Abstract
The Berezin number and Berezin norm have become indispensable tools for analyzing the structure of operators on reproducing kernel Hilbert spaces, yet the gap between these quantities and the operator norm is still far from fully understood. In this paper, we narrow that gap by establishing several new reverse-type inequalities for and under natural disc-localization and accretivity conditions. Combining a refinement of the Schwarz inequality due to Dragomir with Buzano’s inequality, we further derive sharp bounds for and for products , together with their power-type extensions. Applying the polar decomposition, we also obtain companion inequalities for and the Aluthge transform. Finally, for operators with closed range, we use the Moore–Penrose inverse to derive new reverse-type Berezin inequalities that refine and complement the results obtained via the polar decomposition, and we illustrate the sharpness of these bounds with an explicit example.
Keywords:
Berezin number; Berezin norm; reproducing kernel Hilbert spaces; numerical radius; operator norm MSC:
05C38; 15A15; 05A15; 15A18
1. Introduction
The numerical radius of an operator A acting on a Hilbert space H is defined by
It turns out that defines a norm on the Banach algebra of all bounded linear operators
This norm is equivalent to the operator norm, as the following double inequality illustrates:
for any .
Reproducing kernel Hilbert spaces play a key role as a central tool across a range of disciplines, including machine learning and statistics, and they arise naturally in complex analysis, group representation theory, integral operator theory, and probability theory as well.
We now fix the notation and terminology used throughout the paper. Let be a non-empty set and denote the set of all functions from to the complex field . A set is called a reproducing kernel Hilbert space (RKHS for short) on if is a Hilbert space and for every , the map is bounded, where for all . Let denote the inner product of . When no confusion can arise, we write H for in statements about vectors, and I for the identity operator on . The Riesz representation theorem guarantees that, for each , there exists a unique vector such that for every . The associated function given by
is referred to as the kernel function of the RKHS .
For , let be the normalized reproducing kernel of , where is the norm associated with the inner product defined on . Notice that the following set { is a total set in . For any bounded linear operator , the function defined on by is the Berezin symbol of V, which was first introduced by Berezin [1,2]. The Berezin transform of an operator provides important structural information about that operator. For more details, the reader may consult, for instance, [3] and its references or [4]. For some applications related to the Berezin symbol of operators, the interested reader is invited to consult, for example, the following references [5,6,7,8].
We recall that the Berezin number and the Berezin norm of the operator V are defined by
and
where , are two normalized reproducing kernels of the space (see [9] and references therein).
In recent years, several inequalities involving the Berezin number and Berezin norm of operators have been established (see, for instance, [10,11,12,13] and the references therein).
For , we can consider, see for example [14], the following quantity:
It is not difficult to see that
where stands for the usual operator norm of V. It is well known that the inequalities (2) are in general strict.
It is well-known that the operator norm on Hilbert spaces is submultiplicative. However, this property may not be correct in general for the Berezin norm of operators even if it is for positive operators; see [10,14].
By the definitions of and , the following properties
hold immediately for every . Here denotes the adjoint of V. It should be mentioned here that the equality fails to be true for some . We also observe that .
It is also known that for every positive integer n and the equality holds if V is a selfadjoint operator, i.e., , (or in particular if V is positive). However, the above two properties do not hold, in general, for the Berezin number of operators.
For an account of the results involving the numerical range and radius of operators, we refer the reader to [13,15,16,17] and references therein.
It has been recently proven in [10] that if , then
however this equality does not hold, in general, for self-adjoint operators.
We also observe that if then
By taking we also have the equalities
for all
Inequalities of this type have numerical-radius counterparts based on Buzano’s inequality and Schwarz-type refinements [18,19]. Berezin-number and Berezin-norm inequalities have been widely studied [10,20,21], including with Buzano’s inequality [22,23] and the Moore–Penrose inverse [23,24,25]. Our method is of the same kind (a vector inequality applied to the normalized kernels , followed by a supremum over ), but the estimates differ in form: reverse-type bounds for under disc localization (Section 2); bounds for and via Dragomir’s Schwarz refinement and Buzano’s inequality (Section 3); results for and the Aluthge transform via polar decomposition (Section 4); and reverse-type estimates in which enters through the condition (Section 5). To the best of our knowledge, these inequalities are new and do not appear elsewhere in the literature.
The rest of the paper is organized as follows. In Section 2, we establish reverse inequalities for and under disc-localization conditions of type (5). In Section 3, we combine Dragomir’s refinement of the Schwarz inequality with Buzano’s inequality to obtain bounds for and for products , together with power-type extensions. In Section 4, we use the polar decomposition to derive companion inequalities for and the Aluthge transform. Finally, in Section 5 we obtain reverse Berezin inequalities that involve the Moore–Penrose inverse of an operator. Section 6 contains concluding remarks.
