LLT Cholesky decomposition of a sparse matrix and associated features.
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enum | {
SupernodalFactorIsDirty,
MatrixLIsDirty
} |
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typedef SparseMatrix< Scalar,
LowerTriangular > | CholMatrixType |
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typedef NumTraits< typename
MatrixType::Scalar >::Real | RealScalar |
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typedef MatrixType::Scalar | Scalar |
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int | m_flags |
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CholMatrixType | m_matrix |
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RealScalar | m_precision |
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int | m_status |
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bool | m_succeeded |
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template<typename MatrixType, int Backend = DefaultBackend>
class Eigen::SparseLLT< MatrixType, Backend >
LLT Cholesky decomposition of a sparse matrix and associated features.
- Parameters
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MatrixType | the type of the matrix of which we are computing the LLT Cholesky decomposition |
- See Also
- class LLT, class LDLT
Creates a dummy LLT factorization object with flags flags.
SparseLLT |
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const MatrixType & |
matrix, |
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int |
flags = 0 |
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) |
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inline |
Creates a LLT object and compute the respective factorization of matrix using flags flags.
void compute |
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const MatrixType & |
a | ) |
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Computes/re-computes the LLT factorization
Computes / recomputes the LLT decomposition of matrix a using the default algorithm.
- Returns
- the lower triangular matrix L
RealScalar precision |
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const |
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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 |
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RealScalar |
v | ) |
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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 |
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MatrixBase< Derived > & |
b | ) |
const |
bool succeeded |
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void |
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const |
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inline |
- Returns
- true if the factorization succeeded
The documentation for this class was generated from the following file: