# sparsity of QR decomposition

Hi, everyone!

I have a sparse $n \times n$ matrix $A$ with $nnz(A)$ denoting the number of non-zero entries in $A$. Now I use QR factorization to decompose $A$ into an orthogonal matrix $Q$ and triangular matrix $R$. My problem is that : can we use some methods to control the sparsity of $Q$ satisfying $nnz(Q) \le nnz(A)$.

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What about the sparsity of $R$ wrt that of $A$ ? –  Mike Apr 17 '12 at 11:26

The typical case in which sparse QR factorization is used is where $A$ is of size $m$ by $n$, $m \gg n$, and $A$ has full column rank. In this situation, $Q$ would be $m$ by $m$ and typically dense with nonzeros, but the effect of multiplication by $Q$ can be achieved by using sequences of Givens rotations. So $Q$ isn't stored explicitly. It is also possible to order the columns of $A$ to reduce the nonzero fill-in in the $R$ matrix. –  Brian Borchers Apr 17 '12 at 13:32
If you want to use this sparse QR "factorization" to solve $\min \| Ax-b \|$, the procedure is to first apply the Givens rotations to reduce $A$ to $R$, possibly reordering the columns of $A$ to minimize fill-in in the $R$ matrix. As you do these Givens rotations you also apply them to $b$ to obtain $Q^{T}b$. Then you solve the triangular system $Rx=Q^{T}b$ to obtain the least squares solution. There is lots of available software for performing these computations. –  Brian Borchers Apr 17 '12 at 13:36
Also, if $A$ is invertible and $R$ is required to have positive diagonal, the $QR$ decomposition is unique, so you can't really control it. If you are looking for approximate decompositions, though, it's a whole different game.