A module dedicated to the implementation and usage of the Linear operators in Ginkgo. More...

Collaboration diagram for Linear Operators:

Modules
	Factorizations
	A module dedicated to the implementation and usage of the Factorizations in Ginkgo.

	SpMV employing different Matrix formats
	A module dedicated to the implementation and usage of the various Matrix Formats in Ginkgo.

	Preconditioners
	A module dedicated to the implementation and usage of the Preconditioners in Ginkgo.

	Solvers
	A module dedicated to the implementation and usage of the Solvers in Ginkgo.

Classes
class	gko::Combination< ValueType >
	The Combination class can be used to construct a linear combination of multiple linear operators `c1 * op1 + c2 * op2 + ...` More...

class	gko::Composition< ValueType >
	The Composition class can be used to compose linear operators `op1, op2, ..., opn` and obtain the operator `op1 * op2 * ...` More...

class	gko::LinOpFactory
	A LinOpFactory represents a higher order mapping which transforms one linear operator into another. More...

class	gko::ReadableFromMatrixData< ValueType, IndexType >
	A LinOp implementing this interface can read its data from a matrix_data structure. More...

class	gko::WritableToMatrixData< ValueType, IndexType >
	A LinOp implementing this interface can write its data to a matrix_data structure. More...

class	gko::Preconditionable
	A LinOp implementing this interface can be preconditioned. More...

class	gko::DiagonalLinOpExtractable
	The diagonal of a LinOp can be extracted. More...

class	gko::DiagonalExtractable< ValueType >
	The diagonal of a LinOp implementing this interface can be extracted. More...

class	gko::EnableAbsoluteComputation< AbsoluteLinOp >
	The EnableAbsoluteComputation mixin provides the default implementations of `compute_absolute_linop` and the absolute interface. More...

class	gko::EnableLinOp< ConcreteLinOp, PolymorphicBase >
	The EnableLinOp mixin can be used to provide sensible default implementations of the majority of the LinOp and PolymorphicObject interface. More...

class	gko::Perturbation< ValueType >
	The Perturbation class can be used to construct a LinOp to represent the operation `(identity + scalar * basis * projector)`. More...

class	gko::experimental::EnableDistributedLinOp< ConcreteLinOp, PolymorphicBase >
	This mixin does the same as EnableLinOp, but for concrete types that are derived from distributed::DistributedBase. More...

class	gko::experimental::distributed::preconditioner::Schwarz< ValueType, LocalIndexType, GlobalIndexType >
	A Schwarz preconditioner is a simple domain decomposition preconditioner that generalizes the Block Jacobi preconditioner, incorporating options for different local subdomain solvers and overlaps between the subdomains. More...

class	gko::experimental::distributed::Vector< ValueType >
	Vector is a format which explicitly stores (multiple) distributed column vectors in a dense storage format. More...

class	gko::factorization::Ic< ValueType, IndexType >
	Represents an incomplete Cholesky factorization (IC(0)) of a sparse matrix. More...

class	gko::factorization::Ilu< ValueType, IndexType >
	Represents an incomplete LU factorization – ILU(0) – of a sparse matrix. More...

class	gko::factorization::ParIc< ValueType, IndexType >
	ParIC is an incomplete Cholesky factorization which is computed in parallel. More...

class	gko::factorization::ParIct< ValueType, IndexType >
	ParICT is an incomplete threshold-based Cholesky factorization which is computed in parallel. More...

class	gko::factorization::ParIlu< ValueType, IndexType >
	ParILU is an incomplete LU factorization which is computed in parallel. More...

class	gko::factorization::ParIlut< ValueType, IndexType >
	ParILUT is an incomplete threshold-based LU factorization which is computed in parallel. More...

class	gko::matrix::Coo< ValueType, IndexType >
	COO stores a matrix in the coordinate matrix format. More...

class	gko::matrix::Csr< ValueType, IndexType >
	CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format). More...

class	gko::matrix::Dense< ValueType >
	Dense is a matrix format which explicitly stores all values of the matrix. More...

