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  /external/eigen/Eigen/src/Eigen2Support/
SVD.h 18 * \class SVD
20 * \brief Standard SVD decomposition of a matrix and associated features
22 * \param MatrixType the type of the matrix of which we are computing the SVD decomposition
24 * This class performs a standard SVD decomposition of a real matrix A of size \c M x \c N
28 * \sa MatrixBase::SVD()
30 template<typename MatrixType> class SVD
51 SVD() {} // a user who relied on compiler-generated default compiler reported problems with MSVC in 2.0.7
53 SVD(const MatrixType& matrix)
69 SVD& sort();
89 /** Computes / recomputes the SVD decomposition A = U S V^* of \a matri
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  /external/eigen/Eigen/src/SVD/
JacobiSVD.h 22 /*** QR preconditioners (R-SVD)
24 *** Their role is to reduce the problem of computing the SVD to the case of a square matrix.
25 *** This approach, known as R-SVD, is an optimization for rectangular-enough matrices, and is a requirement for
77 void allocate(const JacobiSVD<MatrixType, FullPivHouseholderQRPreconditioner>& svd)
79 if (svd.rows() != m_qr.rows() || svd.cols() != m_qr.cols())
81 m_qr = FullPivHouseholderQR<MatrixType>(svd.rows(), svd.cols());
83 if (svd.m_computeFullU) m_workspace.resize(svd.rows())
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