Abstract
This article explores two distinct function spaces: Hilbert spaces and mixed-Orlicz–Zygmund spaces with variable exponents. We first examine the relational properties of Hilbert spaces in a tensorial framework, utilizing self-adjoint operators to derive key results. Additionally, we extend a Maclaurin-type inequality to the tensorial setting using generalized convex mappings and establish various upper bounds. A non-trivial example involving exponential functions is also presented. Next, we introduce a new function space, the mixed-Orlicz–Zygmund space , which unifies Orlicz–Zygmund spaces of integrability and sequence spaces. We investigate its fundamental properties including separability, compactness, and completeness, demonstrating its significance. This space generalizes the existing structures, reducing to mixed-norm Lebesgue spaces when and to classical Lebesgue spaces when . Given the limited research on such spaces, our findings contribute valuable insights to the functional analysis.
Keywords:
self-adjoint operators; Hilbert spaces; operator convexity; mixed-variable exponent spaces; Zygmund space MSC:
05A30; 26D10; 26D15
1. Introduction
Inequalities in function spaces are a cornerstone of functional analysis and the associated disciplines, which give basic constraints relating the functions, integrals, derivatives, and their norms. They are used extensively to study the behavior of functions in a wide variety of mathematical fields. For example, Sobolev and Poincaré inequalities have been used to prove the existence and uniqueness of PDE solutions [], while Hölder’s and Minkowski’s inequalities are used to prove operator boundedness []. In addition, inequalities are a key tool in energy functional minimization and solving variational problems [], approximating differential operators’ eigenvalues via the Rayleigh quotient [], and proving the stability of numerical schemes using norm inequalities [].
The literature encompasses, in depth, a number of integral inequalities, e.g., Hermite–Hadamard, Giaccardi, Jensen, midpoint, Young’s, Trapezoid, Simpson, Ostrowski, and so forth. For example, the Hermite–Hadamard inequality has been studied in many directions, e.g., its stochastic counterpart [], fuzzy-valued mappings [], center–radius order relations [], inclusion order [], and Hilbert spaces []. Similarly, other inequalities have been explored using similar techniques, e.g., the Ostrowski-type inequality, which has been generalized in stochastic environments [] under various order relations [,] and in function spaces []. For further information on different inequalities, we refer to [,,,,] and the references therein.
The Maclaurin series with an integral remainder term is an effective tool for approximating functions and expressing the approximation error precisely. The integral remainder form is particularly useful in theoretical and numerical approximations when error control is crucial.
1.1. Benefits and Significance of the Maclaurin Series Approximation
- Function Approximation: Replace complex functions with simpler polynomial expressions, which are utilized in calculators and computer software to evaluate functions with high precision.
- Economics and Finance: Investigate physical systems near reference states and use Hamiltonians to solve quantum systems with perturbative relativistic formulas for low velocities.
- Numerical Methods: Use expansions to generate numerical differentiation formulas and analyze truncation mistakes to ensure accuracy.
- Machine Learning: Optimize loss functions and enhance algorithms. Gradient descent inequality employs first- and second-order approximations to assess convergence.
- Physics and Engineering: Approximate potential energy functions at equilibrium points are used to expand state equations or partition functions in systems close to equilibrium.
For more applications in various disciplines, we refer to the following articles [,,]. This widely recognized equality can be stated as follows.
Theorem 1.
Let be a function on some set with continuous derivatives. Then, for any , we have
where is the remainder term after terms. The remainder term can be expressed in integral form as
This formula shows that the error depends on the -th derivative of and the behavior of on the interval .
Variable exponent spaces and Hilbert spaces are inherent mathematical structures of varied use across various disciplines. Their joint application strengthens the analysis of complex systems via a more efficient and effective approach. They are, for example, essential for modeling relations in complex systems where evidence is characterized by extreme variability []. They enhance approximation in approximation theory through the improved representation of functions via integral operators [].
Variable exponent spaces are used in partial differential equations (PDEs) with non-traditional growth conditions [] and the study of nonlinear equations, especially in dynamical systems, where normal techniques break down due to irregularities or complexity []. Hilbert spaces, nonetheless, are the bedrock of quantum mechanics, wherein quantum states and observables are constructed within their theory []. Hilbert spaces are also extensively employed in signal processing and wireless communications, where linear operator algebra must be used in signal analysis []. Moreover, statistical inference has a lot to do with Gaussian Hilbert space measures [], through which statisticians can place bounds on approximations and the optimization of linear rules under various constraints. For recent developments in other areas, we refer to [,].