2. Reverse Berezin Inequalities via Disc-Localization
As shown in (2), the Berezin number is dominated from above by , but the two quantities do not coincide in general. In this section we obtain reverse-type inequalities that bound the gap from above whenever T is localized in a disc of the complex plane. The following results may be stated:
Theorem 1.
Let If and are such that
where I is the identity operator on then
Proof.
Let be a normalized reproducing kernel of For we have from (5) that
giving that
Taking the supremum over in (7) we get the following inequality that is of interest in itself:
Since, by the elementary inequality for real ,
hence by (8) and (9) we deduce
which is equivalent to the desired inequality (6). □
Example 1.
The following corollary may be stated:
Corollary 1.
Let and with If
then
Proof.
Utilizing the fact that in any Hilbert space the following two statements are equivalent:
- (i)
- (ii)
Remark 1.
We say that an operator is accretive, if for every One may observe that the Assumption (10) above is then equivalent to the fact that the operator is accretive.
Perhaps a more convenient sufficient condition in terms of positive operators is the following one:
Corollary 2.
Let with and If is self-adjoint and
in the operator order, then
Corollary 3.
Assume that are as in Theorem 1. If, in addition,
for some then
Remark 2.
In particular, if , and if we take , , then we get
The following result may be stated as well.
Theorem 2.
Let and with If
then
Proof.
Remark 3.
Squaring (21), we get the inequality
The following corollary holds.
Proof.
If we consider and then
Now, on applying Theorem 2, we deduce the desired result. □
The following result may be of interest as well.
Theorem 3.
Let and , with If
then
3. Berezin Inequalities via Buzano’s Inequality
We now turn from the disc-localization approach of Section 2 to a different technique, based on Buzano’s inequality, which allows us to control and the Berezin norm of products and without any localization assumption on T. By making use of the celebrated Buzano’s inequality, we can state the following result as well:
Theorem 4.
Let . Then
Proof.
We need the following refinement of Schwarz’s inequality obtained by S.S. Dragomir in 1985 [26] [Theorem 2] (see also [27,28]):
provided are vectors in H and
Observing that
hence by the first inequality in (40) we deduce
This inequality was obtained in a different way earlier by M. L. Buzano in [29].
If we replace e with for we get
Now, in (41), choose , and to get
for any
Moreover, if we replace x with , then we get
for all
Let be a normalized reproducing kernel of For we have from (43) that
for
By taking the supremum over and using,
and
for nonnegative functions , we get
which gives the desired result (39). □
Corollary 6.
Let . Then
The proof follows by (39) on taking .
The following three examples show that the bound (44) in Corollary 6 can be attained with equality, and that it can also be strict.
Example 2.
In Example 1, D is self-adjoint, and ; therefore
i.e., the inequality of Corollary 6 is strict for this operator.
Example 3.
and
Hence the right-hand side of (44) equals
so equality holds for every diagonal self-adjoint T. Taking gives the concrete instance .
Let with the standard inner product and , so the normalized reproducing kernels are . For , let
Since T is self-adjoint, , and . Because T acts diagonally on the reproducing-kernel basis we have
Example 4.
On the same space, let
Here , so . A direct computation gives , so . Since , the columns of T have norms 1 and , and those of have norms and 3; thus, and . The right-hand side of (44) is , while the left-hand side is 9. This non-self-adjoint example shows the bound is strict in general.
We next establish a power-type version of the preceding inequalities:
Theorem 5.
Let and If then
where
Also,
Proof.
We use the following inequality for vectors in inner product spaces due to Goldstein, Ryff and Clarke [30]:
provided and with
Now, let with From the hypothesis of the theorem, we have that , and applying (47) for the choices , , , we get
for any , and
Let be a normalized reproducing kernel of For we have from (48) that
Taking the supremum over , we get
which proves (45).
Remark 5.
We observe that, if we take with and then by (45) we get
where , while from (46) we get
For in this last inequality, we have that
while for , we get
Moreover, we can state the following bounds for the Berezin norm
Theorem 6.
Let with in the operator order and Then for any and one has the inequality:
Unlike its original (unconditional) form, this estimate is no longer valid for an arbitrary : the lower bound , is essential, since it is precisely what allows to control the normalization in the proof below. The unrestricted case remains available as a special case, since one may then take .
Proof.
