Matrix decompositions

Classes to perform matrix decompositions, used to solve linear equations and provide information about matrices. More...

Classes

class  TooN::Cholesky< Size, Precision >
 Decomposes a positive-semidefinite symmetric matrix A (such as a covariance) into L*D*L^T, where L is lower-triangular and D is diagonal. More...
class  TooN::LU< Size, Precision >
 Performs LU decomposition and back substitutes to solve equations. More...
struct  TooN::SQSVD< Size, Precision >
 version of SVD forced to be square princiapally here to allow use in WLS More...
class  TooN::SymEigen< Size, Precision >
 Performs eigen decomposition of a matrix. More...
class  TooN::SVD< Rows, Cols, Precision >
 Performs SVD and back substitute to solve equations. More...

Functions

template<int R, int C, class Precision, class Base>
void TooN::gauss_jordan (Matrix< R, C, Precision, Base > &m)

Detailed Description

Classes to perform matrix decompositions, used to solve linear equations and provide information about matrices.

These are wrappers for functionality provided by the LAPACK library.


Function Documentation

template<int R, int C, class Precision, class Base>
void TooN::gauss_jordan ( Matrix< R, C, Precision, Base > &  m  ) 

Perform Gauss-Jordan reduction on m.

If m is of the form $[A | I ]$, then after reduction, m will be $[ I | A^{-1}]$. There is no restriction on the input, in that the matrix augmenting A does not need to be I, or square. The reduction is performed using elementary row operations and partial pivoting.

Parameters:
m The matrix to be reduced.


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