class	gko::matrix::Diagonal< ValueType >
	This class is a utility which efficiently implements the diagonal matrix (a linear operator which scales a vector row wise). More...

class	gko::matrix::Ell< ValueType, IndexType >
	ELL is a matrix format where stride with explicit zeros is used such that all rows have the same number of stored elements. More...

class	gko::matrix::Fbcsr< ValueType, IndexType >
	Fixed-block compressed sparse row storage matrix format. More...

class	gko::matrix::Fft
	This LinOp implements a 1D Fourier matrix using the FFT algorithm. More...

class	gko::matrix::Fft2
	This LinOp implements a 2D Fourier matrix using the FFT algorithm. More...

class	gko::matrix::Fft3
	This LinOp implements a 3D Fourier matrix using the FFT algorithm. More...

class	gko::matrix::Hybrid< ValueType, IndexType >
	HYBRID is a matrix format which splits the matrix into ELLPACK and COO format. More...

class	gko::matrix::Identity< ValueType >
	This class is a utility which efficiently implements the identity matrix (a linear operator which maps each vector to itself). More...

class	gko::matrix::IdentityFactory< ValueType >
	This factory is a utility which can be used to generate Identity operators. More...

class	gko::matrix::Permutation< IndexType >
	Permutation is a matrix format that represents a permutation matrix, i.e. More...

class	gko::matrix::RowGatherer< IndexType >
	RowGatherer is a matrix "format" which stores the gather indices arrays which can be used to gather rows to another matrix. More...

class	gko::matrix::ScaledPermutation< ValueType, IndexType >
	ScaledPermutation is a matrix combining a permutation with scaling factors. More...

class	gko::matrix::Sellp< ValueType, IndexType >
	SELL-P is a matrix format similar to ELL format. More...

class	gko::matrix::SparsityCsr< ValueType, IndexType >
	SparsityCsr is a matrix format which stores only the sparsity pattern of a sparse matrix by compressing each row of the matrix (compressed sparse row format). More...

class	gko::multigrid::FixedCoarsening< ValueType, IndexType >
	FixedCoarsening is a very simple coarse grid generation algorithm. More...

class	gko::multigrid::Pgm< ValueType, IndexType >
	Parallel graph match (Pgm) is the aggregate method introduced in the paper M. More...

class	gko::preconditioner::Ic< LSolverType, IndexType >
	The Incomplete Cholesky (IC) preconditioner solves the equation for a given lower triangular matrix L and the right hand side b (can contain multiple right hand sides). More...

class	gko::preconditioner::Ilu< LSolverType, USolverType, ReverseApply, IndexType >
	The Incomplete LU (ILU) preconditioner solves the equation for a given lower triangular matrix L, an upper triangular matrix U and the right hand side b (can contain multiple right hand sides). More...

class	gko::preconditioner::Isai< IsaiType, ValueType, IndexType >
	The Incomplete Sparse Approximate Inverse (ISAI) Preconditioner generates an approximate inverse matrix for a given square matrix A, lower triangular matrix L, upper triangular matrix U or symmetric positive (spd) matrix B. More...

class	gko::preconditioner::Jacobi< ValueType, IndexType >
	A block-Jacobi preconditioner is a block-diagonal linear operator, obtained by inverting the diagonal blocks of the source operator. More...

class	gko::solver::Bicg< ValueType >
	BICG or the Biconjugate gradient method is a Krylov subspace solver. More...

class	gko::solver::Bicgstab< ValueType >
	BiCGSTAB or the Bi-Conjugate Gradient-Stabilized is a Krylov subspace solver. More...

class	gko::solver::CbGmres< ValueType >
	CB-GMRES or the compressed basis generalized minimal residual method is an iterative type Krylov subspace method which is suitable for nonsymmetric linear systems. More...

class	gko::solver::Cg< ValueType >
	CG or the conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods. More...

class	gko::solver::Cgs< ValueType >
	CGS or the conjugate gradient square method is an iterative type Krylov subspace method which is suitable for general systems. More...

