Are there any special considerations when using the [Woodbury matrix identity](http://en.wikipedia.org/wiki/Woodbury_matrix_identity) numerically?  What is the best metric for numerical stability in this case?  Can anyone point me to a good reference?

The special case of the identity that I'm using is:
$ (A + UBU^T)^{-1} = A^{-1} - A^{-1}U(B^{-1} + U^TA^{-1}U)^{-1}U^TA^{-1}$

I'm using the identity to speed up a matrix inverse.  In my case $A$ is diagonal, and $U$ is rectangular by a factor that varies between 10 and 200.  I'm using a sparse form for $U$.

This is failing catastrophically for me.  Normal inversion seems stable for condition number of the LHS well beyond $10^{10}$, while the identity is failing around condition number of $10^7$ (everything in double precision).  However, condition number of the LHS doesn't seem to be the best metric for when things fail, as I can arrange cases where the failure happens at much higher or lower condition number.

Thanks in advance!