Abstract
The relationship between convexity and symmetry is widely recognized. In fuzzy theory, both concepts exhibit similar behavior. It is crucial to remember that real and interval-valued mappings are special instances of fuzzy-number-valued mappings (), as fuzzy theory relies on the unit interval, which is crucial to resolving problems with interval analysis and fuzzy number theory. In this paper, a new harmonic convexities class of fuzzy numbers has been introduced via up and down relation. We show several Hermite–Hadamard () and Fejér-type inequalities by the implementation of fuzzy Aumann integrals using the newly defined class of convexities. Some nontrivial examples are also presented to validate the main outcomes.
Keywords:
fuzzy up and down harmonically ℏ-convexity; numerical analysis; fuzzy Aumann integral inequalities MSC:
26A33; 26A51; 26D10
1. Introduction
The theory of convexity is an active, compelling, and important topic of research that has significantly influenced a variety of different areas of study, including mathematical analysis, economics, optimization issues, finance, control theories, and game theory. Researchers have developed unifying numerical structures through the theory of convexity that may be utilized to solve a variety of issues in both pure and applied mathematics. In the past few decades, there have been significant advancements in convexity, like generalizations and expansions. The demand for fractional operators in several branches of mathematics has increased as a result of the study of fractional analysis. Numerous scholars have worked to create modified versions of inequalities by developing fractional operators using the non-singular special mappings as their kernel in order to satisfy this condition. One method for enhancing the fractional and Aumann integral inequalities for various convexities and pre-invexities, which have important applications in the field of analysis, is the employment of generalized fractional operators.
The generalization of classical calculus, known as fractional and interval Aumann calculus, is crucial to the study of pure, practical, and computational mathematics. Numerous fields, including biology, control operator theory, physics and computer structure optimizations, have greatly benefited from research in the field of fractional analysis [1,2,3,4,5]. The majority of scientists have spent the last few decades attempting to create generalized versions of well-known inequalities and discussing a vast array of applications in the areas of analysis and discrete optimization. Numerous authors have put a lot of effort into [4,5,6,7,8,9,10] and studied the extensions and refinements of various branches of mathematics. By utilizing their kernel in multi-dimension mappings, fractional operators have the potential to construct advanced inequalities analysis, opening up new avenues for research into how inequalities behave in various disciplines of mathematics. Fuzzy set theory, which deals with issues involving unclear, hazy, and imprecise information and decision making for either individual or group collaboration, has numerous important applications. This project expanded on Moore Ramon’s interval analysis concept from 1966 [11,12,13,14]. Due to its review of decision making, this topic is appealing to many scientists. The study of interval analysis has gained a lot of attention from professionals in recent years due to how useful it has been for global optimization and constraint solution algorithms for decision-makers. It has reduced the errors and increased the accuracy while also delivering useful and reliable findings. In order to achieve the intended outcomes, numerous scholars began their research on inequalities as a result of this motivation. The interval-valued mapping () was first introduced by Zhao et al. [15] and Khan et al. [16,17,18,19,20,21,22]. By using several integral operators, many mathematicians established a close correlation between inequalities and s [23,24,25,26,27]. Fuzzy differential equations and fuzzy interval analysis have numerous applications that deal with various computer or mathematical modules [28,29,30,31,32,33]. Numerous inequalities, including the Hermite–Hadamard inequality, the Jensen inequality, trapezoid-type inequalities, the Mercer inequality, and the Schur inequality, have been presented in various research articles [34,35,36] with the aid of a fuzzy number-valued mapping. Qiang et al. [37], and Iscan et al. [38,39] presented the basic version of and Fejér-type inequalities via harmonic convexity. Similarly, Ion [40,41,42] developed a new version of -type inequalities for quasi convex mappings. After that, Noor et al. [43,44,45] generalized the ideas of harmonic convexity in terms of harmonic h-convexity. On the other hand, Moore [46] briefly discussed the basic concepts of interval theory, which play an important role in overcoming the uncertainty in computer programming. Stefanini and Bede [47], and Khan et al. [48,49,50,51,52] provided the definition of differentiability for interval-valued mappings. Diamond and Kloeden [53] and Goetschel and Voxman [54] proposed the basic properties of fuzzy numbers and defined the metric space over fuzzy number space. In one step forward, Kaleva [55,56,57,58] acquired the integrability concepts where the integrable mappings are . Costa and Román-Flores [59,60,61] provided the main directions in the field of integral inequalities via introducing different versions of relations. Therefore, some new modifications of Jensen’s inequalities are acquired. After that, Zhang et al. [62] provided a new version of the relation that is known as the up and down relation ( relation), and with the support of this relation, reported new refinements of Jensen’s inequalities and removed the mistakes of classical inequalities. Then, Khan et al. [63] obtained the and Fejér-type inequalities for Riemann integrals and fractional integrals via relation. To study more basic concepts related to fuzzy theory, see [64,65,66].
