1. Introduction
Matrix theory is widely used in statistics [
1], physics [
2], computer science [
3] and so on. For convenience,
is denoted as the set of all
complex matrices (
is the set of complex numbers) [
4].
A is called a Hermitian matrix when
satisfies
(
denotes conjugate transposition of
A). The Hermitian matrix is frequently used in quadratic forms and their correlation theory [
5]. Let
denote the set of
Hermitian matrices and
denote the
positive semidefinite Hermitian matrix (
is the
n dimensional complex Euclidean space).
Set
to be any orthonormal basis of
, and then the trace operator Tr is defined as [
4]
where
is the inner product of
. It is well known that for any
, the following equalities hold [
6]
where
is the eigenvalue of
A.
From the spectral theorem [
5],
can be decomposed as
where
P is a unitary matrix and
is a diagonal matrix with eigenvalues
. Then, matrix function
is defined as
where
and
.
Based on the above definition, in 1963, the Wigner–Yanase skew information
was introduced by Wigner and Yanase ([
7]), where
is a density matrix (
and
H is a Hermitian matrix. Then, an open problem was left
which is concave for any positive semidefinite matrix
A.
In 1973, (
2) was proven by Lieb for all
[
8], and a more generalized result was obtained from the following fact [
9]
where
. In fact, the Lieb concavity theorem is equivalent to the concavity of
.
A more elegant proof of the Lieb concavity theorem appeared in [
10] using
where
In 2009, Effros gave another proof of the Lieb concavity theorem based on the Hansen–Pedersen–Jensen inequality ([
11]). Using
then one obtains
All the above proof of the Lieb concave theorem is equivalent to the joint concavity of commutative operators. In addition, Epstein also obtained the Lieb concave theorem using the theory of Herglotz functions [
12].
Recently, Shi and Hansen [
13] generalized the Thompson–Golden theorem
As the Thompson–Golden theorem can be regarded as a special form of the Lieb concave theorem, it is worthwhile to study the Lieb concavity theorem for deformed exponentials. In this paper, we will use the theory of the Pick function to obtain a generalization of the Lieb concavity theorem and some other corollaries. The rest of the paper is organized as follows. In
Section 2, some general definitions and important conclusions are introduced. With these preparations, we obtain some useful results, such as the Lieb concavity theorem, presented in the final
Section 3.
2. Preliminary
In this section, some general definitions and some important properties are introduced.
2.1. The q-Logarithm Function and q-Exponential Function
It is well known that the q-logarithm function
is defined as [
13]
for any
. The deformed exponential function or the
exponential
is the inverse function of the
logarithm and is defined as
2.2. Tensor Product and Exterior Algebra
The tensor product, denoted by
, is also called the Kronecker product. It is a generalization of the outer product from vectors to matrices, and the tensor product of matrices is also referred to as the outer product in certain contexts ([
9]). For an
matrix
A and a
matrix
B, the tensor product of
A and
B is defined by
where
.
The tensor product is different from matrix multiplication, and one of the differences is commutativity
From the above equations, we obtain
For convenience, we denote
In addition to the tensor product, there is another common product called exterior algebra [
6]. Exterior algebra, denoted by
, is a binary operation for any
, and the definition is
where
is an orthonormal basis of
, and
is the family of all permutations on .
Let
be the span of the
, and then a simple calculation shows that
2.3. Pick Function
Let
be a complex number where
i is the imaginary unit and
is analytic where
are all real functions.
denotes the real part of
z, and
is the imaginary part of
z. If
for any
, then we call the analytic function
a Pick function [
14]. It is equivalent that
is analytic in the upper half-plane with the positive imaginary part.
The Pick functions evidently form a convex cone—for instance, if
and
are positive numbers and
and
are two Pick functions, then the function
is also a Pick function. A simple example is that
is a Pick function.
Hence, , and this implies that when .
It is well known that the Pick function has a integral representation, such as the following lemma [
14].
Lemma 1. Let be a Pick function. Then, has a unique canonical representation of the formwhere α is real, and is a positive Borel measure on the real axis that is finite. Conversely, any function of this form is also a Pick function. Lemma 1 is frequently used for functions that are positive and harmonic in the half-plane.
2.4. The Matrix-Monotone Function
A matrix function
f is said to be matrix-monotonic if it satisfies
where
is equivalent to
is a positive semidefinite Hermitian matrix.
Since the matrix-monotone function is a special kind of operator monotone function, we have the following general conclusions [
14].
