In linear algebra, eigenvalues and eigenvectors play a fundamental role, since, given a linear transformation, an eigenvector is a vector whose direction is not changed by the transformation, and the corresponding eigenvalue is the measure of the resulting change of magnitude of the vector.

More precisely, suppose the transformation is represented by a square matrix ${\displaystyle A.}$ Then an eigenvector ${\displaystyle \mathbf {v} }$ and the corresponding eigenvalue ${\displaystyle \lambda }$ must satisfy the equation $${\displaystyle A\mathbf {v} =\lambda \mathbf {v} ,}$$ or, equivalently (since ${\displaystyle \lambda \mathbf {v} =\lambda I\mathbf {v} }$), $${\displaystyle (\lambda I-A)\mathbf {v} =\mathbf {0} }$$ where ${\displaystyle I}$ is the identity matrix, and ${\displaystyle \mathbf {v} \neq \mathbf {0} }$ (although the zero vector satisfies this equation for every ${\displaystyle \lambda ,}$ it is not considered an eigenvector).

It follows that the matrix ${\displaystyle (\lambda I-A)}$ must be singular, and its determinant $${\displaystyle \det(\lambda I-A)=0}$$ must be zero.

In other words, the eigenvalues of A are the roots of $${\displaystyle \det(xI-A),}$$ which is a monic polynomial in x of degree n if A is a n×n matrix. This polynomial is the characteristic polynomial of A.

Consider an ${\displaystyle n\times n}$ matrix ${\displaystyle A.}$ The characteristic polynomial of ${\displaystyle A,}$ denoted by ${\displaystyle p_{A}(t),}$ is the polynomial defined by^[5] $${\displaystyle p_{A}(t)=\det(tI-A)}$$ where ${\displaystyle I}$ denotes the ${\displaystyle n\times n}$ identity matrix.

Some authors define the characteristic polynomial to be ${\displaystyle \det(A-tI).}$ That polynomial differs from the one defined here by a sign ${\displaystyle (-1)^{n},}$ so it makes no difference for properties like having as roots the eigenvalues of ${\displaystyle A}$; however the definition above always gives a monic polynomial, whereas the alternative definition is monic only when ${\displaystyle n}$ is even.

To compute the characteristic polynomial of the matrix $${\displaystyle A={\begin{pmatrix}2&1\\-1&0\end{pmatrix}}.}$$ the determinant of the following is computed: $${\displaystyle tI-A={\begin{pmatrix}t-2&-1\\1&t-0\end{pmatrix}}}$$ and found to be ${\displaystyle (t-2)t-1(-1)=t^{2}-2t+1\,\!,}$ the characteristic polynomial of ${\displaystyle A.}$

Another example uses hyperbolic functions of a hyperbolic angle φ. For the matrix take $${\displaystyle A={\begin{pmatrix}\cosh(\varphi )&\sinh(\varphi )\\\sinh(\varphi )&\cosh(\varphi )\end{pmatrix}}.}$$ Its characteristic polynomial is $${\displaystyle \det(tI-A)=(t-\cosh(\varphi ))^{2}-\sinh ^{2}(\varphi )=t^{2}-2t\ \cosh(\varphi )+1=(t-e^{\varphi })(t-e^{-\varphi }).}$$

The characteristic polynomial ${\displaystyle p_{A}(t)}$ of a ${\displaystyle n\times n}$ matrix ${\displaystyle A}$ is monic (its leading coefficient is ${\displaystyle 1}$) and its degree is ${\displaystyle n.}$ The most important fact about the characteristic polynomial was already mentioned in the motivational paragraph: the eigenvalues of ${\displaystyle A}$ are precisely the roots of ${\displaystyle p_{A}(t)}$ (this also holds for the minimal polynomial of ${\displaystyle A,}$ but its degree may be less than ${\displaystyle n}$). All coefficients of the characteristic polynomial are polynomial expressions in the entries of the matrix. In particular its constant coefficient of ${\displaystyle t^{0}}$ is ${\displaystyle \det(-A)=(-1)^{n}\det(A),}$ the coefficient of ${\displaystyle t^{n}}$ is 1, and the coefficient of ${\displaystyle t^{n-1}}$ is tr(−A) = −tr(A), where tr(A) is the trace of ${\displaystyle A.}$ (The signs given here correspond to the formal definition given in the previous section; for the alternative definition these would instead be ${\displaystyle \det(A)}$ and (−1)^{n – 1} tr(A) respectively.^[6])

