Eigenvalues

For any square matrix $A$, there exists at least a scalar $\lambda \in \mathbb{C}$ and $x\in {\mathbb{C}}^n$ such that $Ax=\lambda x$. They are called an eigenvalue and eigenvector of $A$.

The computational cost of computing all the eigenvalues is $O(n^3)$. This will be covered in greater detail in future chapters.

Existence of eigenvalues §

We prove the existence of at least one eigenvalue and eigenvector $Ax=\lambda x$ with $\lambda \in \mathbb{C}$.

Consider the sequence $x,$ $Ax,$ …, $A^{n}x.$ We have $n+1$ vectors so they are linearly dependent. So there exists $a_0,$ …, $a_n$ such that

$$a_{0}x+\cdots +a_n{A}^{n}x=0$$

Using the fundamental theorem of algebra we can find $c$ and ${\lambda }_i$ such that

$$a_0+a_{1}z+\cdots +a_n{z}^n=c(z-{\lambda }_1)\cdots (z-{\lambda }_m),c\ne 0$$

This shows that

$$(A-{\lambda }_{1}I)\cdots (A-{\lambda }_{m}I)x=0$$

If $A-{\lambda }_{i}I$ is non-singular for all $i$ then

$$(A-{\lambda }_{1}I)\cdots (A-{\lambda }_{m}I)$$

is non-singular. This is a contradiction. So there exists an $i$ such that $A-{\lambda }_{i}I$ is singular. So ${\lambda }_i$ is an eigenvalue.

Finding all the eigenvalues §

We show that there exists a non-singular matrix $X$ and upper triangular matrix $T$ such that

$$A=XT{X}^{-1}$$

We will show that all the eigenvalues of $A$ can be found along the diagonal of $T$.

Let’s prove the existence of $X$ and $T$.

We use a proof by induction on the matrix size $n$. The result can be verified when $n=1.$

Assume that the result is true for matrices of size less than $n.$

We know that there exists $x$ and $\lambda$ such that $Ax=\lambda x.$ Denote by

$$U=\text{range}(A-\lambda I)$$

Since $A-\lambda I$ is singular $\dim (U)<n$.

Moreover, $U$ is stable, that is $AU\subset U$. To prove this, take $u\in U$. Then:

$$Au=(A-\lambda I)u+\lambda u$$

Since $(A-\lambda I)u\in U$, we have $Au\in U$. So $U$ is stable.

We can consider the restriction of $A$ to the subspace $U$. Using the induction hypothesis, there is a basis $u_1,$ …, $u_m$ such that $A$ restricted to $U$ is upper triangular in that basis.

Let’s extend this set of independent vectors to get a full basis of ${\mathbb{R}}^n$:

$$X=[u_1,\dots ,u_m,v_1,\dots ,v_{n-m}]$$

Take a $v_k$ and

$$A{v}_k=(A-\lambda I)v_k+\lambda v_k$$

So $A{v}_k\in \text{span}(u_1,\dots ,u_m,v_k)$. This proves that

$$X^{-1}AX=T$$

where $T$ is upper triangular. So $A=XT{X}^{-1}$.

The eigenvalues of $A$ are equal to the eigenvalues of $T$. Moreover, we can prove that $T-\lambda I$ is singular if and only if this matrix has a 0 on the diagonal. This implies that all the eigenvalues of $T$ are on its diagonal, and there are $n$ of them. Note that these eigenvalues might be repeated.

Matrix-vector and matrix-matrix product, Invertible matrix