Characteristic polynomial

In linear algebra, the characteristic polynomial of a square matrix is a polynomial which is invariant under matrix similarity and has the eigenvalues as roots. It has the determinant and the trace of the matrix among its coefficients. The characteristic polynomial of an endomorphism of vector spaces of finite dimension is the characteristic polynomial of the matrix of the endomorphism over any base; it does not depend on the choice of a basis. The characteristic equation, also known as the determinantal equation, is the equation obtained by equating to zero the characteristic polynomial.
In spectral graph theory, the characteristic polynomial of a graph is the characteristic polynomial of its adjacency matrix.
Motivation.
Given a square matrix "A", we want to find a polynomial whose zeros are the eigenvalues of "A". For a diagonal matrix "A", the characteristic polynomial is easy to define: if the diagonal entries are "a"1, "a"2, "a"3, etc. then the characteristic polynomial will be:
This works because the diagonal entries are also the eigenvalues of this matrix.
For a general matrix "A", one can proceed as follows. A scalar "λ" is an eigenvalue of "A" if and only if there is a nonzero vector v, called an eigenvector, such that
or, equivalently,
(where is the identity matrix). Since must be nonzero, this means that the matrix has a nonzero kernel. Thus this matrix is not invertible, and the same is true for its determinant, which must therefore be zero. Thus the eigenvalues of are the roots of , which is a polynomial in .
Formal definition.
We consider an "n"×"n" matrix "A". The characteristic polynomial of "A", denoted by "pA"("t"), is the polynomial defined by
where "I" denotes the "n"×"n" identity matrix.
Some authors define the characteristic polynomial to be . That polynomial differs from the one defined here by a sign , so it makes no difference for properties like having as roots the eigenvalues of "A"; however the definition above always gives a monic polynomial, whereas the alternative definition is monic only when "n" is even.
Examples.
Suppose we want to compute the characteristic polynomial of the matrix
We now compute the determinant of
which is formula_7 the characteristic polynomial of "A".
Another example uses hyperbolic functions of a hyperbolic angle φ.
For the matrix take
Its characteristic polynomial is
Properties.
The characteristic polynomial of a matrix is monic (its leading coefficient is 1) and its degree is . The most important fact about the characteristic polynomial was already mentioned in the motivational paragraph: the eigenvalues of are precisely the roots of (this also holds for the minimal polynomial of , but its degree may be less than ). All coefficients of the characteristic polynomial are polynomial expressions in the entries of the matrix. In particular its constant coefficient is , the coefficient of is one, and the coefficient of is , where is the trace of . (The signs given here correspond to the formal definition given in the previous section; for the alternative definition these would instead be and respectively.)
For a 2×2 matrix "A", the characteristic polynomial is thus given by
Using the language of exterior algebra, one may compactly express the characteristic polynomial of an "n"×"n" matrix "A" as 
where formula_12 is the trace of the th exterior power of , which has dimension formula_13. This trace may be computed as the sum of all principal minors of of size . The recursive Faddeev–LeVerrier algorithm computes these coefficients more efficiently.
When the characteristic of the field of the coefficients is 0, each such trace may alternatively be computed as a single determinant, that of the matrix,
The Cayley–Hamilton theorem states that replacing "t" by "A" in the characteristic polynomial (interpreting the resulting powers as matrix powers, and the constant term "c" as "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 "A" divides the characteristic polynomial of "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 "A" and its transpose have the same characteristic polynomial. "A" is similar to a triangular matrix if and only if its characteristic polynomial can be completely factored into linear factors over "K" (the same is true with the minimal polynomial instead of the characteristic polynomial). In this case "A" is similar to a matrix in Jordan normal form.
Characteristic polynomial of a product of two matrices.
If "A" and "B" are two square "n×n" matrices then characteristic polynomials of "AB" and "BA" coincide: 
When "A" is non-singular this result follows from the fact that "AB" and "BA" are similar:
For the case where both "A" and "B" are singular, one may remark that the desired identity is an equality between polynomials in "t" and the coefficients of the matrices. Thus, to prove this equality, it suffices to prove that it is verified on a non-empty open subset (for the usual topology, or, more generally, for the Zariski topology) of the space of all the coefficients. As the non-singular matrices form such an open subset of the space of all matrices, this proves the result.
More generally, if "A" is a matrix of order "m×n" and "B" is a matrix of order "n×m", then "AB" is "m×m" and "BA" is "n×n" matrix, and one has
To prove this, one may suppose "n" > "m", by exchanging, if needed, "A" and "B". Then, by bordering "A" on the bottom by "n" – "m" rows of zeros, and "B" on the right, by, "n" – "m" columns of zeros, one gets two "n×n" matrices "A' "and "B' " such that "B'A' "= "BA", and "A'B' " is equal to "AB" bordered by "n" – "m" rows and columns of zeros. The result follows from the case of square matrices, by comparing the characteristic polynomials of "A'B' " and "AB".
Characteristic polynomial of "A""k".
If formula_18 is an eigenvalue of a square matrix "A" with eigenvector v, then clearly formula_19 is an eigenvalue of "A""k"
The multiplicities can be shown to agree as well, and this generalizes to any polynomial in place of formula_21:
That is, the algebraic multiplicity of formula_18 in formula_23 equals the sum of algebraic multiplicities of formula_24 in formula_25 over formula_24 such that formula_27.
In particular, formula_28 and formula_29.
Here a polynomial formula_30, for example, is evaluated on a matrix "A" simply as formula_31.
The theorem applies to matrices and polynomials over any field or commutative ring.
However, the assumption that formula_32 has a factorization into linear factors is not always true, unless the matrix is over an algebraically closed field such as the complex numbers.
This proof only applies to matrices and polynomials over complex numbers (or any algebraically closed field).
In that case, the characteristic polynomial of any square matrix can be always factorized as 
where formula_34 are the eigenvalues of formula_25, possibly repeated.
Moreover, the Jordan decomposition theorem guarantees that any square matrix formula_25 can be decomposed as formula_37, where formula_38 is an invertible matrix and formula_39 is upper triangular
with formula_40 on the diagonal (with each eigenvalue repeated according to its algebraic multiplicity).
(The Jordan normal form has stronger properties, but these are sufficient; alternatively the Schur decomposition can be used, which is less popular but somewhat easier to prove).
Let formula_41.
Then 
It is easy to check for an upper triangular matrix formula_39 with diagonal formula_40, the matrix formula_45 is upper triangular with diagonal formula_46 in formula_45,
and hence formula_48 is upper triangular with diagonal formula_49.
Therefore, the eigenvalues of formula_48 are formula_49.
Since formula_52 is similar to formula_48, it has the same eigenvalues, with the same algebraic multiplicities.
Secular function and secular equation.
Secular function.
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, i.e. slow compared to annual motion) of planetary orbits, according to Lagrange's theory of oscillations.
Secular equation.
"Secular equation" may have several meanings.
For general associative algebras.
The above definition of the characteristic polynomial of a matrix formula_54 with entries in a field "F" generalizes without any changes to the case when "F" is just a commutative ring. defines the characteristic polynomial for elements of an arbitrary finite-dimensional (associative, but not necessarily commutative) algebra over a field "F" and proves the standard properties of the characteristic polynomial in this generality.