The significance of variable exponent spaces and Hilbert spaces has led to numerous original results and well-known inequalities in such spaces. Hilbert spaces and tensor operations have been utilized by Frank Hansen and Huzihiro Araki [] in proving Jensen’s operator-type inequalities. In [], the authors compared the two spaces side by side and derived numerous striking results related to Hadamard and triangular-type inequalities by utilizing various unified and generalized convex mappings. Prasenjit and Tapas Kumar [] also worked with frames in the tensor product space of n-Hilbert spaces and generalized earlier results from bases to frames and dual frames in tensor product spaces.
Theorem 2
([]). Assume and be the class of vectors contained in and Hilbert spaces. The vector product is a frame related to for iff is a frame related to for and is a frame related to for . Since and are dual frames corresponding to and of and , respectively, for all ,
Then, for all , we have
Vuk Stojiljkovic [,] used classical integral operators and twice-differentiable mappings to the continuous functions on self-adjoint operators in Hilbert space to develop Simpson and Ostrowski-type double inequalities.
Theorem 3
([]). Let and be self-adjoint operators, and let be their associated spectrums. Assuming that is a continuous function on we obtain
Dragomir [] investigated multiple bounds and developed a well-known classical trapezoid-type inequality in the context of tensor Hilbert spaces.
Theorem 4
([]). Let and be self-adjoint operators, and let be their associated spectrums. Assuming that is a continuous function on we obtain
for all .
Khan et al. [] used fractional integral operators and several forms of extended convex mappings to find boundaries for Simpson and Hermite–Hadamard inequalities.
Theorem 5
([]). Let and be self-adjoint operators, and let be their associated spectrums. Assuming that is a continuous function on we obtain
For more current results related to the results in this article, see [,,,,,,,,] and their references.
Variable exponent Lebesgue spaces are a modern mathematical analysis field that developed from the conventional Lebesgue space. In 1931, W. Orlicz [] developed sequence spaces with variable exponent and discussed some of their properties. In the same paper, Orlicz defined the class of measurable functions such that , thereby generalizing the concept of classical Lebesgue spaces of integrability. In this work, they let the exponent depend on the point in the domain ∆. Their adaptability makes them better suitable for simulating physical processes, in which the space’s behavior may vary locally in a nonlinear way. Though first emerged in 1931, they were initially analyzed as Banach spaces in [].
Taking inspiration from these various authors, they constructed and generalized numerous other forms of function spaces and examined their features and boundedness of operators with varied exponents. For instance, in [], the authors used spaces of variable integrability to study uniform convexity; in [], they studied some interesting problems related to the fixed point theorem in the varying exponent spaces; in [], they examined the boundedness of certain operators on grand Herz spaces with variable exponents; in [], they examined interpolation theorems for variable exponent Lebesgue spaces; and in [], they examined modular-proximal gradient algorithms in varying exponent spaces. Fernanda and James [] investigated the algebraic and topological reflexivity features of sequence spaces with varying exponents. Amri et al. [] employed modular function spaces to examine fixed point theorem with application in the space of variable integrability. Haim Brezis [] explored different kinds of function spaces and discussed problems related to reflexive spaces, separable spaces, and uniform convexity. Zhenghua Luo and Qingjin Cheng [] investigated a novel convexity property of convex functions, resulting in a characterization of the class of reflexive Banach spaces. Jain et al. [] investigated quasi-reflexivity and the sup of linear functions with interesting applications. Enrico Pasqualetto and Tapio Rajala [] studied first-order Sobolev spaces on reflexive Banach spaces, using relaxation, test plans, and divergence.
Completeness, compactness, and the separability of variable exponent spaces play a very essential role in several areas, such as checking the boundedness of operators, checking the existence of the solution of a partial differential equation, embedding of spaces, etc. For instance, the authors examined these three properties for Herz spaces in [], demonstrated separation axioms, representation theorems, and compactness in [], examined completeness for Moore-closed spaces and centered bases in [], determined compactness and separability in [], and demonstrated several pertinent results in weighted Lorentz spaces using the Hardy Operator in [].
Bandaliyev et al. [] studied the precompactness and completeness of sets in variable exponent Morrey spaces using bounded metric measure spaces.
Theorem 6
Then, is totally bounded in .
