Spectral theorem: eigenvalue decomposition for symmetric matrices
We can decompose any symmetric matrix with the symmetric eigenvalue decomposition (SED)

where the matrix of is orthogonal (that is,
), and contains the eigenvectors of
, while the diagonal matrix
contains the eigenvalues of
.
Proof: The proof is by induction on the size of the matrix
. The result is trivial for
. Now let
and assume the result is true for any matrix of size
.
Consider the function of ,
. From the basic properties of the determinant, it is a polynomial of degree
, called the characteristic polynomial of
. By the fundamental theorem of algebra, any polynomial of degree
has
(possibly not distinct) complex roots; these are called the eigenvalues of
. We denote these eigenvalues by
.
If is an eigenvalue of
, that is,
, then
must be non-invertible (see here). This means that there exists a non-zero real vector
such that
. We can always normalize
so that
. Thus,
is real. That is, the eigenvalues of a symmetric matrix are always real.
Now consider the eigenvalue and an associated eigenvector
. Using the Gram-Schmidt orthogonalization procedure, we can compute a
matrix
such that
is orthogonal. By induction, we can write the
symmetric matrix
as,
where
is a
matrix of eigenvectors, and
are the
eigenvalues of
. Finally, we define the
matrix
. By construction the matrix
is orthogonal.
We have

where we have exploited the fact that , and
.
We have exhibited an orthogonal matrix
such that
is diagonal. This proves the theorem.