class	gko::solver::Fcg< ValueType >
	FCG or the flexible conjugate gradient method is an iterative type Krylov subspace method which is suitable for symmetric positive definite methods. More...

class	gko::solver::Gcr< ValueType >
	GCR or the generalized conjugate residual method is an iterative type Krylov subspace method similar to GMRES which is suitable for nonsymmetric linear systems. More...

class	gko::solver::Gmres< ValueType >
	GMRES or the generalized minimal residual method is an iterative type Krylov subspace method which is suitable for nonsymmetric linear systems. More...

class	gko::solver::Idr< ValueType >
	IDR(s) is an efficient method for solving large nonsymmetric systems of linear equations. More...

class	gko::solver::Ir< ValueType >
	Iterative refinement (IR) is an iterative method that uses another coarse method to approximate the error of the current solution via the current residual. More...

class	gko::solver::Multigrid
	Multigrid methods have a hierarchy of many levels, whose corase level is a subset of the fine level, of the problem. More...

class	gko::solver::EnableSolverBase< DerivedType, MatrixType >
	A LinOp deriving from this CRTP class stores a system matrix. More...

class	gko::solver::IterativeBase
	A LinOp implementing this interface stores a stopping criterion factory. More...

class	gko::solver::EnableIterativeBase< DerivedType >
	A LinOp deriving from this CRTP class stores a stopping criterion factory and allows applying with a guess. More...

class	gko::solver::EnablePreconditionedIterativeSolver< ValueType, DerivedType >
	A LinOp implementing this interface stores a system matrix and stopping criterion factory. More...

class	gko::solver::LowerTrs< ValueType, IndexType >
	LowerTrs is the triangular solver which solves the system L x = b, when L is a lower triangular matrix. More...

class	gko::solver::UpperTrs< ValueType, IndexType >
	UpperTrs is the triangular solver which solves the system U x = b, when U is an upper triangular matrix. More...

Macros
#define	GKO_CREATE_FACTORY_PARAMETERS(_parameters_name, _factory_name)
	This Macro will generate a new type containing the parameters for the factory `_factory_name`. More...

#define	GKO_ENABLE_BUILD_METHOD(_factory_name)
	Defines a build method for the factory, simplifying its construction by removing the repetitive typing of factory's name. More...

#define	GKO_FACTORY_PARAMETER(_name, ...)
	Creates a factory parameter in the factory parameters structure. More...

#define	GKO_FACTORY_PARAMETER_SCALAR(_name, _default) GKO_FACTORY_PARAMETER(_name, _default)
	Creates a scalar factory parameter in the factory parameters structure. More...

#define	GKO_FACTORY_PARAMETER_VECTOR(_name, ...) GKO_FACTORY_PARAMETER(_name, __VA_ARGS__)
	Creates a vector factory parameter in the factory parameters structure. More...

#define	GKO_ENABLE_LIN_OP_FACTORY(_lin_op, _parameters_name, _factory_name)
	This macro will generate a default implementation of a LinOpFactory for the LinOp subclass it is defined in. More...

Typedefs
template<typename ConcreteFactory , typename ConcreteLinOp , typename ParametersType , typename PolymorphicBase = LinOpFactory>
using	gko::EnableDefaultLinOpFactory = EnableDefaultFactory< ConcreteFactory, ConcreteLinOp, ParametersType, PolymorphicBase >
	This is an alias for the EnableDefaultFactory mixin, which correctly sets the template parameters to enable a subclass of LinOpFactory. More...

Functions
template<typename Matrix , typename... TArgs>
std::unique_ptr< Matrix >	gko::initialize (size_type stride, std::initializer_list< typename Matrix::value_type > vals, std::shared_ptr< const Executor > exec, TArgs &&... create_args)
	Creates and initializes a column-vector. More...

template<typename Matrix , typename... TArgs>
std::unique_ptr< Matrix >	gko::initialize (std::initializer_list< typename Matrix::value_type > vals, std::shared_ptr< const Executor > exec, TArgs &&... create_args)
	Creates and initializes a column-vector. More...

template<typename Matrix , typename... TArgs>
std::unique_ptr< Matrix >	gko::initialize (size_type stride, std::initializer_list< std::initializer_list< typename Matrix::value_type >> vals, std::shared_ptr< const Executor > exec, TArgs &&... create_args)
	Creates and initializes a matrix. More...

template<typename Matrix , typename... TArgs>
std::unique_ptr< Matrix >	gko::initialize (std::initializer_list< std::initializer_list< typename Matrix::value_type >> vals, std::shared_ptr< const Executor > exec, TArgs &&... create_args)
	Creates and initializes a matrix. More...

