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cholupdate

Rank 1 update to Cholesky factorization

Syntax

R1 = cholupdate(R,x)
R1 = cholupdate(R,x,'+')
R1 = cholupdate(R,x,'-')
[R1,p] = cholupdate(R,x,'-')

Description

R1 = cholupdate(R,x) where R = chol(A) is the original Cholesky factorization of A, returns the upper triangular Cholesky factor of A + x*x', where x is a column vector of appropriate length. cholupdate uses only the diagonal and upper triangle of R. The lower triangle of R is ignored.

R1 = cholupdate(R,x,'+') is the same as R1 = cholupdate(R,x).

R1 = cholupdate(R,x,'-') returns the Cholesky factor of A - x*x'. An error message reports when R is not a valid Cholesky factor or when the downdated matrix is not positive definite and so does not have a Cholesky factorization.

[R1,p] = cholupdate(R,x,'-') will not return an error message. If p is 0, R1 is the Cholesky factor of A - x*x'. If p is greater than 0, R1 is the Cholesky factor of the original A. If p is 1, cholupdate failed because the downdated matrix is not positive definite. If p is 2, cholupdate failed because the upper triangle of R was not a valid Cholesky factor.

Examples

A = pascal(4)
A =

     1     1     1     1
     1     2     3     4
     1     3     6    10
     1     4    10    20

R = chol(A)
R =

     1     1     1     1
     0     1     2     3
     0     0     1     3
     0     0     0     1
x = [0 0 0 1]';

This is called a rank one update to A since rank(x*x') is 1:

A + x*x' 
ans =
     1     1     1     1
     1     2     3     4
     1     3     6    10
     1     4    10    21

Instead of computing the Cholesky factor with R1 = chol(A + x*x'), we can use cholupdate:

R1 = cholupdate(R,x)
R1 =
    1.0000    1.0000    1.0000    1.0000
         0    1.0000    2.0000    3.0000
         0         0    1.0000    3.0000
         0         0         0    1.4142

Next destroy the positive definiteness (and actually make the matrix singular) by subtracting 1 from the last element of A. The downdated matrix is:

A - x*x'
ans =
 
     1     1     1     1
     1     2     3     4
     1     3     6    10
     1     4    10    19

Compare chol with cholupdate:

R1 = chol(A-x*x')
Error using chol
Matrix must be positive definite.
R1 = cholupdate(R,x,'-')
Error using cholupdate
Downdated matrix must be positive definite.

However, subtracting 0.5 from the last element of A produces a positive definite matrix, and we can use cholupdate to compute its Cholesky factor:

x = [0 0 0 1/sqrt(2)]';
R1 = cholupdate(R,x,'-') 
R1 =
    1.0000    1.0000    1.0000    1.0000
         0    1.0000    2.0000    3.0000
         0         0    1.0000    3.0000
         0         0         0    0.7071

Tips

cholupdate works only for full matrices.

Extended Capabilities

See Also

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Introduced before R2006a