LU decomposition of a sparse matrix and associated features.
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enum | { MatrixLUIsDirty
} |
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typedef SparseMatrix< Scalar,
LowerTriangular > | LUMatrixType |
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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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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::SparseLU< MatrixType, Backend >
LU decomposition of a sparse matrix and associated features.
- Parameters
-
MatrixType | the type of the matrix of which we are computing the LU factorization |
- See also
- class LU, class SparseLLT
Creates a dummy LU factorization object with flags flags.
SparseLU |
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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 LU 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 LU factorization
Computes / recomputes the LU decomposition of matrix a using the default algorithm.
RealScalar precision |
( |
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const |
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inline |
Sets the flags. Possible values are:
- CompleteFactorization
- IncompleteFactorization
- MemoryEfficient
- one of the ordering methods
- etc...
- 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.
Note that the exact meaning of this parameter might depends on the actual backend. Moreover, not all backends support this feature.
- See also
- precision()
- Returns
- the lower triangular matrix L
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the upper triangular matrix U
Computes *x = U^-1 L^-1 b
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: