Diagonalizing a matrix comes up frequently for me when wanting to diagonalize the variance matrix of a multivariate normal to derive conditional distributions. I prefer to proceed by doing an LDU Decomposition and leaving it in terms of the Schur complement as I find it easier to remember. Consider some matrix V and partition it as follows

Consider right and left multiplying this matrix by

The matrices really correspond to the L and U matrices in the LDU decomposition, noting that the matrix on the right or left is just the transpose of the other. Define the Schur complement of the matrix V with respect to the block V22 as

then working through the above multiplication it follows that

This result is useful in deriving the Shermanâ€“Morrisonâ€“Woodbury matrix inverse formula/identity, otherwise known as the matrix inversion lemma.

One can now simply multiply the above equation by the corresponding inverse matrices to obtain the LDU decomposition of the matrix V. The inverse matrices are easy

So finally

Notice how this is a UDL factorization. It is entirely possible to do things a little differently and result with the LDU decomposition

Consider right and left multiplying this matrix by

where

i.e. the Schur complement of matrix V with respect to block 11. So finally