Positive semi-definition of Diagonal Dominant Matrix (with non-negative diagonal) is an important property in numeric linear algebra. Here I record two prooves, one following the idea of Gershgorin Circle Theorem , the other is a proof for the weakened version of strictly diagonal dominance.
A diagonal dominant matrix A = { a i j } i , j = 1 n A=\{a_{ij}\}_{i,j=1}^n A = { a ij } i , j = 1 n satisties
∣ a i i ∣ ≥ ∑ j ≠ i ∣ a i j ∣ , ∀ i \begin{align}
\vert a_{ii}\vert \geq \sum_{j\neq i}\vert a_{ij}\vert ,\quad \forall i
\end{align} ∣ a ii ∣ ≥ j = i ∑ ∣ a ij ∣ , ∀ i
§ Gershgorin Circle Theorem
The eigen vector x x x with eigen value λ \lambda λ , say x i x_i x i is the element with largest absolute value.
∑ j = 1 n a i j x j = λ x i ⇒ ∣ ( λ − a i i ) ∣ = ∣ ∑ j ≠ i a i j x j x i ∣ ≤ ∑ j ≠ i ∣ a i j ∣ ∣ x j x i ∣ ≤ ∑ j ≠ i ∣ a i j ∣ \begin{align}
\sum_{j=1}^na_{ij}x_{j}=\lambda x_i\Rightarrow \vert (\lambda -a_{ii})\vert =&\left\vert \sum_{j\neq i}\dfrac{a_{ij}x_j}{x_i}\right\vert \leq \sum_{j\neq i} \left\vert a_{ij}\right\vert \left\vert \dfrac{x_{j}}{x_i}\right\vert \\
\leq& \sum_{j\neq i}\vert a_{ij}\vert
\end{align} j = 1 ∑ n a ij x j = λ x i ⇒ ∣ ( λ − a ii ) ∣ = ≤ j = i ∑ x i a ij x j ≤ j = i ∑ ∣ a ij ∣ x i x j j = i ∑ ∣ a ij ∣
i.e. λ \lambda λ lies in one of the Gershgorin disks:
λ ∈ ⋓ i = 1 n D i s c ( a i i ; R i = ∑ j ≠ i ∣ a i j ∣ ) \begin{align}
\lambda \in \Cup_{i=1}^n \mathrm{Disc}\left( a_{ii}; R_i=\sum_{j\neq i}\vert a_{ij}\vert \right)
\end{align} λ ∈ ⋓ i = 1 n Disc a ii ; R i = j = i ∑ ∣ a ij ∣
Immediately we would find that for diagonal dominant matrix with non-negative diagonal elements, all eigen vectors would be non-negative, thus A A A is positive semi-definite.
a i i − λ ≤ ∣ λ − a i i ∣ ≤ ∑ j ≠ i ∣ a i j ∣ ⇒ λ ≥ ∣ a i i ∣ − ∑ j ≠ i ∣ a i j ∣ ≥ 0 \begin{align}
a_{ii}-\lambda \leq \vert \lambda -a_{ii}\vert \leq \sum_{j\neq i}\vert a_{ij}\vert \Rightarrow \ \lambda \geq \vert a_{ii}\vert -\sum_{j\neq i}\vert a_{ij}\vert \geq 0
\end{align} a ii − λ ≤ ∣ λ − a ii ∣ ≤ j = i ∑ ∣ a ij ∣ ⇒ λ ≥ ∣ a ii ∣ − j = i ∑ ∣ a ij ∣ ≥ 0
§ Another Interesting Proof for Strictly Diagonal Dominant
Strictly Diagonal Dominant Matrix:
∣ a i i ∣ > ∑ j ≠ i ∣ a i j ∣ , ∀ i \begin{align}
\vert a_{ii}\vert >\sum_{j\neq i}\vert a_{ij}\vert ,\quad \forall i
\end{align} ∣ a ii ∣ > j = i ∑ ∣ a ij ∣ , ∀ i
A strictly diagonal dominant matrix is non-singular: Assume there ∃ x , s . t . A x = 0 \exists x, s.t. Ax=0 ∃ x , s . t . A x = 0 , with x i x_i x i the element with largest absolute value, then follows similar idea as in Gershgorin thm.:
∑ j = 1 n a i j x j = 0 ⇒ ∣ a i i ∣ = ∣ ∑ j ≠ i a i j x j x i ∣ ≤ ∑ j ≠ i ∣ a i j ∣ \begin{align}
\sum_{j=1}^na_{ij}x_j=0\Rightarrow \vert a_{ii}\vert =\left\vert \sum_{j\neq i} \dfrac{a_{ij}x_j}{x_i} \right\vert \leq \sum_{j\neq i}\vert a_{ij}\vert
\end{align} j = 1 ∑ n a ij x j = 0 ⇒ ∣ a ii ∣ = j = i ∑ x i a ij x j ≤ j = i ∑ ∣ a ij ∣
which contradicts with strict diagonal dominance, thus A A A is non-singular. i.e. ∣ A ∣ ≠ 0 \vert A\vert \neq 0 ∣ A ∣ = 0
Further consider matrix A + τ I A+\tau I A + τ I , which is also strictly diagonal dominant, thus ∣ A + τ I ∣ ≠ 0 \vert A+\tau I\vert \neq 0 ∣ A + τ I ∣ = 0 . Consider the function
ϕ ( τ ) : = ∣ A + τ I ∣ ≠ 0 , ∀ τ ≥ 0 \begin{align}
\phi (\tau):=\vert A+\tau I\vert \neq 0,\quad \forall \tau\geq 0
\end{align} ϕ ( τ ) := ∣ A + τ I ∣ = 0 , ∀ τ ≥ 0
Naturally ϕ ( τ ) \phi (\tau) ϕ ( τ ) should be continuous, and lim τ → ∞ ϕ ( τ ) > 0 \lim_{\tau\to\infty}\phi(\tau)>0 lim τ → ∞ ϕ ( τ ) > 0 , which would indicate that
ϕ ( 0 ) = ∣ A ∣ > 0 \begin{align}
\phi (0)=\vert A\vert >0
\end{align} ϕ ( 0 ) = ∣ A ∣ > 0
Notice that all the sequential principal minor D i D_i D i s of A A A are still strictly diagonal dominant, thus ∣ D i ∣ > 0 \vert D_i\vert >0 ∣ D i ∣ > 0 , ∀ i = 1 , 2 , … , n \forall i=1,2,\ldots,n ∀ i = 1 , 2 , … , n . Thus A A A is positive definite.