Detailed Description

A module dedicated to the implementation and usage of the Linear operators in Ginkgo.

Below we elaborate on one of the most important concepts of Ginkgo, the linear operator. The linear operator (LinOp) is a base class for all linear algebra objects in Ginkgo. The main benefit of having a single base class for the entire collection of linear algebra objects (as opposed to having separate hierarchies for matrices, solvers and preconditioners) is the generality it provides.

Advantages of this approach and usage

A common interface often allows for writing more generic code. If a user's routine requires only operations provided by the LinOp interface, the same code can be used for any kind of linear operators, independent of whether these are matrices, solvers or preconditioners. This feature is also extensively used in Ginkgo itself. For example, a preconditioner used inside a Krylov solver is a LinOp. This allows the user to supply a wide variety of preconditioners: either the ones which were designed to be used in this scenario (like ILU or block-Jacobi), a user-supplied matrix which is known to be a good preconditioner for the specific problem, or even another solver (e.g., if constructing a flexible GMRES solver).

For example, a matrix free implementation would require the user to provide an apply implementation and instead of passing the generated matrix to the solver, they would have to provide their apply implementation for all the executors needed and no other code needs to be changed. See The custom-matrix-format program example for more details.

Linear operator as a concept

The linear operator (LinOp) is a base class for all linear algebra objects in Ginkgo. The main benefit of having a single base class for the entire collection of linear algebra objects (as opposed to having separate hierarchies for matrices, solvers and preconditioners) is the generality it provides.

First, since all subclasses provide a common interface, the library users are exposed to a smaller set of routines. For example, a matrix-vector product, a preconditioner application, or even a system solve are just different terms given to the operation of applying a certain linear operator to a vector. As such, Ginkgo uses the same routine name, LinOp::apply() for each of these operations, where the actual operation performed depends on the type of linear operator involved in the operation.

Second, a common interface often allows for writing more generic code. If a user's routine requires only operations provided by the LinOp interface, the same code can be used for any kind of linear operators, independent of whether these are matrices, solvers or preconditioners. This feature is also extensively used in Ginkgo itself. For example, a preconditioner used inside a Krylov solver is a LinOp. This allows the user to supply a wide variety of preconditioners: either the ones which were designed to be used in this scenario (like ILU or block-Jacobi), a user-supplied matrix which is known to be a good preconditioner for the specific problem, or even another solver (e.g., if constructing a flexible GMRES solver).

A key observation for providing a unified interface for matrices, solvers, and preconditioners is that the most common operation performed on all of them can be expressed as an application of a linear operator to a vector:

the sparse matrix-vector product with a matrix is a linear operator application ;
the application of a preconditioner is a linear operator application $y = M^{-1}x$ , where is an approximation of the original system matrix (thus a preconditioner represents an "approximate inverse" operator $M^{-1}$ ).
the system solve can be viewed as linear operator application $x = A^{-1}b$ (it goes without saying that the implementation of linear system solves does not follow this conceptual idea), so a linear system solver can be viewed as a representation of the operator $A^{-1}$ .

Finally, direct manipulation of LinOp objects is rarely required in simple scenarios. As an illustrative example, one could construct a fixed-point iteration routine $x_{k+1} = Lx_k + b$ as follows:

std::unique_ptr<matrix::Dense<>> calculate_fixed_point(
        int iters, const LinOp *L, const matrix::Dense<> *x0
        const matrix::Dense<> *b)
{
    auto x = gko::clone(x0);
    auto tmp = gko::clone(x0);
    auto one = Dense<>::create(L->get_executor(), {1.0,});
    for (int i = 0; i < iters; ++i) {
        L->apply(tmp, x);
        x->add_scaled(one, b);
        tmp->copy_from(x);
    }
    return x;
}

Here, if $L$ is a matrix, LinOp::apply() refers to the matrix vector product, and L->apply(a, b) computes $b = L \cdot a$ . x->add_scaled(one, b) is the axpy vector update $x:=x+b$ .