([]). Let be a bounded doubling metric measure space, and let satisfy and . Assume that a subset of satisfies the following:
- is bounded in ;
- For some ,
Orlicz–Zygmund spaces are a class of function spaces that generalize both Orlicz spaces and Zygmund spaces. They enable logarithmic changes to the evolution of Orlicz functions, making them helpful in a variety of applications including nonlinear analysis and partial differential equations. For instance in [], the authors investigated the Lipschitz regularity for local minimizers of convex variational integrals of the form using Orlicz–Zygmund spaces with variable exponents; in [], the authors investigated the higher differentiability of minimizers of variational integrals with variable Orlicz–Zygmund spaces; in [], the authors investigated global gradient estimates for the borderline case of double-phase problems with BMO for Orlicz–Zygmund spaces; and in [], the authors investigated regularity for double-phase energy functionals of different power growth.
For further recent results relevant to variable exponent spaces, see [,,,,,,] and the references therein.
1.2. Main Contribution
The article makes two significant contributions. The first is that tensor approximation can be used to extend equality (1) from the lower dimension (classical sense) to a higher dimension since tensor approximation deals with a wide range of complex functions in a very straightforward manner. Second, we can introduce a new norm structure, as Orlicz–Zygmund spaces with variable exponents have been extensively studied for analyzing problems involving variable growth conditions and non-standard behavior in partial differential equations.
The Zygmund space , for for is defined as the Orlicz–Zygmund space generated by the Young function
Therefore, the Orlicz–Zygmund space is defined as
and it becomes a Banach space with the Luxemburg norm
The main idea we use is a combination of the variable sequence space and variable Orlicz–Zygmund space that is merged simultaneously, and we introduce a new type of modular functional with an associated norm that is based on these two spaces. In this space, we define a new norm structure that we call mixed-variable Orlicz–Zygmund space and investigate several well-known properties, such as completeness, separability, and precompactness. Since these types of spaces related to developed results are rare in the literature, this paper will play a very important role in the advancement of results related to these in function spaces.
As a main motivation for this study, we were primarily influenced by the works of the following authors [,,,].
The work is organized into six sections. In Section 2, we will recall some basic definitions and arithmetic operations in tensor Hilbert spaces. In Section 3, we construct our major conclusions, including Maclaurin-type approximation, using various types of generalized convex mappings in tensorial Hilbert spaces. In Section 4, we define a few well-known notions and results that we need to support our primary conclusions regarding Orlicz–Zygmund spaces and other related spaces. In Section 5, we demonstrate the separability, completeness, and compactness of newly defined mixed-Orlicz–Zygmund spaces. In Section 6, we discuss our main findings and some future possible work related to these results.
2. Preliminaries
In this section, we recall some fundamental concepts related to extended convex mappings and arithmetic operations in tensor Hilbert spaces. The following articles [,] contain further important concepts and discoveries related to this section.
Definition 1
([]). An inner product on a complex linear space is a map
such that for all and , we have
A complete inner product space is called a Hilbert space.
Definition 2
([]). Let be a bilinear mapping. The related tensor product of with on Hilbert space is defined as follows:
- The Hilbert space contains the class of all vectors, and is its span;
- for . If is a Cartesian product of and , it is typical to read as instead of . A Cartesian product and a function of into satisfy the following axioms
Let is taken to be bounded mapping defined in terms of intervals. Assume that be a -tuple of the self-adjoint operators associated with Hilbert spaces. Then,
is the spectrum of possible operators for ; following [], we define as follows:
When the dimensions of Hilbert spaces are finite, many complicated processes can be considerably reduced by integrating processes into finite summations. The authors [] expand the construction, and it is defined as follows:
where can be split as a product of one-variable mappings .
If is sub(super)-multiplicative across the interval , then
and additionally, if is continuous on , one has
Consequently, it may be shown that if
where and are spectral resolutions for function on , then
The geometric property of linear bounded operator is represented as follows:
where and
By the definitions of # and ⊗, we have
Examine the final result that is comparable to the tensorial product that is
that holds . If we take and , then we obtain
Through induction, we have
Specifically,
for all . Additionally, we note that the and are commutative with each other that is
Moreover, for any two natural numbers
Using the functional calculus and tensorial product characteristics for continuous functions of self-adjoint operators the associated tensorial perspective , is defined as follows:
due to the commutativity of and .
