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Order the interior grid values into a vector. The coefficient matrix has diagonal $4+c{h}^2$ and off-diagonal entries $-1$ at horizontal and vertical neighbours, with boundary values already zero. Every row is strictly diagonally dominant because $c>0$. The standard convergence theorem for strict diagonal dominance gives convergence of both the Jacobi method and the Gauss-Seidel method. The matrix is also symmetric positive definite.

For $D=(4+c{h}^2)I$, the weighted Jacobi method is $u^{(r+1)}=u^{(r)}+\omega D^{-1}(b-A{u}^{(r)})$, with iteration matrix $H_{\omega }=I-\omega D^{-1}A$. The sine-product grid eigenvectors giveThese weighted Jacobi eigenvalues for a square Dirichlet grid determine convergence. All eigenvalues of $A$ are positive. Thus for fixed $h$ convergence requires $0<\omega <2(4+c{h}^2)/a_{\max }$, where $a_{\max }=8{\cos }^2(\pi h/2)+c{h}^2$. As $h\downarrow 0$, this upper bound tends to $1$ from above. Any fixed $\omega >1$ therefore fails on sufficiently fine grids; $\omega \le 0$ also fails. Consequently $0<\omega \le 1$ is necessary for all mesh sizes, and it is sufficient, since $a_{\max }<2(4+c{h}^2)$ for every finite grid.

With $\mathbf{u}=(u_1,\dots ,u_n{)}^T$ and $\mathbf{b}=h^2(f_1,\dots ,f_n{)}^T$, the system isIts diagonal part is $D=-2I$. The weighted Jacobi method, also called the Relaxed Jacobi method, isComponentwise,with $u_0^{(\nu )}=u_{n+1}^{(\nu )}=0$.

Subtracting the fixed point from the weighted Jacobi method givesPut $x=\pi /(n+1)$. Acting on the supplied sine eigenvector givesThereforeand the corresponding eigenvalues of the iteration matrix are