Let
be the set of
complex-valued matrices. Let us consider a matrix
and denote its complex conjugate by
and its transpose by
. We then have the following
Definition: A matrix
is said to be Hermitian if
, where
. It is skew-Hermitian if
.
A Hermitian matrix can be the representation, in a given orthonormal basis, of a self-adjoint operator.
Properties of Hermitian matrices
For two matrices
we have:
- If
is Hermitian, then the main diagonal entries of
are all real. In order to specify the
elements of
one may specify freely any
real numbers for the main diagonal entries and any
complex numbers for the off-diagonal entries;
-
,
and
are all Hermitian for all
;
- If
is Hermitian, then
is Hermitian for all
. If
is nonsingular as well, then
is Hermitian;
- If
are Hermitian, then
is Hermitian for all real scalars
;
-
is skew-Hermitian for all
;
- If
are skew-Hermitian, then
is skew-Hermitian for all real scalars
;
- If
is Hermitian, then
is skew-Hermitian;
- If
is skew-Hermitian, then
is Hermitian;
- Any
can be written as
where
respectively
are the Hermitian and skew-Hermitian parts of
.
Theorem: Each
can be written uniquely as
, where
and
are both Hermitian. It can also be written uniquely as
, where
is Hermitian and
is skew-Hermitian.
Theorem: Let
be Hermitian. Then
-
is real for all
;
- All the eigenvalues of
are real; and
-
is Hermitian for all
.
Theorem: Let
be given. Then
is Hermitian if and only if at least one of the following holds:
-
is real for all
;
-
is normal and all the eigenvalues of
are real; or
-
is Hermitian for all
.
Theorem [the spectral theorem for Hermitian matrices]: Let
be given. Then
is Hermitian if and only if there are a unitary matrix
and a real diagonal matrix
such that
. Moreover,
is real and Hermitian (i.e. real symmetric) if and only if there exist a real orthogonal matrix
and a real diagonal matrix
such that
.
Theorem: Let
be a given family of Hermitian matrices. Then there exists a unitary matrix
such that
is diagonal for all
if and only if
for all
.
Positivity of Hermitian matrices
Definition: An
Hermitian matrix
is said to be positive definite if
for all
If
, then
is said to be positive semidefinite.
The following two theorems give useful and simple characterizations of the positivity of Hermitian matrices.
Theorem: A Hermitian matrix
is positive semidefinite if and only if all of its eigenvalues are nonnegative. It is positive definite if and only if all of its eigenvalues are positive.
In the following we denote by
the leading principal submatrix of
determined by the first
rows and columns:
.
As for any positive matrix, if
is positive definite, then all principal minors of
are positive; when
is Hermitian, the converse is also valid. However, an even stronger statement can be made.
Theorem: If
is Hermitian, then
is positive definite if and only if
for
. More generally, the positivity of any nested sequence of
principal minors of
is a necessary and sufficient condition for
to be positive definite.
Bibliography
- R. A. Horn and C. R. Johnson, Matrix analysis, Cambridge University Press (1985).

