# Do all square matrices have eigenvectors?

I came across a video lecture in which the professor stated that there may or may not be any eigenvectors for a given linear transformation. And, so far I thought every matrix has eigenvectors. Please clarify.

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Think of rotation in $\Bbb R^2$. –  David Mitra Jan 28 at 11:51
Your question says linear transformation, while your title says square matrices. These are not the same things. –  Marc van Leeuwen Jan 28 at 17:04

It depends over what field we're working. For example, the real matrix

$$A=\begin{pmatrix}0&\!\!-1\\1&0\end{pmatrix}$$

has no eigenvalues at all (i.e., over $\;\Bbb R\;$ ), yet the very same matrix defined over the complex field $\;\Bbb C\;$ has two eignevalues: $\;\pm i\;$

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So when we talk about eigenvectors we must specify the domain? –  Saurabh Shringarpure Jan 28 at 11:58
Always, @SaurabhShringarpure . –  DonAntonio Jan 28 at 12:05
And of course over the rationals $\mathbb{Q}$ even a matrix like $$\begin{pmatrix} 0 & 2 \\ 1 & 0 \\ \end{pmatrix}$$ lacks eigenvalues (and eigenvectors). –  Jeppe Stig Nielsen Jan 28 at 17:36

Over an algebraically closed field, every matrix has an eigenvalue. For instance, every complex matrix has an eigenvalue. Every real matrix has an eigenvalue, but it may be complex.

In fact, a field $K$ is algebraically closed iff every matrix with entries in $K$ has an eigenvalue. You can use the companion matrix to prove one direction. In particular, the existence of eigenvalues for complex matrices is equivalent to the fundamental theorem of algebra.

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Every matrix? (Certainly only square ones). But the question says linear transformations. –  Marc van Leeuwen Jan 28 at 16:58

Take a look at the matrix $$A=\begin{bmatrix}0 &1\\-1 & 0\end{bmatrix}.$$

This matrix has characteristic polynimial of $p(\lambda) = \lambda^2 + 1$, meaning that for real $\lambda$, every matrix $A-\lambda I$ has rank $2$, therefore the matrix has no eigenvectors.

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So when we talk about eigenvectors we must specify the domain? –  Saurabh Shringarpure Jan 28 at 11:55
Of course. An eigenvector of matrix $A$ which maps a vector space $V$ onto itself iz by definition such a vector $x$ in $V$ that $Ax=\lambda x$. Mind you, for complex matrices, you always have at least one eigenvector. –  5xum Jan 28 at 11:57

No, but you can build some.

A matrix in a given field (or even commutative ring) may or may not have eigenvectors. It has eigenvectors if and only if it has eigenvalues, by definition. The Cayley-Hamilton theorem provides an easy characterization of whether a matrix has eigenvalues: the eigenvalues are exactly the roots of the characteristic polynomial. Thus a matrix has eigenvectors if and only if the characteristic polynomial has at least one root. For example, the following matrix over $\mathbb{R}$ has no eigenvectors, because its characteristic polynomial $X^2+1$ has no real root: $$\begin{pmatrix} 0 & -1 \\ 1 & 0 \\ \end{pmatrix}$$ This is a rotation matrix: it represents a planar transformation that transforms any vector into a vector that makes a specific angle with the original (a right angle, in this case), and in particular the result cannot possibly be parallel with the original.

Thus it is certainly possible for a matrix not to have any eigenvectors. However, given a matrix over a field, it is possible to construct a larger field in which the matrix has eigenvectors. Any extension field in which the characteristic polynomial has at least one root will do. In particular, in an algebraically closed field such as $\mathbb{C}$, every matrix has at least one eigenvalue and therefore has eigenvectors. For example, the matrix above, when taken as a matrix over $\mathbb{C}$, has the eigenvalues $i$ and $-i$ and eigenvectors of the form $\{(\pm i z,z) \mid z\in\mathbb{C}\}$.

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+1 for the only complete explanation :) –  nbubis Jan 29 at 9:02

Here are some cases of endomorphisms of a vector space that don't have any eigenvectors. (Note that a linear transformation has to be from a space to itself, i.e. a vector space endomorphism, for the notion of eigenvalue to even be defined. Note also that "linear transformation" implies working over a given field$~K$, which one cannot extend at will, unlike the case of matrices that can be interpreted over any ring that contains their entries.)

• The left-shift operator $\def\N{{\mathbf N}}(a_i)_{i\in\N}\mapsto(a_{i+1})_{i\in\N}$ in the $2$-dimensional $\def\Q{{\mathbf Q}}~\Q$ vector space of sequences in $\Q^\N$ that satisfy the Fibonacci recurrence $a_{i+2}=a_i+a_{i+1}$. (Here eigenvectors do exist for the corresponding endomorphism of the corresponding space of sequences of real numbers.)

• Rotation about an angle $\def\Z{{\mathbf Z}}\alpha\notin\pi\Z$ in a $2$-dimensional Euclidean space. (Here the characteristic polynomial only has complex roots, which do not give eigenvectors in this $\def\R{{\mathbf R}}\R$ vector space.)

• Multiplication by some fixed non-constant polynomial in $K[X]$, or by some fixed non-constant series in $K[[X]]$, for any field $K$. (Here the infinite dimension is the essential aspect.)

• The unique linear endomorphism of any $0$-dimensional $K$-vector space.

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