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SVD module

This module provides SVD decomposition for matrices (both real and complex). Two decomposition algorithms are provided:

JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very slow for larger ones.
BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast for large problems. These decompositions are accessible via the respective classes and following MatrixBase methods:
MatrixBase::jacobiSvd()
MatrixBase::bdcSvd()

#include <Eigen/SVD>


class	Eigen::BDCSVD< _MatrixType >
	class Bidiagonal Divide and Conquer SVD More...

class	Eigen::JacobiSVD< _MatrixType, QRPreconditioner >
	Two-sided Jacobi SVD decomposition of a rectangular matrix. More...

class	Eigen::SVDBase< Derived >
	Base class of SVD algorithms. More...