1. Introduction
The study of the numerical radius, a central concept in operator theory [
1], has a rich history dating back to foundational works like Kato [
2]. In recent decades, significant progress has been achieved in establishing sharp bounds, with the inequalities developed by Kittaneh [
3] being particularly influential. These bounds have found wide application, for instance, in the analysis of operator matrices [
4]. The ongoing quest for more accurate [
5] and extended [
6] estimates continues to motivate new research, and in this paper, we contribute to this effort by introducing a new framework based on operator symmetries.
Let be a complex Hilbert space equipped with an inner product and its induced norm . The algebra of all bounded linear operators on is denoted by . For any given operator , its adjoint is denoted by , and its positive square root is defined as . The real and imaginary parts of are defined as and , respectively. The numerical range of , denoted by , is the set .
Let be a complex Hilbert space equipped with an inner product and its induced norm . The algebra encompassing all bounded linear operators on is denoted by . For any given operator , its adjoint is signified by , and its positive square root is defined as . The real and imaginary parts of are specified as and , respectively. The numerical range of , denoted by , is the set of all values .
The operator norm
and numerical radius
of an operator
are given by:
It is widely recognized that the numerical radius
defines a norm on
that is equivalent to the operator norm
. The following sharp inequalities are satisfied:
The first inequality becomes an equality if
, whereas the second becomes an equality if
is a normal operator, meaning that
. Kittaneh [
3] further refined these bounds, showing that
For more extensive discussions regarding (
1) and (
2), readers are encouraged to consult references [
7,
8,
9,
10,
11,
12,
13].
For a pair of operators
in
, the Euclidean operator radius, denoted by
, is defined as
Additional details can be found in [
14]. According to [
15], the function
constitutes a norm that satisfies
Here, the constants
and 1 are optimal. For self-adjoint operators
and
, this inequality simplifies to
Notably, when and are self-adjoint, , a result easily derived from the definition of .
In a previous study [
16], the second author derived the following lower bound:
The constant
in (
3) has been demonstrated to be the best possible. The same work presented other results, such as
where
is sharp in both inequalities. Further, the following sharp inequalities were established:
and
By substituting the pair
with either
or
for an operator
, the second author in [
16] obtained several inequalities for the norm and numerical radius of a single operator. For enhancements of these results, one may refer to the recent work [
17], in which S. Jana, P. Bhunia, and K. Paul introduced significant findings, including:
and
for any
.
For a given
, we examine the generalized expressions for a pair of operators
from [
18]:
along with the generalized
s-
r norm from [
18]:
When
, these definitions correspond to the Euclidean numerical radius [
15]:
and the Euclidean norm [
15]:
When
, we use the notations
and
These definitions can be extended to , although in that range, they no longer satisfy the properties of a norm.
In 2017, Moslehian et al. [
14] established fundamental inequalities for the
s–
r numerical radius of an operator pair
, which include the following:
and
among others. They further applied these findings to the Cartesian decomposition of an operator.
In 1992, J. E. Pečarić [
19] (see also [
20], p. 394) established a general inequality for inner product spaces. It states that for any vectors
and scalars
, the following inequality holds:
Setting
in (
4), we find that
for any
and
.
Motivated by these findings and by applying (
5), we derive multiple inequalities. For a single operator
, these include:
and
for all
.
Furthermore, for an operator pair
in
, with
and
, we establish:
and for the case
,
Moreover, for
,
, and
, we have
We also conduct a detailed investigation of instances where the pair is specified as or for some operator .
Symmetry is a foundational concept in many operator-theoretic studies. The pairs and represent fundamental symmetries: the former illustrates the duality between an operator and its adjoint, while the latter signifies the Cartesian decomposition into real and imaginary parts. These structures exhibit a balanced and complementary nature, positioning them as ideal subjects for deriving more refined inequalities. This work demonstrates how such symmetric properties can extend Pečarić’s inequality to operator theory, yielding more acute bounds for norms and numerical radii.