For a ${\displaystyle 2\times 2}$ matrix ${\displaystyle A,}$ the characteristic polynomial is thus given by $${\displaystyle t^{2}-\operatorname {tr} (A)t+\det(A).}$$

Using the language of exterior algebra, the characteristic polynomial of an ${\displaystyle n\times n}$ matrix ${\displaystyle A}$ may be expressed as $${\displaystyle p_{A}(t)=\sum _{k=0}^{n}t^{n-k}(-1)^{k}\operatorname {tr} \left(\textstyle \bigwedge ^{k}A\right)}$$ where ${\textstyle \operatorname {tr} \left(\bigwedge ^{k}A\right)}$ is the trace of the ${\displaystyle k}$th exterior power of ${\displaystyle A,}$ which has dimension ${\textstyle {\binom {n}{k}}.}$ This trace may be computed as the sum of all principal minors of ${\displaystyle A}$ of size ${\displaystyle k.}$ The recursive Faddeev–LeVerrier algorithm computes these coefficients more efficiently ^{[clarification needed]}.

When the characteristic of the field of the coefficients is ${\displaystyle 0,}$ each such trace may alternatively be computed as a single determinant, that of the ${\displaystyle k\times k}$ matrix, $${\displaystyle \operatorname {tr} \left(\textstyle \bigwedge ^{k}A\right)={\frac {1}{k!}}{\begin{vmatrix}\operatorname {tr} A&k-1&0&\cdots &0\\\operatorname {tr} A^{2}&\operatorname {tr} A&k-2&\cdots &0\\\vdots &\vdots &&\ddots &\vdots \\\operatorname {tr} A^{k-1}&\operatorname {tr} A^{k-2}&&\cdots &1\\\operatorname {tr} A^{k}&\operatorname {tr} A^{k-1}&&\cdots &\operatorname {tr} A\end{vmatrix}}~.}$$

The Cayley–Hamilton theorem states that replacing ${\displaystyle t}$ by ${\displaystyle A}$ in the characteristic polynomial (interpreting the resulting powers as matrix powers, and the constant term ${\displaystyle c}$ as ${\displaystyle c}$ times the identity matrix) yields the zero matrix. Informally speaking, every matrix satisfies its own characteristic equation. This statement is equivalent to saying that the minimal polynomial of ${\displaystyle A}$ divides the characteristic polynomial of ${\displaystyle A.}$

Two similar matrices have the same characteristic polynomial. The converse however is not true in general: two matrices with the same characteristic polynomial need not be similar.

The matrix ${\displaystyle A}$ and its transpose have the same characteristic polynomial. ${\displaystyle A}$ is similar to a triangular matrix if and only if its characteristic polynomial can be completely factored into linear factors over ${\displaystyle K}$ (the same is true with the minimal polynomial instead of the characteristic polynomial). In this case ${\displaystyle A}$ is similar to a matrix in Jordan normal form.

If ${\displaystyle A}$ and ${\displaystyle B}$ are two square ${\displaystyle n\times n}$ matrices then characteristic polynomials of ${\displaystyle AB}$ and ${\displaystyle BA}$ coincide: $${\displaystyle p_{AB}(t)=p_{BA}(t).\,}$$

Proof: If ${\displaystyle \lambda }$ is a non-zero generalized eigenvalue of ${\displaystyle AB}$ of algebraic multiplicity ${\displaystyle k}$, and ${\displaystyle v}$ belongs to the kernel of ${\displaystyle (BA-\lambda )^{k}}$, then ${\displaystyle Av}$ belongs to the kernel of ${\displaystyle (AB-\lambda )^{k}}$, so the non-zero generalized eigenspaces of ${\displaystyle AB}$ and ${\displaystyle BA}$ have the same dimension. Therefore, since ${\displaystyle AB}$ and ${\displaystyle BA}$ are both ${\displaystyle n\times n}$, the remaining generalized eigenspaces, with eigenvalue 0, have the same dimension. Therefore ${\displaystyle AB}$ and ${\displaystyle BA}$ have the same characteristic polynomial, because all generalized eigenvalues are the same, with the same algebraic multiplicities.