We use the following inequality obtained by S. S. Dragomir in [28]:
to get:
for any , and .
Choosing in (50) , , , we get
Now, if we take with then we also get
for all which gives for that
for , since for ,
Remark 6.
Let be as in Theorem 6. Then from (49) for respectively, and for any one has the inequalities:
and
These imply that
We observe from (49) for and we get for that
For we get
for we obtain
while for we derive that
for all .
Theorem 7.
Let Then
and, if ,
Proof.
We know that for any , (see, for instance, [31]) the bound
actually implies that
for any and .
Now if in (55) we choose , , , then we obtain
for any .
Let be a normalized reproducing kernel of For we have from (56) that
and if we take the supremum over , then we get the desired result (53).
If we take , in (55), then we obtain
for any , and .
Remark 7.
We have for that
So, if
then by (54) and (58) for we get
which gives that
provided that (59) is valid. This shows that the inequality (26) holds in fact without the restriction Here is assumed in the two displayed ratio inequalities, in accordance with Theorem 7; for , the inequality (26) is trivial.
Theorem 8.
Let Then for and we have that
In particular,
Proof.
We use the following inequality obtained by Dragomir and Sándor in [27]:
for any and
Now, if we choose , , then we get
for any ,
4. Berezin Inequalities via Polar Decomposition
We now combine the Buzano-type inequalities of Section 3 with the polar decomposition of an operator in order to obtain reverse Berezin inequalities for the operator modulus and for the Aluthge transform of T. Every bounded linear operator on a complex Hilbert space H can be uniquely factored as:
The operator is defined via the continuous functional calculus as the unique positive square root of . The operator U is a partial isometry. It acts as an isometry on the orthogonal complement of the kernel (null space) of T, mapping it isomorphically onto the closure of the range of T.
We need the following fact:
Lemma 1.
If is a continuous function satisfying , then:
where .
Proof.
For the sake of completeness we give a simple proof as follows.
We first prove that
Observe that
Recall that represents the orthogonal projection onto . Since the operator vanishes identically on its own kernel, then
Substituting back, gives
Further, we have that
We also have that and and then
Hence, we have verified that:
Because , the operator is positive. By the unique existence of the positive square root of the positive operator , we conclude:
We extend the property to higher powers using induction for all integers . Assume that the identity holds for ,
We want to prove it for :
We insert the projection directly before the final term, namely,
Applying the induction hypothesis yields:
Therefore, by mathematical induction, holds for all integers .
Let be an arbitrary polynomial satisfying . We obtain:
Let be a continuous function satisfying . By the Stone–Weierstrass Approximation Theorem, there exists a sequence of polynomials with that converges uniformly to on the compact domain . The continuous functional calculus assures that uniform convergence of a sequence of continuous functions implies the norm convergence of their corresponding operators:
Since U and are bounded linear operators, they preserve limits under the operator norm topology:
Substituting our polynomial identity derived above yields:
This completes the proof. □
For the power function , , we then have that
Theorem 9.
Let For we have that
In particular,
and
Proof.
In the following examples, we discuss the sharpness of (66). Let , .
Example 5.
Let
with . Then , so , and
gives . Both sides of (66) (with ) equal , so the equality holds for every positive diagonal T.
The equality in Example 5 is not surprising, since T is already positive; the next example shows that (66) can still be an equality even when T is far from self-adjoint.
Example 6.
Let
Then
and . Also (its only nonzero singular value) and . Thus : the equality holds even for this non-self-adjoint nilpotent shift.
The two preceding examples might suggest that (66) is always an equality; the following example shows this is not the case in general.
Example 7.
Finally, let
Here , with eigenvalues , so and . Diagonalizing to compute its square root gives , so , and the left-hand side is . The right-hand side is . This example shows the inequality is strict in general.
We close this section with the following bound for the Aluthge transform:
Theorem 10.
Let and be the polar decomposition of V. If is the Aluthge transform of namely, then
5. Reverse Berezin Inequalities via the Moore–Penrose Inverse
In this final section, we complement the disc-localization and polar-decomposition techniques of Section 2, Section 3 and Section 4 by involving, for operators with closed range, the Moore–Penrose inverse in reverse Berezin-type inequalities. This provides a further tool, alternative to the ones used above, for estimating the gap between , , and . A fundamental concept in operator theory is the Moore–Penrose inverse, which generalizes the notion of operator inverses to more general settings. In a related direction, Ren, Ighachane and Benchiheb [32] recently obtained forward-type Berezin number bounds for operators admitting a Moore–Penrose inverse, using interpolational means and McCarthy-type inequalities; the results below are of a complementary, reverse-type nature, derived instead from the disc-localization condition .
