Derivation and example of the PreCholesky Factorization.

This method is illustrated only to show that the Crout method can be modified to obtain computed values similar to the Cholesky factorization.
A more efficient method is the Cholesky subroutine for matrices that are real, symmetric and positive definite.

Consider the matrix product  A = LU where L is lower triangular and U is upper triangular.
Use the rule for finding the element [Graphics:../Images/CholeskyMod_gr_46.gif] to successively compute the entries in U and L.

[Graphics:../Images/CholeskyMod_gr_47.gif]

 

[Graphics:../Images/CholeskyMod_gr_48.gif]

 

[Graphics:../Images/CholeskyMod_gr_49.gif]

 

 

Compute the first row of  U  and the first column of  L.

[Graphics:../Images/CholeskyMod_gr_50.gif]

 

Compute the second row of  U  and the second column of  L.

[Graphics:../Images/CholeskyMod_gr_51.gif]

 

Compute the third row of  U  and the third column of  L.

[Graphics:../Images/CholeskyMod_gr_52.gif]

 

Compute the k-th row of  U  and the k-th column of  L.

[Graphics:../Images/CholeskyMod_gr_53.gif]

 

The above derivation leads to the following PreCholesky subroutine.

[Graphics:../Images/CholeskyMod_gr_54.gif]

Implementation of the PreCholesky subroutine will produce an LU factorization where [Graphics:../Images/CholeskyMod_gr_55.gif] .

 

Exercise 1 (c).  Find the A = LU  factorization for the matrix  [Graphics:../Images/CholeskyMod_gr_56.gif]  using the PreCholesky subroutine.

Solution.

[Graphics:../Images/CholeskyMod_gr_57.gif]
[Graphics:../Images/CholeskyMod_gr_58.gif] [Graphics:../Images/CholeskyMod_gr_59.gif] [Graphics:../Images/CholeskyMod_gr_60.gif] [Graphics:../Images/CholeskyMod_gr_61.gif] [Graphics:../Images/CholeskyMod_gr_62.gif] [Graphics:../Images/CholeskyMod_gr_63.gif] [Graphics:../Images/CholeskyMod_gr_64.gif]

But what is the purpose of a having this third method ?

    In the special case that  A  is known to be real, symmetric and positive definite there is an advantage to the Cholesky method.  If  A  is real, symmetric and positive definite then  U is the transpose of  L  and both do not need to be computed. Hence there will be a tremendous saving of computing effort if this property is considered.  BUT the Cholesky can only be used if  A  is real, symmetric and positive definite.

 

 

 

 

 

 

 

 

 

 

 

 

(c) John H. Mathews 2003