More generally, if ${\displaystyle A}$ is a matrix of order ${\displaystyle m\times n}$ and ${\displaystyle B}$ is a matrix of order ${\displaystyle n\times m,}$ then ${\displaystyle AB}$ is ${\displaystyle m\times m}$ and ${\displaystyle BA}$ is ${\displaystyle n\times n}$ matrix, and one has $${\displaystyle p_{BA}(t)=t^{n-m}p_{AB}(t).\,}$$

To prove this, one may suppose ${\displaystyle n>m,}$ by exchanging, if needed, ${\displaystyle A}$ and ${\displaystyle B.}$ Then, by bordering ${\displaystyle A}$ on the bottom by ${\displaystyle n-m}$ rows of zeros, and ${\displaystyle B}$ on the right, by, ${\displaystyle n-m}$ columns of zeros, one gets two ${\displaystyle n\times n}$ matrices ${\displaystyle A^{\prime }}$ and ${\displaystyle B^{\prime }}$ such that ${\displaystyle B^{\prime }A^{\prime }=BA}$ and ${\displaystyle A^{\prime }B^{\prime }}$ is equal to ${\displaystyle AB}$ bordered by ${\displaystyle n-m}$ rows and columns of zeros. The result follows from the case of square matrices, by comparing the characteristic polynomials of ${\displaystyle A^{\prime }B^{\prime }}$ and ${\displaystyle AB.}$

If ${\displaystyle \lambda }$ is an eigenvalue of a square matrix ${\displaystyle A}$ with eigenvector ${\displaystyle \mathbf {v} ,}$ then ${\displaystyle \lambda ^{k}}$ is an eigenvalue of ${\displaystyle A^{k}}$ because $${\displaystyle A^{k}{\textbf {v}}=A^{k-1}A{\textbf {v}}=\lambda A^{k-1}{\textbf {v}}=\dots =\lambda ^{k}{\textbf {v}}.}$$

The multiplicities can be shown to agree as well, and this generalizes to any polynomial in place of ${\displaystyle x^{k}}$:^[7]

That is, the algebraic multiplicity of ${\displaystyle \lambda }$ in ${\displaystyle f(A)}$ equals the sum of algebraic multiplicities of ${\displaystyle \lambda '}$ in ${\displaystyle A}$ over ${\displaystyle \lambda '}$ such that ${\displaystyle f(\lambda ')=\lambda .}$ In particular, ${\displaystyle \operatorname {tr} (f(A))=\textstyle \sum _{i=1}^{n}f(\lambda _{i})}$ and ${\displaystyle \operatorname {det} (f(A))=\textstyle \prod _{i=1}^{n}f(\lambda _{i}).}$ Here a polynomial ${\displaystyle f(t)=t^{3}+1,}$ for example, is evaluated on a matrix ${\displaystyle A}$ simply as ${\displaystyle f(A)=A^{3}+I.}$

The theorem applies to matrices and polynomials over any field or commutative ring.^[8] However, the assumption that ${\displaystyle p_{A}(t)}$ has a factorization into linear factors is not always true, unless the matrix is over an algebraically closed field such as the complex numbers.

The term secular function has been used for what is now called characteristic polynomial (in some literature the term secular function is still used). The term comes from the fact that the characteristic polynomial was used to calculate secular perturbations (on a time scale of a century, that is, slow compared to annual motion) of planetary orbits, according to Lagrange's theory of oscillations.

Secular equation may have several meanings.

• In linear algebra it is sometimes used in place of characteristic equation.
• In astronomy it is the algebraic or numerical expression of the magnitude of the inequalities in a planet's motion that remain after the inequalities of a short period have been allowed for.^[9]

• In molecular orbital calculations relating to the energy of the electron and its wave function it is also used instead of the characteristic equation.

The above definition of the characteristic polynomial of a matrix ${\displaystyle A\in M_{n}(F)}$ with entries in a field ${\displaystyle F}$ generalizes without any changes to the case when ${\displaystyle F}$ is just a commutative ring. Garibaldi (2004) defines the characteristic polynomial for elements of an arbitrary finite-dimensional (associative, but not necessarily commutative) algebra over a field ${\displaystyle F}$ and proves the standard properties of the characteristic polynomial in this generality.

• Characteristic equation (disambiguation)
• Invariants of tensors
• Companion matrix
• Faddeev–LeVerrier algorithm
• Cayley–Hamilton theorem
• Samuelson–Berkowitz algorithm