For an operator M∈, the Moore–Penrose inverse of M, denoted by , is the unique operator satisfying the following four Penrose equations [33],
These conditions ensure the existence and uniqueness of whenever M has a closed range, i.e., range is closed in
We define the set of all operators in that admit a Moore–Penrose inverse as follows:
For any , its Moore–Penrose inverse is also a bounded operator in .
For an operator , the Moore–Penrose inverse satisfies the following important properties:
- Self-adjoint property: If M is self-adjoint, i.e., then is also selfadjoint and
- Involutivity: The Moore–Penrose inverse satisfies the equality
- Order reversal: If with in the operator order, then provided are positive and boundedly invertible, in which case , and this is the classical operator-antitonicity of the inverse (see [34]). This property is recalled for completeness only and is not used below.
Note that is the orthogonal projection onto and ; in particular , , and when M is invertible. These are the only properties of used in the proof of Theorem 11.
We have the following inequalities:
Theorem 11.
Let If and are such that
then
Also, we have that
Proof.
We observe that for we have that
For with we have that
Since
and
then by (73) we get that
namely,
for with since for every ; hence,
Let be a normalized reproducing kernel of For we have from (75) that
If we take the supremum over , we get that
Since , we have by (3) that
and by (76) we get (71).
From (74) we also have that
for with
Remark 8.
By taking and in Theorem 11 we can also state that:
Corollary 7.
Let and with If
then
and
Remark 9.
Let If are such that either is accretive, or, sufficiently, is self-adjoint and
then, by Corollary 7 we have that
and
Here because , so and ; thus, the first bound may equivalently be written with .
Assume that ; then, by (78) we get for and that
We will show that this inequality is sharp. Here we take with equal to the unit sphere of H, so that ranges over all unit vectors of H and reduces to the numerical radius ; in particular, for every positive P, which we use below.
Consider the matrix which has the characteristic polynomial , giving the spectrum . Because both eigenvalues fit within the closed spectrum , the operator ordering is verified.
Compute the matrix square :
The eigenvalues of this positive-definite matrix are given by namely, . The maximum eigenvalue is . Thus:
Also,
The eigenvalues are , meaning . The Berezin number matches the dominant boundary spectral point, . Calculating the full expression gives:
These prove that the inequality (79) is sharp. This shows that the equality holds in (79) for this example, so neither the coefficient 4 nor the additive constant 1 can be decreased while the other is held fixed; in this sense, the constants in (79) cannot be improved.
6. Conclusions
In this paper we established several families of reverse-type and refined inequalities for the Berezin number , the Berezin norm , and the associated quantity on reproducing kernel Hilbert spaces. The main contributions can be summarized as follows.
- Under disc-localization conditions, we showed that the gap is bounded above by quantities depending only on the center and radius of the disc (Theorems 1–3 and their corollaries), and that equality in Theorem 1 is attained on the Hardy space (Example 1).
- Combining Buzano’s inequality with Dragomir’s refinement of the Schwarz inequality, we derived an upper bound for (Theorem 4), together with power-type estimates (Theorem 5) and the bound(Theorem 7).
- Using the polar decomposition, we transferred these estimates to and to the Aluthge transform of T (Theorems 9 and 10).
Taken together, these results refine and unify several known Berezin-number and Berezin-norm inequalities. Equality is attained in several of the displayed estimates, as shown by the corresponding examples; in particular, the constants identified explicitly in those results cannot be improved under the stated hypotheses. It would be of interest to investigate whether analogous disc-localization and reverse-type estimates hold for other operator radii and to explore applications of these bounds to Toeplitz and Hankel operators on reproducing kernel Hilbert spaces.
Author Contributions
The Methodology, R.K.A. and S.S.D.; Formal analysis, R.K.A. and S.S.D.; Writing—original draft, R.K.A. and S.S.D.; Writing—review and editing, R.K.A. and S.S.D.; Funding acquisition, R.K.A. The authors declare that they have contributed equally to this work. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Ongoing Research Funding Program (ORF-2026-802), King Saud University, Riyadh, Saudi Arabia.
Data Availability Statement
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
Acknowledgments
The authors sincerely appreciate the reviewers for their valuable comments and suggestions, which have greatly improved this paper. Additionally, the first author wishes to express her gratitude for the support received from the Ongoing Research Funding Program (ORF-2026-802), King Saud University, Riyadh, Saudi Arabia.
Conflicts of Interest
The authors declare no conflicts of interest.
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