|
| enum | {
RowsAtCompileTime = MatrixType::RowsAtCompileTime
, ColsAtCompileTime = MatrixType::ColsAtCompileTime
, DiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_DYNAMIC(RowsAtCompileTime,ColsAtCompileTime)
, MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime
,
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
, MaxDiagSizeAtCompileTime = EIGEN_SIZE_MIN_PREFER_FIXED(MaxRowsAtCompileTime,MaxColsAtCompileTime)
, MatrixOptions = MatrixType::Options
} |
| |
| typedef _MatrixType | MatrixType |
| |
| typedef MatrixType::Scalar | Scalar |
| |
| typedef NumTraits< typenameMatrixType::Scalar >::Real | RealScalar |
| |
| typedef Base::MatrixUType | MatrixUType |
| |
| typedef Base::MatrixVType | MatrixVType |
| |
| typedef Base::SingularValuesType | SingularValuesType |
| |
| typedef internal::plain_row_type< MatrixType >::type | RowType |
| |
| typedef internal::plain_col_type< MatrixType >::type | ColType |
| |
| typedef Matrix< Scalar, DiagSizeAtCompileTime, DiagSizeAtCompileTime, MatrixOptions, MaxDiagSizeAtCompileTime, MaxDiagSizeAtCompileTime > | WorkMatrixType |
| |
| enum | |
| |
| typedef internal::traits< JacobiSVD< _MatrixType, QRPreconditioner > >::MatrixType | MatrixType |
| |
| typedef MatrixType::Scalar | Scalar |
| |
| typedef NumTraits< typenameMatrixType::Scalar >::Real | RealScalar |
| |
| typedef Eigen::internal::traits< SVDBase >::StorageIndex | StorageIndex |
| |
| typedef Eigen::Index | Index |
| |
| typedef Matrix< Scalar, RowsAtCompileTime, RowsAtCompileTime, MatrixOptions, MaxRowsAtCompileTime, MaxRowsAtCompileTime > | MatrixUType |
| |
| typedef Matrix< Scalar, ColsAtCompileTime, ColsAtCompileTime, MatrixOptions, MaxColsAtCompileTime, MaxColsAtCompileTime > | MatrixVType |
| |
| typedef internal::plain_diag_type< MatrixType, RealScalar >::type | SingularValuesType |
| |
| enum | |
| |
| typedef EigenBase< SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > > > | Base |
| |
| typedef internal::traits< SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > > >::Scalar | Scalar |
| |
| typedef Scalar | CoeffReturnType |
| |
| typedef internal::add_const< Transpose< constDerived > >::type | ConstTransposeReturnType |
| |
| typedef internal::conditional< NumTraits< Scalar >::IsComplex, CwiseUnaryOp< internal::scalar_conjugate_op< Scalar >, ConstTransposeReturnType >, ConstTransposeReturnType >::type | AdjointReturnType |
| |
| typedef Eigen::Index | Index |
| | The interface type of indices. More...
|
| |
| typedef internal::traits< SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > > >::StorageKind | StorageKind |
| |
|
| | JacobiSVD () |
| | Default Constructor. More...
|
| |
| | JacobiSVD (Index rows, Index cols, unsigned int computationOptions=0) |
| | Default Constructor with memory preallocation. More...
|
| |
| | JacobiSVD (const MatrixType &matrix, unsigned int computationOptions=0) |
| | Constructor performing the decomposition of given matrix. More...
|
| |
| JacobiSVD & | compute (const MatrixType &matrix, unsigned int computationOptions) |
| | Method performing the decomposition of given matrix using custom options. More...
|
| |
| JacobiSVD & | compute (const MatrixType &matrix) |
| | Method performing the decomposition of given matrix using current options. More...
|
| |
| bool | computeU () const |
| |
| bool | computeV () const |
| |
| Index | rows () const |
| |
| Index | cols () const |
| |
| Index | rank () const |
| |
| JacobiSVD< _MatrixType, QRPreconditioner > & | derived () |
| |
| const JacobiSVD< _MatrixType, QRPreconditioner > & | derived () const |
| |
| const MatrixUType & | matrixU () const |
| |
| const MatrixVType & | matrixV () const |
| |
| const SingularValuesType & | singularValues () const |
| |
| Index | nonzeroSingularValues () const |
| |
| Index | rank () const |
| |
| JacobiSVD< _MatrixType, QRPreconditioner > & | setThreshold (const RealScalar &threshold) |
| | 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. More...
|
| |
| JacobiSVD< _MatrixType, QRPreconditioner > & | setThreshold (Default_t) |
| | Allows to come back to the default behavior, letting Eigen use its default formula for determining the threshold. More...
|
| |
| RealScalar | threshold () const |
| | Returns the threshold that will be used by certain methods such as rank(). More...
|
| |
| bool | computeU () const |
| |
| bool | computeV () const |
| |
| Index | rows () const |
| |
| Index | cols () const |
| |
| EIGEN_DEVICE_FUNC ComputationInfo | info () const |
| | Reports whether previous computation was successful. More...