Lemma 2. The following statements for a real valued continuous function f on are equivalent:
is matrix-monotone;
admits an analytic continuation to the whole domain and .
f admits an integral representation:where α is a real number, β is non-negative and μ is a finite positive measure on . From Lemmas 1 and 2, we know that a Pick function must be a matrix-monotone function.
2.5. Convexity of Matrix
Suppose that
X is a convex set in
and
f is a function defined on
X. Then, we call
f a convex function if
for all
and
.
A matrix function
f is called convex if [
15,
16,
17]
for any
and any
. Replacing ≤ by < in (
5), this gives the definition of a strictly matrix convex function. A matrix function
f is called (strictly) concave if
is (strictly) convex. More details can be found in [
18].
A matrix convex function must be a convex function; however, the inverse claim is not always true. For instance, the function given by is a convex function. However, the matrix function for any is not convex.
Let
be a bivariate function defined on
. We call
jointly convex if
for all
and all
.
2.6. Brunn–Minkowski Inequality
Finally, let us review the Brunn–Minkowski inequality [
19].
Lemma 3. For any , and then Proof. Let
be the eigenvectors of
with the eigenvalue
, then
where
and ≥ holds due to
.
As
is concave [
20], we have
□
3. Lieb Concavity Theorem for Deformed Exponential
In this section, we obtain some useful conclusions, and some simple and straightforward computations are omitted. Recently, by using the Young inequality,
Shi and Hansen obtained that
is concave for any
where
(
I is the identity matrix of
) [
13], namely, the following theorem.
Theorem 1. For , and , the functionis concave for the strictly positive . Proof. (The first proof of Theorem 1)
Since [
21]
we obtain
where
are eigenvalues of A. When
is a convex function, we obtain
for any
t. This implies that
Thus, the concavity of
is equivalent to the jointly concavity of
for the strictly positive
A and
C, which is the Lieb concavity theorem [
22,
23]. □
Unfortunately, Theorem 1 cannot be obtained using Epstein’s theorem. Hence, we require a more general generalization of Epstein’s theorem. First, for any
, we know that
is invertible and
is a Pick function for any
[
14]. For any
, we know
is defined as [
12]
where
is a complex holomorphic function in an open set of the complex plane containing
(the set of all eigenvalues of
A). Then, we have the following lemma.
Lemma 4. Let and , thenis a Pick function for any and if , such as . Generally, we can find thatis a Pick function when f is a Pick function. Proof. Setting
, we have
where
.
Since
, we see that
is invertible. Hence, we have
This implies that
hence,
when
.
In the same way, we can obtain
In particular, letting
, we have
This is equivalent to .
To prove
, let
, we find
where
.
When
is a Pick function, using the integral represented of
, in a similar way, we can obtain that
is a Pick function for any
. □
Using Lemma 4, another proof of Theorem 1 can be obtained.
Theorem 2. For , and , the functionis concave for the strictly positive . Proof. (The second proof of Theorem 1)
First, setting
where
and
. As
when
and
, then
where
are the eigenvectors of
.
Hence,
where
is the
i eigenvalue of
and
.
Thus, is a Pick function, and this implies that is concave. □
Using a similar method, we can obtain the following corollary.
Corollary 1. For and , the functionis concave for the strictly positive . Since the Thompson–Golden theorem can be seen as a corollary of the Lieb concavity theorem, we discuss the Lieb concavity theorem for deformed exponentials. Setting
and
, then for any
,
and
,
, we have [
12]
and then the following theorem can be obtained.
Theorem 3. For , and , the following functionis concave for any . Proof. Set .
When
is a eigenvector of
and
,
if
. This implies
such as
Similarly, we can also obtain
Hence, using Equation (8), we see that
Thus, we know , which implies that is a Pick function. Hence, L(A) is concave. □
In fact, Theorem 3 is a generalization of the Lieb concavity theorem setting , and . Moreover, we can obtain the following theorem.
Theorem 4. For , and , the functionsandare jointly concave for any . The proof of Theorem 4 is similar to Theorem 3; here, we do not repeat the proof. In [
19], Huang used exterior algebra to find that
is a concave function for any
,
and
. Associated with Theorem 1, we can obtain a generalization as the following theorem.
Theorem 5. For , and , the functionis concave for the strictly positive and . Proof. In fact, we can prove that
is a concave function for any
where
and
.
Using Theorem 1, we know that
is a concave function for any
where
and
.
Then, for any
, we have
where
and
. Analogously, we can obtain
□
Clearly, the proof of Theorem 5 is in the application of exterior algebra and the Brunn–Minkowski inequality. Hence, other theorems, such as the Thompson–Golden theorem in a deformed exponential, can be generalized to a more general form, but we do not discuss this here.