I'm solving the constrained least squares problem $\underset{u \in [0,1]^N} \min \lVert Au-f \rVert_2^2$ with $u \in \mathbb{R}^N$, $A \in \mathbb{R}^{N \times N}$ and $f \in \mathbb{R}^N$ by using the following modified Jacobi-Method:
$u^{k+1}=\max \{ \min \{ D^{-1} (f - R u^k), 1 \},0 \}$ where $A=D+R$, $D$ is the diagonal of $A$. The $\max$ and $\min$ are applied element-wise, so what I'm doing is basically the standard Jacobi-Method but with projection to the constraint after each iteration.
I need to find out under which conditions on $A$ and $f$ this actually converges to the correct solution of the above problem. Any hints on related literature would be appreciated!