Inspiration
Convexity and generalized convexity are crucial ideas in fuzzy optimization because they characterize the optimality condition of convexity and produce fuzzy variational inequalities. Numerous mathematical issues relating to minimization theory and interval analysis were solved using powerful approaches developed from variational inequality and fuzzy set theory. is another name for fuzzy mapping. Numerous fractional integrals have in both the lower and upper cases. The effective application of these forms of fractional integrals allows for the verification of the behavior of well-known inequalities. These studies show the significance of this method for converting real integral inequalities into fuzzy integral inequalities, both theoretically and practically. This study discusses the new class of harmonic convexity as well as types of inequalities for via the relation. Moreover, the validity of the main outcomes has been discussed with the support of nontrivial examples.
2. Preliminary Concepts
In this section, we will review the basic terms and concepts that help to make sense of the concepts underlying our new discoveries.
Let be the space of all the closed and bounded intervals of and let be defined by
It is called a positive interval if . The definition of , which represents the set of all the positive intervals, is
Let and be defined by
Then, the Minkowski “difference” , “addition” and “product” for are defined by
Remark 1.
(i) For the given the relation defined on by if and only if for all the is a partial interval inclusion relation. The relation is coincident to on It can easily be seen that “” looks like “up and down” on the real line so we call the “up and down” (or “” order, in short) [62]. For the Hausdorff–Pompeiu distance between intervals and is defined by
It is a familiar fact that is a complete metric space [46,53,54].
We will merely go over some fundamental concepts concerning fuzzy sets and fuzzy numbers because we will be using the conventional definitions of a fuzzy set and a fuzzy number. Please note that we refer to and as the set of all the fuzzy subsets and the fuzzy numbers of , respectively.
Definition 1.
([53]). Given , the level sets or cut sets are given by for all and by . These sets are known as -level sets or -cut sets of
Proposition 1.
([59]). Let . Then, relation is given on by when and only when , for every which are left- and right-order relations.
Proposition 2.
([63]). Let . Then, relation is given on by when and only when for every which is the -order relation on
Remember the approaching notions, which are offered in the literature. If and , then, for every the arithmetic operations addition “”, multiplication “”, and scalar multiplication “” are defined by
Equation (8) through (10) have immediate consequences for these outcomes.
Theorem 1.
([53,55]). If is an interval-valued mapping () satisfying , then is an Aumann integrable (AI integrable) over when and only when and both are integrable over such that
The following conclusions can be drawn from the literature [54,59,62]:
Definition 2.
([59]). A fuzzy-interval-valued map is called . For each its are classified according to their -levels that are given by for all Here, for each the end-point real mappings are called the lower and upper mappings of .
Definition 3.
Let be an . Then, the fuzzy integral of over denoted by , is given level-wise by
for all where denotes the collection of Riemannian integrable mappings of . The is -integrable over if Note that, if are Lebesgue-integrable, then is fuzzy Aumann-integrable mapping over , see [37].
Theorem 2.
([26]). Let be an , its are classified according to their -levels that are given by for all and for all Then, is -integrable over if and only if and are both -integrable over . Moreover, if is -integrable over then
for all For all denotes the collection of all the -integrable over .
Definition 4.
([38]). A set is said to be a harmonically () convex set, if, for all , we have
Definition 5.
([38]). The relation is called an -convex mapping on if
for all where for all If expression (14) is inverted, then is called an -concave on , such that
Definition 6.
([43]). The positive real-valued mapping is called an --convex mapping on if
for all where for all and such that . If expression (16) is inverted, then is called an --concave mapping on , such that
The set of all the --convex (--concave) mappings is denoted by
Definition 7.
([29]). The is called an -convex on if
for all where for all and , such that . If expression (18) is inverted, then is called an -concave on . The set of all the -convex (-concave) is denoted by
Definition 8.
([20]). The is called a -harmonically () convex on if
for all where , for all If expression (19) is inverted, then is called a -concave on .
Definition 9.
The is called a -convex on if
for all where , for all and such that . If expression (20) is inverted, then is called a -concave on . The set of all the -convex ( -concave) is denoted by
Theorem 3.
Let be an -convex set, and let be a , its are classified according to their -levels that are given by
for all , . Then, if and only if, for all , and
Proof.
The proof is similar to the proof of Theorem 2.12 (see [16]). □
Example 1.
We consider the defined by,
Then, for each we have . We can easily see that , , with , for each . Hence, .
Remark 2.
If , then Definition 9 cuts down Definition 8.