Throughout this paper, the significance of symmetry in operator inequalities is consistently emphasized. When inequalities are developed for pairs like , or , we highlight the corresponding symmetric relationship. This underscores the duality and symmetry inherent in these decompositions, mirroring the intrinsic symmetry of operator adjoints. Adopting this perspective not only simplifies several proofs, but also clarifies how bounds can be transferred from one symmetric context to another.
Section 2 introduces the primary inequalities and their proofs.
Section 3 is dedicated to applications involving
s–
r-norm inequalities, while
Section 4 outlines the implications for the
s–
r-numerical radius. The paper concludes with a discussion on the sharpness of these bounds and provides illustrative examples.
2. Main Results
In this section, we present our main results. The subsequent theorem establishes several vector inequalities for a linear combination of operators. These will serve as a foundation for deriving the various norm and numerical radius inequalities below.
Theorem 1. Let and . Then, for the following inequality holds:for all For the specific case where we have:for all Proof. Let
and
. From the inequality (
5), we can deduce that
for all
By taking the power
and applying the elementary inequality
we can infer from (
7) that
for any
, which confirms the inequality (
6). □
Remark 1. If we take the supremum over all unit vectors (i.e., ) in the inequality (6), we obtain: Consequently, this leads towhich yields the main inequality of interest:for In the case of we obtain If we set
in Theorem 1, then for
we have
for all
For
this simplifies to
for all
The corresponding norm inequalities are
and
for any
. The pair
effectively highlights the adjoint symmetry between an operator and its conjugate counterpart; the resulting bounds quantify this duality. By choosing
and
, we obtain
for all
For the case
we obtain
for all
These vector inequalities yield the following norm inequalities:
and
Let
have the Cartesian decomposition
. For
we can deduce from (
6) with
and
that
for all
This Cartesian decomposition makes the symmetry between
and
explicit, and several of our inequalities can be interpreted as measures of the balanced contribution from these symmetric parts. For
we obtain
for all
These relations lead to the following norm inequalities
and for
For an operator’s Cartesian decomposition, a corresponding result for the numerical radius can also be stated. Here, the pair represents a Cartesian symmetry, while displays the adjoint symmetry; both of these perspectives will be leveraged in the estimates that follow.
Theorem 2. For any operator the following numerical radius inequality is satisfied:for all Proof. We employ the well-known representation of the numerical radius, as detailed for instance in [
21], Theorem 2.2.11:
It can be observed that
for any
. The formulation
demonstrates rotational symmetry in the
plane, a viewpoint we utilize throughout our analysis. From (
8), we have
By setting
and
in this inequality, we find that
for all
.
Taking the supremum over
then yields
Consider a vector
with
An application of the elementary Hölder’s inequality allows us to state that
for any
with
and
.
We now observe that
and, for
it follows that
Furthermore, by applying the McCarthy inequality for a non-negative operator
P and a unit vector
,
we can establish for
with
that
Using (
11), we can then deduce that
and by raising this to the power
we obtain
for any
.
By taking the supremum over
and utilizing (
10), we arrive at the desired result (
9). □
Remark 2. If we set in (9), we obtainwhile for the inequality becomes Let us now define the operator
The following result holds.
Theorem 3. For any operator we have the following numerical radius inequality:for all Proof. Note that the operator
can be expressed as
which implies that
for any
.
By taking the norm and then the power
the convexity of the power function ensures that
From inequality (
8), taking the square root gives for
that,
for
and
.
Applying (
14) with the substitutions
and
we obtain
for any
.
From (
13), it then follows that
for any
.
By taking the supremum over
in the above inequality, we arrive at (
12). □
Remark 3. For the case in (12), we have: For the case , we find that: We also present the following theorem.
Theorem 4. For any operator the subsequent numerical radius inequality holds:where and . Proof. We utilize the following identity from [
22], also found in [
21], p. 34:
for any
.
From (
8) with
and
, we obtain
for any
.
In a similar manner to the proof of Theorem 2, we can show that
for all
.