Lemma 1
([]). Let and be self-adjoint operators with and . If are continuous on , then are continuous on , and is continuous on , and subsequently the class of intervals is
where and are the spectral resolutions
Definition 3
([]). A mapping is stated to be convex (concave) on , if
valid for all and .
Definition 4
([]). A mapping is stated to be convex in a quasi sense if
for all and .
Definition 5
([]). A mapping is stated to be Godunova convex on , if
valid for all and .
The following lemma, whose properties are also utilized in our main findings, is presented in a similar manner using synchronous functions in the work of Dragomir [].
Lemma 2.
Assume that are asynchronous and continuous on , while are continuous and non-negative on . If are self-adjoint with spectrums , then
or, equivalently
Proof.
Suppose and are asynchronous functions on ∆, then
for all .
To obtain this inequality, we multiply it by and it follows that
for all .
If we use the double integral, we obtain
Note that
and
Now, by applying the tensorial property
for any , we obtain
and
which proves Lemma 2. □
3. The Major Results
These two results play an important role in our further subsequent findings related to Macalurin approximation equality.
Lemma 3.
Suppose that and are self-adjoint operators such that and . If are continuous on are continuous on and , are continuous on interval , subsequently the sum of is
Proof.
Let and positive-continuous functions such that , for and for . Then,
By applying Lemma 2 to the functions and , we obtain
Now, observe that, by the commutativity of the operators and ,
and, similarly
Hence, we obtain the desired representation for Lemma 3. □
Theorem 7.
If is a Godunova–Levin mapping defined over the positive half-line, then is also Godunova–Levin in pairs of positive definite operators. If and , then also
Proof.
Suppose that is a operator Godunova–Levin function in the positive half-line. Given the commutative nature of and , we have
for . If and , then we have
and it demonstrates that is a Godunova–Levin operator in pairs of positive definite operators. If and , then and , so we have
□
Tensorial Maclaurin Approximation Using Different Types of Generalize Convex Mappings
Lemma 4.
Assume that is of class on the open interval . Let and be self-adjoint operators with respective spectrums, and , then one has
Proof.
Using Maclaurin’s representation with the integral remainder, we can express the following two identities:
for .
Using the substitution and for any continous function with distinct values in that interval, one has
Therefore,
and it follows that
for all . If and have the spectral resolutions
and after that, we obtain the integral applying over , that is
We have
and, by Lemma 3, we have
Using Fubini’s theorem, we obtain
and so we obtain the required outcome. □
Theorem 8.
Assume that is of class on the open interval such that for some constants Let and be self-adjoint operators with respective spectrums, and , then one has
Proof.
For with in (4), we have
Based on the boundedness assumption, we have that
for all and .
Multiplying the inequality (7) by with yields
By taking the integral over in (8), we obtain
Using Lemma 4, we derive
In addition, if we multiply this inequality by , and integrate, we obtain
which gives that
namely
By utilizing identity (6), we derive the desired inequality. □
Theorem 9.
Assume that is of class on the open interval such that is a convex function on . Let and be self-adjoint operators with respective spectrums, and , then one has
Proof.
Since is convex on ∆, then
for all and .
Now, if we multiply the above inequality by with , then we have
for all and .
Theorem 10.
Assume that is of class on the open interval such that is a quasi-convex function on . Let and be self-adjoint operators with respective spectrums, and , then one has
Proof.
Example 1.
Under the identical hypothesis of Theorem 8, and if , then by (6), we obtain
for any self-adjoint operators . If , then by (5), we obtain
From (9), we obtain
for any self-adjoint operators . If and if we take in (15), , then we have
and for , we have
If , then by (16), we obtain
and for , we have
From (17), we obtain
and for ,
for all . Using the commutativity of and , we may deduce from (18) that
provided that .
4. Preliminary Results
In this section, we will recall some fundamental definitions and conclusions related to mixed norm function spaces with varying exponents. Reference [] contains the majority of the properties linked to function spaces, whereas mixed Lebesgue spaces are covered in the following articles [,,].
4.1. Semi-Modular Spaces
The variable Lebesgue spaces are categorized as semi-modular spaces, which is a generalization of a normed space. For this purpose, we first review certain modular space definitions and results.
Definition 6.
Let be a vector space over some field (real or complex). The mapping is said to be semi-modular on if
- ;
- for all and ;
- if for all , then ;
- function is left-continuous for every ;
- function is non-decreasing for every .
If a vector space has as a semi-modular on it, then the space
known as a semi-modular space, is connected to the functional and is defined as follows:
Proposition 1.
