# Showing that the Jacobi method converges for diagonally dominant matrix?

We have a matrix A that is diagonally dominant by rows $|a_{ii}|>\sum_{j\neq i} |a_{ij}|$ and I want to show that it will converge by showing that this statement is true $\lVert e^{(k)}\rVert_\infty \leq \lVert T\rVert^k_\infty \lVert e^{(0)}\rVert_\infty$, where $e^{(k)} = x^{(k)} - x^\ast$ and $x^\ast$ is the exact solution to $Ax=b$. I have already proved $\lVert T^k x\rVert \leq \lVert T\rVert^k \lVert x\rVert$ that statement but I have not sure how to apply it to help prove the convergence.

We know that $e^{(k+1)} = x^{(k+1)} - x^*$, so now consider the norms and use the relation $Tx^k = x^{(k+1)}$ so that we have: $$\begin{equation}\label{star} \|x^{(k+1)} - x^*\| = \|Tx^k - Tx^*\| = \|T(x^(k) - x^*)\| \leq \|T\| \|e^k\| \end{equation}$$
Then by submultiplicativity of operator norms, we have using $\|T^k\| \leq \|T\|^k$, and inductively (by applying the above equation $k$ times) we have $\|e^{(k+1)}\| \leq \|T\|^k \|e^{(0)}$.
Now for convergence, you need to show something about $\|T\|$...
• For the convergence would it be proving that $\|T\|$ is always $<1$? – user1775500 Oct 29 '17 at 19:03
• Also why do you start with $e^{(k+1)}$ instead of $e^k$? – user1775500 Oct 29 '17 at 20:04
• @user1775500: started with $e^{(k+1)}$ out of habit and could have equally started with $e^{(k)}$ and used $x^{(k)} = Tx^{(k-1)}$ – jjjjjj Oct 29 '17 at 21:48
• and yes for $\|T\| < 1$. Consider: $\|T\|_{\infty} = \max_i \sum_{i\neq j} \frac{a_{ij}}{a_{ii}} < 1$ – jjjjjj Oct 29 '17 at 21:50