SparseLLT Class Template Reference
[Sparse module]

List of all members.

---

Detailed Description

template<typename MatrixType, int Backend = DefaultBackend>
class Eigen::SparseLLT< MatrixType, Backend >

LLT Cholesky decomposition of a sparse matrix and associated features.

Parameters:

MatrixType

the type of the matrix of which we are computing the LLT Cholesky decomposition

See also:

class LLT, class LDLT

Public Member Functions
void	compute (const MatrixType &matrix)
int	flags () const
const CholMatrixType &	matrixL (void) const
RealScalar	precision () const
void	setFlags (int f)
void	setPrecision (RealScalar v)
template<typename Derived>
bool	solveInPlace (MatrixBase< Derived > &b) const
	SparseLLT (const MatrixType &matrix, int flags=0)
	SparseLLT (int flags=0)
bool	succeeded (void) const
Protected Types
enum	{   SupernodalFactorIsDirty,   MatrixLIsDirty }
typedef SparseMatrix< Scalar, LowerTriangular >	CholMatrixType
typedef NumTraits< typename MatrixType::Scalar >::Real	RealScalar
typedef MatrixType::Scalar	Scalar
Protected Attributes
int	m_flags
CholMatrixType	m_matrix
RealScalar	m_precision
int	m_status
bool	m_succeeded

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Constructor & Destructor Documentation

SparseLLT

(

int

flags = 0

)

[inline]

Creates a dummy LLT factorization object with flags flags.

SparseLLT	(	const MatrixType &	matrix,
		int	flags = 0
	)			[inline]

Creates a LLT object and compute the respective factorization of matrix using flags flags.

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Member Function Documentation

void compute

(

const MatrixType &

a

)

[inline]

Computes/re-computes the LLT factorization

Computes / recomputes the LLT decomposition of matrix a using the default algorithm.

int flags

(

)

const [inline]

Returns:

the current flags

const CholMatrixType& matrixL

(

void

)

const [inline]

Returns:

the lower triangular matrix L

RealScalar precision

(

)

const [inline]

Returns:

the current precision.

See also:

setPrecision()

void setFlags

(

int

f

)

[inline]

Sets the flags. Possible values are:

• CompleteFactorization
• IncompleteFactorization
• MemoryEfficient (hint to use the memory most efficient method offered by the backend)
• SupernodalMultifrontal (implies a complete factorization if supported by the backend, overloads the MemoryEfficient flags)
• SupernodalLeftLooking (implies a complete factorization if supported by the backend, overloads the MemoryEfficient flags)

See also:

flags()

void setPrecision

(

RealScalar

v

)

[inline]

Sets the relative threshold value used to prune zero coefficients during the decomposition.

Setting a value greater than zero speeds up computation, and yields to an imcomplete factorization with fewer non zero coefficients. Such approximate factors are especially useful to initialize an iterative solver.

Warning:

if precision is greater that zero, the LLT factorization is not guaranteed to succeed even if the matrix is positive definite.

Note that the exact meaning of this parameter might depends on the actual backend. Moreover, not all backends support this feature.

See also:

precision()

bool solveInPlace

(

MatrixBase< Derived > &

b

)

const [inline]

Computes b = L^-T L^-1 b

bool succeeded

(

void

)

const [inline]

Returns:

true if the factorization succeeded

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

• Sparse