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Eigen
3.2.92
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class Bidiagonal Divide and Conquer SVD
MatrixType | the type of the matrix of which we are computing the SVD decomposition We plan to have a very similar interface to JacobiSVD on this class. It should be used to speed up the calcul of SVD for big matrices. |
Public Types | |
typedef Eigen::Index | Index |
Public Member Functions | |
BDCSVD () | |
Default Constructor. More... | |
BDCSVD (Index rows, Index cols, unsigned int computationOptions=0) | |
Default Constructor with memory preallocation. More... | |
BDCSVD (const MatrixType &matrix, unsigned int computationOptions=0) | |
Constructor performing the decomposition of given matrix. More... | |
BDCSVD & | compute (const MatrixType &matrix, unsigned int computationOptions) |
Method performing the decomposition of given matrix using custom options. More... | |
BDCSVD & | compute (const MatrixType &matrix) |
Method performing the decomposition of given matrix using current options. More... | |
bool | computeU () const |
bool | computeV () const |
const MatrixUType & | matrixU () const |
const MatrixVType & | matrixV () const |
Index | nonzeroSingularValues () const |
Index | rank () const |
BDCSVD< _MatrixType > & | setThreshold (const RealScalar &threshold) |
BDCSVD< _MatrixType > & | setThreshold (Default_t) |
const SingularValuesType & | singularValues () const |
const Solve< BDCSVD< _MatrixType >, Rhs > | solve (const MatrixBase< Rhs > &b) const |
RealScalar | threshold () const |
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Default Constructor.
The default constructor is useful in cases in which the user intends to perform decompositions via BDCSVD::compute(const MatrixType&).
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Default Constructor with memory preallocation.
Like the default constructor but with preallocation of the internal data according to the specified problem size.
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Constructor performing the decomposition of given matrix.
matrix | the matrix to decompose |
computationOptions | optional parameter allowing to specify if you want full or thin U or V unitaries to be computed. By default, none is computed. This is a bit - field, the possible bits are #ComputeFullU, #ComputeThinU, #ComputeFullV, #ComputeThinV. |
Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not available with the (non - default) FullPivHouseholderQR preconditioner.
BDCSVD< MatrixType > & Eigen::BDCSVD< MatrixType >::compute | ( | const MatrixType & | matrix, |
unsigned int | computationOptions | ||
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Method performing the decomposition of given matrix using custom options.
matrix | the matrix to decompose |
computationOptions | optional parameter allowing to specify if you want full or thin U or V unitaries to be computed. By default, none is computed. This is a bit - field, the possible bits are #ComputeFullU, #ComputeThinU, #ComputeFullV, #ComputeThinV. |
Thin unitaries are only available if your matrix type has a Dynamic number of columns (for example MatrixXf). They also are not available with the (non - default) FullPivHouseholderQR preconditioner.
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Method performing the decomposition of given matrix using current options.
matrix | the matrix to decompose |
This method uses the current computationOptions, as already passed to the constructor or to compute(const MatrixType&, unsigned int).
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inlineinherited |
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For the SVD decomposition of a n-by-p matrix, letting m be the minimum of n and p, the U matrix is n-by-n if you asked for #ComputeFullU, and is n-by-m if you asked for #ComputeThinU.
The m first columns of U are the left singular vectors of the matrix being decomposed.
This method asserts that you asked for U to be computed.
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For the SVD decomposition of a n-by-p matrix, letting m be the minimum of n and p, the V matrix is p-by-p if you asked for #ComputeFullV, and is p-by-m if you asked for ComputeThinV.
The m first columns of V are the right singular vectors of the matrix being decomposed.
This method asserts that you asked for V to be computed.
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*this
is the SVD.
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Allows to prescribe a threshold to be used by certain methods, such as rank() and solve(), which need to determine when singular values are to be considered nonzero. This is not used for the SVD decomposition itself.
When it needs to get the threshold value, Eigen calls threshold(). The default is NumTraits<Scalar>::epsilon()
threshold | The new value to use as the threshold. |
A singular value will be considered nonzero if its value is strictly greater than .
If you want to come back to the default behavior, call setThreshold(Default_t)
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Allows to come back to the default behavior, letting Eigen use its default formula for determining the threshold.
You should pass the special object Eigen::Default as parameter here.
See the documentation of setThreshold(const RealScalar&).
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For the SVD decomposition of a n-by-p matrix, letting m be the minimum of n and p, the returned vector has size m. Singular values are always sorted in decreasing order.
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b | the right-hand-side of the equation to solve. |
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Returns the threshold that will be used by certain methods such as rank().
See the documentation of setThreshold(const RealScalar&).