The interesting part of this example is the apply() routine at line 4 of the function body. Since this routine is part of the LinOp base class, the fixed-point iteration routine can calculate a fixed point not only for matrices, but for any type of linear operator.

Linear Operators

Macro Definition Documentation

◆ GKO_CREATE_FACTORY_PARAMETERS

#define GKO_CREATE_FACTORY_PARAMETERS	(	_parameters_name,
		_factory_name
	)

Value:

public:                                                                 \
    class _factory_name;                                                \
    struct _parameters_name##_type                                      \
        : public ::gko::enable_parameters_type<_parameters_name##_type, \
                                               _factory_name>

This Macro will generate a new type containing the parameters for the factory _factory_name.

For more details, see GKO_ENABLE_LIN_OP_FACTORY(). It is required to use this macro before calling the macro GKO_ENABLE_LIN_OP_FACTORY(). It is also required to use the same names for all parameters between both macros.

Parameters

_parameters_name	name of the parameters member in the class
_factory_name	name of the generated factory type

◆ GKO_ENABLE_BUILD_METHOD

#define GKO_ENABLE_BUILD_METHOD ( _factory_name )

Value:

static auto build()->decltype(_factory_name::create())                   \
    {                                                                        \
        return _factory_name::create();                                      \
    }                                                                        \
    static_assert(true,                                                      \
                  "This assert is used to counter the false positive extra " \
                  "semi-colon warnings")

Defines a build method for the factory, simplifying its construction by removing the repetitive typing of factory's name.

Parameters

_factory_name the factory for which to define the method

◆ GKO_ENABLE_LIN_OP_FACTORY

#define GKO_ENABLE_LIN_OP_FACTORY	(	_lin_op,
		_parameters_name,
		_factory_name
	)

This macro will generate a default implementation of a LinOpFactory for the LinOp subclass it is defined in.

It is required to first call the macro GKO_CREATE_FACTORY_PARAMETERS() before this one in order to instantiate the parameters type first.

The list of parameters for the factory should be defined in a code block after the macro definition, and should contain a list of GKO_FACTORY_PARAMETER_* declarations. The class should provide a constructor with signature _lin_op(const _factory_name *, std::shared_ptr<const LinOp>) which the factory will use a callback to construct the object.

A minimal example of a linear operator is the following:

```c++ struct MyLinOp : public EnableLinOp<MyLinOp> { GKO_ENABLE_LIN_OP_FACTORY(MyLinOp, my_parameters, Factory) { // a factory parameter named "my_value", of type int and default // value of 5 int GKO_FACTORY_PARAMETER_SCALAR(my_value, 5); // a factory parameter named my_pair of type std::pair<int,int> // and default value {5, 5} std::pair<int, int> GKO_FACTORY_PARAMETER_VECTOR(my_pair, 5, 5); }; // constructor needed by EnableLinOp explicit MyLinOp(std::shared_ptr<const Executor> exec) { : EnableLinOp<MyLinOp>(exec) {} // constructor needed by the factory explicit MyLinOp(const Factory *factory, std::shared_ptr<const LinOp> matrix) : EnableLinOp<MyLinOp>(factory->get_executor()), matrix->get_size()), // store factory's parameters locally my_parameters_{factory->get_parameters()}, { int value = my_parameters_.my_value; // do something with value }

MyLinOp can then be created as follows:
 
```c++
auto exec = gko::ReferenceExecutor::create();
// create a factory with default `my_value` parameter
auto fact = MyLinOp::build().on(exec);
// create a operator using the factory:
auto my_op = fact->generate(gko::matrix::Identity::create(exec, 2));
std::cout << my_op->get_my_parameters().my_value;  // prints 5
 