Suppose that on vector space , is semi-modular. Consequently, for each , we have
Proof.
Assume that . Then, is inferred from the definition of the norm . Conversely, if we assume that , then for each and due to the left continuity of the function, we have
□
4.2. Variable Exponent Spaces
Now let us review the idea of space. To do this, we take into consideration that is a measurable set and we define the measurable function , which is normally described as follows:
We refer to any Lebesgue measurable functions as variable exponents such that . For the set ∆, represents the space of all varying exponents functions. The notation marks the pack of all varying exponent functions such that .
Consider the vector space , which contains all measurable functions over ∆. For , over the space we define the semi-modular as follows:
where
for each . This modular space is described by the semi-modular , which in turn defines the space as follows:
The following Luxemburg quasi-norm makes the space a quasi-Banach space:
It is the particular case of the Musielak–Orlicz space, and whenever , then becomes Banach space. When the exponent is constant, it reduces to the classical Lebesgue space.
Notably, the definition of space becomes simpler if so that if, and only if
Certain presumptions on the exponent function are required for a few claims in this work. Specifically, function is locally log-Hölder continuous on ∆, if there exists and for all , we have
We say that satisfies the log-Hölder continuous at infinity (or has a log decay at infinity), if there exists and a constant and for all . We have
If both of the aforementioned criteria are met by the function , we declare that is globally log-Hölder continuous on ∆. The corresponding class of functions is defined as follows:
For the semi-modular and the quasi-norm , we now formulate several well-known and practical statements.
Proposition 2.
Let with and let . Then,
We now recall a generalized Orlicz–Zygmund space with variable integrability.
Definition 7
([]). The space is defined for each as
and the associated Luxemburg norm is defined as follows:
it becomes a Banach space.
4.3. Mixed Norm Function Spaces
Definition 8
([]). Let . The mixed Lebesgue sequence space is defined on sequences of as follows:
where the corresponding Luxemburg norm, typically defined as
Under certain assumptions [], the space becomes a normed space.
Definition 9
([]). Consider where . The mixed norm Morrey-sequence space encompasses all measurable sequences such that
5. Main Results Related to Mixed-Orlicz–Zygmund Space
The aim of this section is to develop a new norm structure by merging two different types of function spaces and analyze whether this space is complete, separable, and compact. First, taking into account Definition 7 and Definition 8, we propose a new type of function space and its related modular and norm, which we use in our major results.
Definition 10.
Let . The mixed generalized Orlic Zygmund sequence space is defined on the sequences of as follows:
If we consider the following convention , the norm is defined as follows:
If , then
5.1. Completeness
First, we demonstrate that this modular space is a quasi-Banach space.
Theorem 11.
If , then is a quasi-Banach space.
Proof.
To demonstrate is a quasi-Banach space. We first show that it is a quasi-normed space. By Definition 6, we just need to consider the quasi-convexity. Let and define and . Then, clearly . Thus, we have
and this completes the proof. The next step is to demonstrate the space’s completeness. For this purpose, we fix a Cauchy sequence in . Hence, for any , there exists ℘ such that for all , we have
By the quasi-norm’s definition
and hence
In particular, for each , we have
Hence, by the definition of the space , we obtain
Thus, passing with , we obtain . Thus, we demonstrate that for each is a Cauchy sequence in . Thus, for every , there exists such that
Now, we shall prove that
where . For this purpose, we require the following:
In order to prove (20), we introduce the following notation:
Let be a subsequence such that
Hence, for each there exists such that for all , one has
Thus,
Next, we apply the Fatou Lemma and the fundamental characteristics of the norm, and we obtain
Hence,
Finally, applying , we obtain (20). Now, we can prove (19). Since is a Cauchy sequence in , for we have
Therefore, by (20), for each and , we have
Then, passing , we obtain
□
5.2. Separability
The next result shows that the mixed norm space is separable when the exponents satisfy .
Theorem 12.
If and , then is separable.
Proof.
For each , we define
It is clear that represents a closed subset of and
We divide the proof into two parts. First, we show that for each , the space is separable. Since , we know that is separable. So there is a countable subset of , say, such that which . Set
Obviously is countable, so it is enough to show that . Let us fix and . Then, there exists such that for , we have
Then, we have , and we obtain
Therefore,
Now, we have show that
To prove it, we let , then
where . Therefore, for every , ∃ such that
Thus, we obtain
On the other hand, we have
Thanks to (22), we have
Then, in view of (21), we have
Finally, let us define
It is clear that , and by (24), we obtain , which is the desired result. □
Proposition 3.
