12. Eigenvalues and eigenvectors


If you have tried to imagine a linear map of a plane, you usually imagine that it stretches or squeezes the plane along some directions. It would be nice to know whether a given linear map \varphi\colon V\to V (such a map, which the same domain and range, is called an endomorphism), it is actually of this type. In other words we would like to know whether there exists a non-zero vector v and a scalar \lambda, such that \varphi simply multiples v by \lambda (so it stretches or squeezes the space in the direction of v), so:

    \[\varphi(v)=\lambda v.\]

If v and \lambda have such properties, then v is said to be an eigenvector of \varphi and \lambda an eigenvalue.

Determining eigenvalues

Notice that if \lambda is an eigenvalue of a map \varphi and v is its eigenvector, then \varphi(v)-\lambda v=0. Therefore if M is a matrix of \varphi (in standard basis), then

    \[0=Mv-\lambda v=Mv-\lambda I v=(M-\lambda I)v\]

, where I is the identity matrix.

Since multiplication of a matrix by a vector gives a linear combination of its columns and v is a non-zero vector, we see that the columns of M-\lambda I can be non-trivially combined to get the zero vector! It is possible if and only if \det (M-\lambda I)=0.

How to find the eigenvalues of a map? Simply one needs to solve the equation \det (M-\lambda I)=0. E.g let \varphi (x,y,z)=(2x,x+y,-x+z). Then:



    \[M(\varphi)_{st}^{st}-\lambda I=\left[\begin{array}{ccc}2-\lambda&0&0\\1&1-\lambda&0\\-1&0&1-\lambda\end{array}\right].\]

So we have to solve the following:

    \[\det (M(\varphi)_{st}^{st}-\lambda I)=(2-\lambda)(1-\lambda)^2=0\]

And therefore the eigenvalues are: 2 and 1.


Now let’s find eigenvectors related to subsequent eigenvalues. Notice that since \varphi is a linear map, if v, v' are eigenvectors for an eigenvalue \lambda, then for any scalar a also av and v+v' are eigenvectors for \lambda. Therefore, the set of all eigenvectors for \lambda forms a linear subspace. Notice that v satisfies the equation

    \[(M-\lambda I)v=0\]

so the space of eigenvectors (i.e. eigenspace) for \lambda (denoted as V_{(\lambda)}) is given by the following system of equations:

    \[(M-\lambda I)v=0\]

and we can easily find its basis.

In our example, let us find a basis of V_{(1)}, so let \lambda=1. Then:

    \[M(\varphi)_{st}^{st}-\lambda I=\left[\begin{array}{ccc}1&0&0\\1&0&0\\-1&0&0\end{array}\right]\]

Therefore, we have the following system of equations:


The space of solutions is V_{(1)}=\{(0,y,z)\colon y,z\in\mathbb{R}\}, and its basis is (0,1,0),(0,0,1). Indeed, \varphi((0,1,0))=1\cdot (0,1,0) and \varphi((0,0,1))=1\cdot (0,0,1).

Let’s find a basis of V_{(2)}, so let \lambda=2. Then:

    \[M(\varphi)_{st}^{st}-\lambda I=\left[\begin{array}{ccc}0&0&0\\1&-1&0\\-1&0&-1\end{array}\right]\]

The system of equations:


In the reduced ,,stair-like” form:


The space of solutions is V_{(2)}=\{(-z,-z,z)\colon y,z\in\mathbb{R}\}, and its basis is (-1,-1,1). Indeed, \varphi((-1,-1,1))=(-2,-2,2)=2\cdot (-1,-1,1).

Eigenvector basis

If the sum of dimensions of spaces related to the eigenvalues of a given map equals the dimension of the whole space (as in our example: 1+2=3), then the basis of the whole space which consists of the vectors from the bases of subspaces related to the eigenvalues is called an eigenvector basis (in our case: \mathcal{A}=\{(0,1,0),(0,0,1),(-1,-1,1)\}).

If a map has an eigenvector basis, then it can be actually described by means of squeezing and stretching in the directions of eigenvectors. Notice that the matrix of such a map in an eigenvector basis is an diagonal matrix (has non-zero elements only on its diagonal) with eigenvalues related to subsequent eigenvectors on its diagonal. In our example:


It may happen that a map has no eigenvectors (e.g. a rotation of the plane) or that the subspaces of eigenvectors are to small (e.g. a 10 degree rotation of a three-dimensional space around an axis had only one-dimensional space of eigenvectors).

Diagonalization of a matrix

A matrix M is diagonalizable, if there exists a matrix C, such that:

    \[M=C\cdot D\cdot C^{-1},\]

where D is a diagonal matrix.

How to check it and diagonalize a matrix if it is possible? Simply consider a linear map \varphi such that M is its matrix in standard basis. Matrix M is diagonalizable, if and only if \varphi has eigenvector basis \oA. Then:

    \[M=M(id)_{\mathcal{A}}^{st}\cdot D\cdot (M(id)_{\mathcal{A}}^{st})^{-1}\]

and D=M(\varphi)_{\mathcal{A}}^{\mathcal{A}}, since (M(id)_{\mathcal{A}}^{st})^{-1}=M(id)_{st}^{\mathcal{A}}.

E.g. we know that


is diagonalizable, since \varphi related to this matrix has an eigenvector basis. furthermore in this case:




Calculating diagonalizable matrices

Diagonalization of a matrix has the following interesting matrix. We can use it to calculate a power of a matrix, if it is diagonalizable. Notice that if \mathcal{A} is a basis, then:




    \[=M(id)_{\mathcal{A}}^{st}\cdot M\left(\underbrace{\varphi\circ\ldots\circ\varphi}_{n}\right)_{\mathcal{A}}^{\mathcal{A}}\cdot M(id)_{st}^{\mathcal{A}} = M(id)_{\mathcal{A}}^{st}\cdot \left(M\left(\varphi\right)_{\mathcal{A}}^{\mathcal{A}}\right)^{n}\cdot M(id)_{st}^{\mathcal{A}}\]

but if \mathcal{A} is a basis of eigenvectors, then M\left(\varphi\right)_{\mathcal{A}}^{\mathcal{A}} is a diagonal matrix so calculating its power is simply calculating powers of the elements on the diagonal.

Let us show it on an example. Let us calculate:


We have:





    \[\left[\begin{array}{ccc}2&0&0\\1&1&0\\-1&0&1\end{array}\right]^{5}=C \cdot \left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&2\end{array}\right]^{5}\cdot C^{-1}=C \cdot \left[\begin{array}{ccc}1&0&0\\0&1&0\\0&0&32\end{array}\right]^{5}\cdot C^{-1}=\left[\begin{array}{ccc}32&0&0\\31&1&0\\-31&0&1\end{array}\right].\]