I have a matrix which is already up-triangular like :
$\begin{bmatrix}A & B\\ 0 & C\end{bmatrix}$
in which A and C is up-triangular block, now I add a block row then get :
$\begin{bmatrix}A & B\\ 0 & C \\ D & E\end{bmatrix}$
I want to apply a qr decomposition to keep this matrix up-triangular while keep A is not changed:
$ Q*\begin{bmatrix}A & B\\ 0 & C \\ D & E\end{bmatrix} = \begin{bmatrix} A & F \\ 0 & G \\ 0 & 0 \end{bmatrix}$
I know this could be achieved by gauss elimination method, because A is already up-triangular, we can modify the D col by col according the data from A, this could also keep A not changed.
but col by col operation is not computation efficient. I know the givens rotation or householder reflection is the common method for QR, but I think they can't keep A not changed, so any more efficient method than gauss elimination ?
and I have an advance question, I have an up-triangular matrix like, which means a linear solve system :
$ \begin{bmatrix} A & B &C \\ 0 & D & E \\ 0 & 0 & F \end{bmatrix} $
A,D,F is up-triangular block. with some variables permute I can get another linear system:
$ \begin{bmatrix} D & 0 & E \\ B & A & C \\ 0 & 0 & F \end{bmatrix} $
I want apply a qr decomposition while keep A not changed, which mean:
$ Q* \begin{bmatrix} D & 0 & E \\ B & A & C \\ 0 & 0 & F \end{bmatrix} = \begin{bmatrix} G & H & I \\ 0 & A & J \\ 0 & 0 & K \end{bmatrix}$
I think even gauss elimination can't achieve this purpose. is there any solution :)?