// create a factory with custom `my_value` parameter
auto fact = MyLinOp::build().with_my_value(0).on(exec);
// create a operator using the factory:
auto my_op = fact->generate(gko::matrix::Identity::create(exec, 2));
std::cout << my_op->get_my_parameters().my_value;  // prints 0

Note: It is possible to combine both the #GKO_CREATE_FACTORY_PARAMETER_*() macros with this one in a unique macro for class templates (not with regular classes). Splitting this into two distinct macros allows to use them in all contexts. See https://stackoverflow.com/q/50202718/9385966 for more details.

Parameters

_lin_op	concrete operator for which the factory is to be created [CRTP parameter]
_parameters_name	name of the parameters member in the class (its type is `<_parameters_name>_type`, the protected member's name is `<_parameters_name>_`, and the public getter's name is `get_<_parameters_name>()`)
_factory_name	name of the generated factory type

◆ GKO_FACTORY_PARAMETER

#define GKO_FACTORY_PARAMETER	(	_name,
		...
	)

Value:

_name{__VA_ARGS__};                                                      \
                                                                             \
    template <typename... Args>                                              \
    auto with_##_name(Args&&... _value)                                      \
        ->std::decay_t<decltype(*(this->self()))>&                           \
    {                                                                        \
        using type = decltype(this->_name);                                  \
        this->_name = type{std::forward<Args>(_value)...};                   \
        return *(this->self());                                              \
    }                                                                        \
    static_assert(true,                                                      \
                  "This assert is used to counter the false positive extra " \
                  "semi-colon warnings")

Creates a factory parameter in the factory parameters structure.

Parameters

_name	name of the parameter
<strong>VA_ARGS</strong>	default value of the parameter

See also: GKO_ENABLE_LIN_OP_FACTORY for more details, and usage example

◆ GKO_FACTORY_PARAMETER_SCALAR

#define GKO_FACTORY_PARAMETER_SCALAR	(	_name,
		_default
	)	GKO_FACTORY_PARAMETER(_name, _default)

Creates a scalar factory parameter in the factory parameters structure.

Scalar in this context means that the constructor for this type only takes a single parameter.

Parameters

_name	name of the parameter
_default	default value of the parameter

See also: GKO_ENABLE_LIN_OP_FACTORY for more details, and usage example

◆ GKO_FACTORY_PARAMETER_VECTOR

#define GKO_FACTORY_PARAMETER_VECTOR	(	_name,
		...
	)	GKO_FACTORY_PARAMETER(_name, __VA_ARGS__)

Creates a vector factory parameter in the factory parameters structure.

Vector in this context means that the constructor for this type takes multiple parameters.

Parameters

_name	name of the parameter
_default	default value of the parameter

See also: GKO_ENABLE_LIN_OP_FACTORY for more details, and usage example

Typedef Documentation

◆ EnableDefaultLinOpFactory

template<typename ConcreteFactory , typename ConcreteLinOp , typename ParametersType , typename PolymorphicBase = LinOpFactory>

using gko::EnableDefaultLinOpFactory = typedef EnableDefaultFactory<ConcreteFactory, ConcreteLinOp, ParametersType, PolymorphicBase>

This is an alias for the EnableDefaultFactory mixin, which correctly sets the template parameters to enable a subclass of LinOpFactory.

Template Parameters

ConcreteFactory	the concrete factory which is being implemented [CRTP parameter]
ConcreteLinOp	the concrete LinOp type which this factory produces, needs to have a constructor which takes a const ConcreteFactory *, and an std::shared_ptr<const LinOp> as parameters.
ParametersType	a subclass of enable_parameters_type template which defines all of the parameters of the factory
PolymorphicBase	parent of ConcreteFactory in the polymorphic hierarchy, has to be a subclass of LinOpFactory

Function Documentation

◆ initialize() [1/4]

template<typename Matrix , typename... TArgs>

std::unique_ptr<Matrix> gko::initialize	(	size_type	stride,
		std::initializer_list< std::initializer_list< typename Matrix::value_type >>	vals,
		std::shared_ptr< const Executor >	exec,
		TArgs &&...	create_args
	)

Creates and initializes a matrix.

