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Description
Class Summary  
Cholesky  Computes the cholesky decomposition of a symmetric positivedefinite Matrix and can therefore test whether a symmetric matrix is positivedefinite. 
Householder  Computes for a matrix A the matrices Q and R complying A=Q·R. 
LR  Computes for a square matrix A the matrices L and R complying A=L·R. 
PLR  Computes for a square matrix A the matrices P, L and R complying P·A=L·R. 
QR  Computes for a square matrix A the matrices Q and R complying A=Q·R. 
TestCholesky  
TestHouseholder  
TestLR  
TestPLR  
TestQR  
TestTridiagonal  
Tridiagonal  Transforms a symmetric matrix into triadiagonal form. 
Unmerge  Seperates merged matrices. 
Algorithms related to decomposition of matrices and their determinants.
All algorithms are offered in an efficient version (using as few resources as possible but having sometimes difficult arguments) and in an inefficient version (that has therefore easy to use arguments).
The following table gives an overview of the different decomposition algorithms (sorted by speed  fastest first, slowest last):
class  advantages  disadvantages  minimal resources  algorithm 


· very fast · no additional resources · when successful gives determinant 
· does not decompose all regular matrices · no control on condition of triangular matrices 
· one matrix 
Gauss 

· fast · no additional resources · gives determinant 
· no control on condition of triangular matrices 
· one matrix · one vector 
Gauss with pivoting 

· triangular matrix has never worse condition than original matrix · gives determinant 
· not very fast · sometimes not goodnatured 
· two matrices 
GramSchmidt 

· always goodnatured · few additional resources · works on nonsquare matrices (more rows than columns) · gives determinant 
· not very fast 
· one matrix · one vector · one temporary vector 
Householder 


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