Let and let . If for each , then
It suffices to show that
Suppose that is such that
Let us fix and let be such that
Due to assumption that , we obtain
Therefore,
Finally, it easily follows that
and this concludes the proof.
Lemma 5.
Assume that with . Then, for every , we have
Proof.
Suppose , and we define the sequence in such a way that . Then, for each we have , as sequences preserve their order, it follows as
Finally, by Proposition 3, we obtain
and the proof follows. □
5.3. Precompactness
Theorem 13.
Let , and assume that . Then, the class is precompact in if the following assumptions are met:
- (a) is precompact in ;
- (b) For all there exists such that
- (c) For all and , then
- (d) For all .
Proof.
Initially, we will demonstrate that the conditions are satisfied. The space is a quasi-Banach space; it is sufficient to establish that has a convergent subsequence.
For every sequence, is bounded in . Given , there exists such that
that is
Let us fix . Using Lemma 5, we may conclude that
and this implies that
which proves that is bounded in Finally, for a constant , we define
Since sets are closed and bounded in , thus, a strictly increasing sequence and a sequence exist by using the Cantor diagonal argument technique for every , that is
Let , and we prove
We denote by . Fixing , we demonstrate that
for sufficiently large . There exists from such that
Subsequently, we can split the modular as follows:
Next, by the triangle inequality for , Lemma 5, the Fatou lemma for the counting measure, and Equation (27), we have
Therefore, for each , we have
Additionally, we assert that for any , we also have
And for some specific , and , we arrive at
Since in , for sufficiently big , we have
which proves that
According to Equation (29), for sufficiently large , we have
this indicates that in and it proves that the class of measurable functions is bounded. Then, we prove that conditions are necessary, assuming is totally bounded. Since is evident, we shift our focus to . Let us set and define
where the constant is once more used for the triangle inequality . According to our assumptions, there exists such that
where are an open balls in .
Since , ∃ such that
for . Let , then ∃, such that
If we consider the unit ball property, then it follows that
Finally, the triangle inequality yields
which establishes .
To show that conditions and are valid, let us consider and analyze Lemma 5, then one has
valid for each . As a result, the projective map
is defined as follows:
As is a quasi-Banach space, it is a closed and bounded subset of , which is equivalent to its precompactness. Following that, the continuous image of the projective map is defined as
which is precompact in , and the proof is completed.
The results of Theorem 13 show that if instead of , assumptions imply the closedness and boundedness. As a result, the theorem holds true. □
6. Conclusions and Future Remarks
The structure of function spaces and the behavior of operators are clarified by the equalities and inequalities of the Hilbert space, which are essential for functional analysis. The paper contributes in two important ways. The first is the employment of continuous self-adjoint operators on Hilbert spaces, which are essential when working with complex functions on higher dimensions, to extend equality from the classical sense to the tensorial sense using the tensor approximation. Next, the authors introduce a new type of function space, namely mixed-Orlicz–Zygmund space, which combines the Orlicz–Zygmund space of integrability with the sequence space. To prove its validity, we also examine several of its characteristics, including the completeness, compactness, and separability. We believe that this generalized space plays a very essential role in the progress of function spaces and their linked problems, such as the bounds of operators, the regularity of partial differential equations, embedding, etc.
This study advances mathematical inequality theory by investigating inequalities supporting tensor Hilbert spaces and mixed norm function spaces, which is a rare topic in the literature in terms of produced results. Following these results, we advise readers to attempt to develop the Maclaurin inequality on coordinates using tensor operations and also investigate various other properties of our newly defined mixed spaces, such as checking the boundedness of operators, analysis of differential equations, extending over a complex domain, etc.
Author Contributions
Conceptualization, W.A.; Validation, S.B.; Formal analysis, W.A., M.A. and S.B.; Resources, M.Z.M.; Data curation, S.B.; Writing—original draft, W.A.; Writing—review & editing, M.A. and M.Z.M.; Supervision, M.A.; Funding acquisition, M.Z.M. All authors have read and agreed to the published version of the manuscript.
Funding
APC were supported by the resources of the corresponding author, Mutum Zico Meetei.
Data Availability Statement
Data are contained within the article.
Conflicts of Interest
The authors declare no conflicts of interest.
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