2]

◆ conj_transpose() [2/2]

◆ create() [1/3]

◆ create() [2/3]

◆ create() [3/3]

◆ create_const()

◆ create_const_view_of()

◆ create_const_view_of_impl()

◆ create_real_view() [1/2]

◆ create_real_view() [2/2]

◆ create_submatrix() [1/2]

◆ create_submatrix() [2/2]

◆ create_submatrix_impl()

◆ create_view_of()

◆ create_view_of_impl()

◆ create_with_config_of()

◆ create_with_same_config()

◆ create_with_type_of() [1/3]

◆ create_with_type_of() [2/3]

◆ create_with_type_of() [3/3]

◆ create_with_type_of_impl()

◆ extract_diagonal() [1/2]

◆ extract_diagonal() [2/2]

◆ fill()

◆ get_const_values()

◆ get_imag() [1/2]

◆ get_imag() [2/2]

◆ get_num_stored_elements()

◆ get_real() [1/2]

◆ get_real() [2/2]

◆ get_stride()

◆ get_values()

◆ inv_scale()

◆ inv_scale_impl()

◆ inverse_column_permute() [1/4]

◆ inverse_column_permute() [2/4]

Dense< ValueType > Class Template Reference#