Lines Matching defs:JacobiSVD
26 *** JacobiSVD which by itself is only able to work on square matrices.
55 void allocate(const JacobiSVD<MatrixType, QRPreconditioner>&) {}
56 bool run(JacobiSVD<MatrixType, QRPreconditioner>&, const MatrixType&)
77 void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
87 bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
122 void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
133 bool run(JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
161 void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd)
172 bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
214 void allocate(const JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd)
226 bool run(JacobiSVD<MatrixType, ColPivHouseholderQRPreconditioner>& svd, const MatrixType& matrix)
261 void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd)
272 bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix)
313 void allocate(const JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd)
325 bool run(JacobiSVD<MatrixType, HouseholderQRPreconditioner>& svd, const MatrixType& matrix)
354 *** JacobiSVD consists in performing a series of 2x2 SVD subproblems
360 typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
368 typedef JacobiSVD<MatrixType, QRPreconditioner> SVD;
442 * \class JacobiSVD
458 * This JacobiSVD decomposition computes only the singular values by default. If you want \a U or \a V, you need to ask for them explicitly.
469 * This JacobiSVD class is a two-sided Jacobi R-SVD decomposition, ensuring optimal reliability and accuracy. The downside is that it's slower than
486 * JacobiSVD decompositions of square matrices. Non-square matrices require a QR preconditioner. Using this option will result in
487 * faster compilation and smaller executable code. It won't significantly speed up computation, since JacobiSVD is always checking
490 * \sa MatrixBase::jacobiSvd()
493 class JacobiSVD : public SVDBase<_MatrixType>
527 * perform decompositions via JacobiSVD::compute(const MatrixType&).
529 JacobiSVD()
538 * \sa JacobiSVD()
540 JacobiSVD(Index rows, Index cols, unsigned int computationOptions = 0)
556 JacobiSVD(const MatrixType& matrix, unsigned int computationOptions = 0)
595 inline const internal::solve_retval<JacobiSVD, Rhs>
598 eigen_assert(this->m_isInitialized && "JacobiSVD is not initialized.");
599 eigen_assert(SVDBase<MatrixType>::computeU() && SVDBase<MatrixType>::computeV() && "JacobiSVD::solve() requires both unitaries U and V to be computed (thin unitaries suffice).");
600 return internal::solve_retval<JacobiSVD, Rhs>(*this, b.derived());
621 void JacobiSVD<MatrixType, QRPreconditioner>::allocate(Index rows, Index cols, unsigned int computationOptions)
628 "JacobiSVD: can't compute thin U or thin V with the FullPivHouseholderQR preconditioner. "
640 JacobiSVD<MatrixType, QRPreconditioner>::compute(const MatrixType& matrix, unsigned int computationOptions)
738 struct solve_retval<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
739 : solve_retval_base<JacobiSVD<_MatrixType, QRPreconditioner>, Rhs>
741 typedef JacobiSVD<_MatrixType, QRPreconditioner> JacobiSVDType;
771 * \sa class JacobiSVD
774 JacobiSVD<typename MatrixBase<Derived>::PlainObject>
775 MatrixBase<Derived>::jacobiSvd(unsigned int computationOptions) const
777 return JacobiSVD<PlainObject>(*this, computationOptions);
