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std::vector< CompressedStorage
< Scalar > > & | _data () |
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const std::vector
< CompressedStorage< Scalar > > & | _data () const |
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Scalar | coeff (int row, int col) const |
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Scalar & | coeffRef (int row, int col) |
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int | cols () const |
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| DynamicSparseMatrix (int rows, int cols) |
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template<typename OtherDerived > |
| DynamicSparseMatrix (const SparseMatrixBase< OtherDerived > &other) |
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| DynamicSparseMatrix (const DynamicSparseMatrix &other) |
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void | endFill () |
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Scalar & | fill (int row, int col) |
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Scalar & | fillrand (int row, int col) |
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int | innerNonZeros (int j) const |
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int | innerSize () const |
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int | nonZeros () const |
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DynamicSparseMatrix & | operator= (const DynamicSparseMatrix &other) |
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template<typename OtherDerived > |
DynamicSparseMatrix & | operator= (const SparseMatrixBase< OtherDerived > &other) |
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int | outerSize () const |
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void | prune (Scalar reference, RealScalar epsilon=precision< RealScalar >()) |
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void | resize (int rows, int cols) |
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void | resizeAndKeepData (int rows, int cols) |
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int | rows () const |
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void | setZero () |
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void | startFill (int reserveSize=1000) |
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void | swap (DynamicSparseMatrix &other) |
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| ~DynamicSparseMatrix () |
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template<typename _Scalar, int _Flags = 0>
class Eigen::DynamicSparseMatrix< _Scalar, _Flags >
A sparse matrix class designed for matrix assembly purpose.
- Parameters
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_Scalar | the scalar type, i.e. the type of the coefficients |
Unlike SparseMatrix, this class provides a much higher degree of flexibility. In particular, it allows random read/write accesses in log(rho*outer_size) where rho
is the probability that a coefficient is nonzero and outer_size is the number of columns if the matrix is column-major and the number of rows otherwise.
Internally, the data are stored as a std::vector of compressed vector. The performances of random writes might decrease as the number of nonzeros per inner-vector increase. In practice, we observed very good performance till about 100 nonzeros/vector, and the performance remains relatively good till 500 nonzeros/vectors.
- See also
- SparseMatrix