|
| |
| void | _solve_impl (const RhsType &rhs, DstType &dst) const |
| |
| void | _solve_impl_transposed (const RhsType &rhs, DstType &dst) const |
| |
| | SolverBase () |
| | Default constructor. More...
|
| |
| | ~SolverBase () |
| |
| const Solve< SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > >, Rhs > | solve (const MatrixBase< Rhs > &b) const |
| |
| ConstTransposeReturnType | transpose () const |
| |
| AdjointReturnType | adjoint () const |
| |
| EIGEN_DEVICE_FUNC SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > > & | derived () |
| |
| EIGEN_DEVICE_FUNC const SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > > & | derived () const |
| |
| EIGEN_DEVICE_FUNC SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > > & | derived () |
| |
| EIGEN_DEVICE_FUNC const SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > > & | derived () const |
| |
| EIGEN_DEVICE_FUNC SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > > & | const_cast_derived () const |
| |
| EIGEN_DEVICE_FUNC const SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > > & | const_derived () const |
| |
| EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index | rows () const EIGEN_NOEXCEPT |
| |
| EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index | cols () const EIGEN_NOEXCEPT |
| |
| EIGEN_DEVICE_FUNC EIGEN_CONSTEXPR Index | size () const EIGEN_NOEXCEPT |
| |
| EIGEN_DEVICE_FUNC void | evalTo (Dest &dst) const |
| |
| EIGEN_DEVICE_FUNC void | addTo (Dest &dst) const |
| |
| EIGEN_DEVICE_FUNC void | subTo (Dest &dst) const |
| |
| EIGEN_DEVICE_FUNC void | applyThisOnTheRight (Dest &dst) const |
| |
| EIGEN_DEVICE_FUNC void | applyThisOnTheLeft (Dest &dst) const |
| |
template<typename _MatrixType, int QRPreconditioner>
class Eigen::JacobiSVD< _MatrixType, QRPreconditioner >
Two-sided Jacobi SVD decomposition of a rectangular matrix.
- Template Parameters
-
| _MatrixType | the type of the matrix of which we are computing the SVD decomposition |
| QRPreconditioner | this optional parameter allows to specify the type of QR decomposition that will be used internally for the R-SVD step for non-square matrices. See discussion of possible values below. |
SVD decomposition consists in decomposing any n-by-p matrix A as a product
\[ A = U S V^* \]
where U is a n-by-n unitary, V is a p-by-p unitary, and S is a n-by-p real positive matrix which is zero outside of its main diagonal; the diagonal entries of S are known as the singular values of A and the columns of U and V are known as the left and right singular vectors of A respectively.
Singular values are always sorted in decreasing order.
This JacobiSVD decomposition computes only the singular values by default. If you want U or V, you need to ask for them explicitly.
You can ask for only thin U or V to be computed, meaning the following. In case of a rectangular n-by-p matrix, letting m be the smaller value among n and p, there are only m singular vectors; the remaining columns of U and V do not correspond to actual singular vectors. Asking for thin U or V means asking for only their m first columns to be formed. So U is then a n-by-m matrix, and V is then a p-by-m matrix. Notice that thin U and V are all you need for (least squares) solving.
Here's an example demonstrating basic usage:
Output:
This JacobiSVD class is a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The downside is that it's slower than bidiagonalizing SVD algorithms for large square matrices; however its complexity is still \( O(n^2p) \) where n is the smaller dimension and p is the greater dimension, meaning that it is still of the same order of complexity as the faster bidiagonalizing R-SVD algorithms. In particular, like any R-SVD, it takes advantage of non-squareness in that its complexity is only linear in the greater dimension.
If the input matrix has inf or nan coefficients, the result of the computation is undefined, but the computation is guaranteed to terminate in finite (and reasonable) time.
The possible values for QRPreconditioner are:
- ColPivHouseholderQRPreconditioner is the default. In practice it's very safe. It uses column-pivoting QR.
- FullPivHouseholderQRPreconditioner, is the safest and slowest. It uses full-pivoting QR. Contrary to other QRs, it doesn't allow computing thin unitaries.
- HouseholderQRPreconditioner is the fastest, and less safe and accurate than the pivoting variants. It uses non-pivoting QR. This is very similar in safety and accuracy to the bidiagonalization process used by bidiagonalizing SVD algorithms (since bidiagonalization is inherently non-pivoting). However the resulting SVD is still more reliable than bidiagonalizing SVDs because the Jacobi-based iterarive process is more reliable than the optimized bidiagonal SVD iterations.
- NoQRPreconditioner allows not to use a QR preconditioner at all. This is useful if you know that you will only be computing JacobiSVD decompositions of square matrices. Non-square matrices require a QR preconditioner. Using this option will result in faster compilation and smaller executable code. It won't significantly speed up computation, since JacobiSVD is always checking if QR preconditioning is needed before applying it anyway.
- See also
- MatrixBase::jacobiSvd()
Public Member Functions inherited from Eigen::SVDBase< JacobiSVD< _MatrixType, QRPreconditioner > >
1.9.4