By employing (
16), we arrive at
for any
.
We note the identities:
from which it follows that
and
for any
. By substituting these into (17) and taking the supremum over
, we obtain the desired result (
15). □
Remark 4. For the case , we derive the inequalities: Also, if we take in (15), then we obtainfor any For , this gives the simpler bounds: 3. Applications for –-Norm Inequalities
This section demonstrates the application of our main inequalities to the specific case of the s–r-norm. We will emphasize the symmetric pairs and where appropriate, clarifying how bounds can be translated between these corresponding frameworks. Here, we introduce several power inequalities pertaining to the s–r-norm. By setting the operator pair to be or for an operator , we establish norm inequalities for a single operator. In particular, these choices make the adjoint and Cartesian symmetries explicit, a fact we leverage in the following results. Our first main result in this section is as follows.
Theorem 5. Let . For and , we have: For the case we have: For , we obtain the following inequality for the Euclidean norm: Proof. Let
For
we define the scalars
and
Also, we have
and
for
From the inequality (
6), we have
By substituting the expressions for
and
defined above, we obtain
This inequality remains valid even if or
For unit vectors
(i.e.,
), we have
and
Using (
21), we obtain
for any unit vectors
.
By taking the supremum over all unit vectors
, we arrive at the desired result (
18).
For the case
let
If we choose
(for
) and
(for
) in (
20), we obtain
which holds true even when
or
Taking the supremum over all unit vectors
yields the inequality (
19). The remaining claims follow directly. □
Remark 5. Setting in Theorem 5, we obtain for : For , the inequality for the Euclidean norm is: Consider an operator
. Let us define the quantity
The function
acts as a symmetric two-component gauge that captures the duality between an operator and its adjoint. For
, we denote
and for
, we consider
From Theorem 5, we deduce for
and
that
For
, we obtain the inequality
For
, we derive for
that
And for
, the inequality is
Now, let
be given by its Cartesian decomposition
. For
, we can introduce the following quantities:
Thus,
provides a measure of the balanced (Cartesian) symmetry between the real and imaginary parts of
. For
, we obtain
We can observe that
for any
.
From Theorem 5, we deduce for
and
that
For
, we obtain the following inequality
The case
gives for
:
For
, this becomes:
while for
we have:
4. Applications for –-Numerical Radius Inequalities
This section applies the preceding results to the s–r-numerical radius. We continue to emphasize the symmetry perspective to clarify how a bound for one decomposition can immediately yield its symmetric analogue. Here, we present several power inequalities for the s–r-numerical radius. By specifying the pair as either or for an operator , we derive various norm and numerical radius inequalities for a single operator.
Theorem 6. Let . For and , we have: For the case we have: For , we obtain the following inequality for the Euclidean numerical radius: Proof. From (
21), by setting
, we have:
for any
Observe that for a unit vector
, we have
and
From (
25), we can then derive
for any unit vector
.
For the case
we have from (
22) that
for any unit vector
.
By taking the supremum over all unit vectors
, we obtain the desired results (
23) and (
24). □
Remark 6. For any , when , we obtain for : For , the inequality for the Euclidean numerical radius is as follows: For
and the special case
, we have that
If we set
for an operator
in Theorem 6 and carry out the calculations, then for
we find that:
For the case
this simplifies to:
For
, we also obtain the inequality:
Now, let
have the Cartesian decomposition
. For
, we can introduce the quantities:
In the same vein,
encodes the symmetric contribution of
and
to the numerical radius. For
, we denote:
while for
If we set
for an operator
in Theorem 6, then for
:
For
, we obtain:
while for
:
If we take
then for
we obtain:
For
:
and for
:
In conclusion, the inequalities developed in this paper not only refine classical operator bounds, but also demonstrate the essential role of symmetry in Hilbert space analysis. By uncovering symmetric structures between adjoints, real and imaginary parts, and operator pairs, our results provide new perspectives on how symmetry governs the interplay between norms and numerical radii.