This function first creates a temporary Dense matrix, fills it with passed in values, and then converts the matrix to the requested type.

Template Parameters

Matrix	matrix type to initialize (Dense has to implement the ConvertibleTo<Matrix> interface)
TArgs	argument types for Matrix::create method (not including the implied Executor as the first argument)

Parameters

stride	row stride for the temporary Dense matrix
vals	values used to initialize the matrix
exec	Executor associated to the matrix
create_args	additional arguments passed to Matrix::create, not including the Executor, which is passed as the first argument

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

◆ initialize() [2/4]

template<typename Matrix , typename... TArgs>

std::unique_ptr<Matrix> gko::initialize	(	size_type	stride,
		std::initializer_list< typename Matrix::value_type >	vals,
		std::shared_ptr< const Executor >	exec,
		TArgs &&...	create_args
	)

Creates and initializes a column-vector.

This function first creates a temporary Dense matrix, fills it with passed in values, and then converts the matrix to the requested type.

Template Parameters

Matrix	matrix type to initialize (Dense has to implement the ConvertibleTo<Matrix> interface)
TArgs	argument types for Matrix::create method (not including the implied Executor as the first argument)

Parameters

stride	row stride for the temporary Dense matrix
vals	values used to initialize the vector
exec	Executor associated to the vector
create_args	additional arguments passed to Matrix::create, not including the Executor, which is passed as the first argument

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

◆ initialize() [3/4]

template<typename Matrix , typename... TArgs>

std::unique_ptr<Matrix> gko::initialize	(	std::initializer_list< std::initializer_list< typename Matrix::value_type >>	vals,
		std::shared_ptr< const Executor >	exec,
		TArgs &&...	create_args
	)

Creates and initializes a matrix.

This function first creates a temporary Dense matrix, fills it with passed in values, and then converts the matrix to the requested type. The stride of the intermediate Dense matrix is set to the number of columns of the initializer list.

Template Parameters

Matrix	matrix type to initialize (Dense has to implement the ConvertibleTo<Matrix> interface)
TArgs	argument types for Matrix::create method (not including the implied Executor as the first argument)

Parameters

vals	values used to initialize the matrix
exec	Executor associated to the matrix
create_args	additional arguments passed to Matrix::create, not including the Executor, which is passed as the first argument

◆ initialize() [4/4]

template<typename Matrix , typename... TArgs>

std::unique_ptr<Matrix> gko::initialize	(	std::initializer_list< typename Matrix::value_type >	vals,
		std::shared_ptr< const Executor >	exec,
		TArgs &&...	create_args
	)

Creates and initializes a column-vector.

This function first creates a temporary Dense matrix, fills it with passed in values, and then converts the matrix to the requested type. The stride of the intermediate Dense matrix is set to 1.

Template Parameters

Matrix	matrix type to initialize (Dense has to implement the ConvertibleTo<Matrix> interface)
TArgs	argument types for Matrix::create method (not including the implied Executor as the first argument)

Parameters

vals	values used to initialize the vector
exec	Executor associated to the vector
create_args	additional arguments passed to Matrix::create, not including the Executor, which is passed as the first argument

Modules

Classes

Macros

Typedefs

Functions

Detailed Description

Advantages of this approach and usage

Linear operator as a concept

Macro Definition Documentation

◆ GKO_CREATE_FACTORY_PARAMETERS

◆ GKO_ENABLE_BUILD_METHOD

◆ GKO_ENABLE_LIN_OP_FACTORY

◆ GKO_FACTORY_PARAMETER

◆ GKO_FACTORY_PARAMETER_SCALAR

◆ GKO_FACTORY_PARAMETER_VECTOR

Typedef Documentation

◆ EnableDefaultLinOpFactory

Function Documentation

◆ initialize() [1/4]

◆ initialize() [2/4]

◆ initialize() [3/4]

◆ initialize() [4/4]