- If with then the cuts down classical -convex mapping (see [43]).
- If with and with , then from , one can obtain the -convex mapping (see [43]).
- If with and , then the cuts down mappings (see [38]).
- If with and , then the --convex cuts down mappings (see [39]).
3. Main Outcomes
The construction of generalized Aumann fuzzy integral inequalities for the is utilized to produce our key results, which are covered in this section.
Theorem 4.
Let , its are classified according to their -levels that are given by for all , . If and , so that
If , then
Proof.
Let . In this case, we could write a hypothesis
Therefore, for each , we have
Then
It follows that
that is
Thus,
Similar to what was stated above, we have
Combining (24) and (25), we have
This completes the proof. □
Example 2.
We consider for , and the as in Example 1. Then, for each we have , which is a . Since, . We now compute the following:
for all This means
Similar to what was stated above, we have
for all such that
From which, we have
that is
Hence,
Remark 3.
If , where , the result for the is then obtained by reducing Theorem 4 (see [22]):
If , the result for the is then obtained by reducing Theorem 4 (see [20]):
If , the result for the is then obtained by reducing Theorem 4 (see [22]):
If with , the result for the is then obtained by reducing Theorem 4 (see [43]):
If with and , the result for the is then obtained by reducing Theorem 4 (see [43]):
If with and , the result for the is then obtained by reducing Theorem 4 (see [38]):
If with and , the result for the is then obtained by reducing Theorem 4 (see [43]):
Theorem 5.
Let with , its are classified according to their -levels that are given by for all , . If , so that
where
and ,
If , Inequality (26) is then turned around.
Proof.
Take so that
Therefore, for every , yields
In consequence, we obtain
That is
It follows that
Similar to what was stated above, we have
Combining (27) and (28), we can write
Therefore, for every , by using Theorem 4, we have
that is
The proof is completed. □
In the upcoming results, we will discuss the Pachpatte-type inequalities with the help of a product of two -concave .
Theorem 6.
Let and , whose -levels are defined by and for all , , respectively. If , then
where , and
Proof.
Since and are and -convex s, for each we then have
and
From the definition of the -convexity of the , it follows that and , so that
The outcome of integrating the given inequality over is
It follows that
that is
Thus,
The proof is completed. □
Theorem 7.
Let , , whose -levels are defined by and for all , , respectively. If and , so that
where and
Proof.
Theoretically, we have a value for each ,
Integrating over then gives
that is
the desired result follows. □
Fuzzy Fejér inequality
Theorems 8 and 9 are generalized by some of the findings in what follows. Start with the second fuzzy Fejér inequality.
Theorem 8.
Let , where its s are classified according to their -levels that are given by for all , . If and then
If , Inequality (32) is then turned around such that
Proof.
Let be an -convex . Then, for each we have
Similar to what was stated above, we have
By combining (34) and (35), then integrating them over , we arrive at
Since is symmetric, then
Therefore, the results
From (36) and (37), we have
that is
and hence
The proof is completed. □
Theorem 9.
(First fuzzy Fejér inequality) Let , where its s are classified according to their -levels that are given by for all , . If and so that
If , Inequality (38) is then turned around such that
Proof.
Since is a , then for we have
By multiplying (40) by and integrating it by over we yield
Therefore, the results
From (41) and (42), the results
from which, we have
that is
This completes the proof. □
Remark 4.
Let Then, one can achieve the correct from of Inequality (22).
- If , then with Theorems 8 and 9, we obtain the results for the (see [20]).
- If with and then we achieve the first and second classical H⋅H Fejér inequality for the classical mapping.
4. Conclusions
Several mathematical inequalities are based on a mapping’s convexity. It should be highlighted that in order to achieve answers that are pertinent to novel convexity-related situations, a general understanding of convex mapping is necessary. One of these overviews is the idea of the , which is best explained by comparing the differences between the left and middle terms and the right and middle terms of the famous Fejér inequality, one of the many inequalities connected to convex mappings. Additionally, for , we created several new analogues of Hermite–Hadamard–Fejér-type inequalities. This publication is anticipated to inspire additional study in this area.
Author Contributions
Conceptualization, A.A.; validation, S.A.; formal analysis, S.A.; investigation, A.A. and M.V.C.; resources, A.A. and M.V.C.; writing—original draft, A.A. and M.V.C.; writing—review and editing, A.A. and S.A.; visualization, M.V.C. and A.A.; supervision, M.V.C. and A.A.; project administration, M.V.C. and A.A. All authors have read and agreed to the published version of the manuscript.
Funding
The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through a project number (TU-DSPP-2024-87).
Data Availability Statement
There is no data availability statement to be declared.
Conflicts of Interest
The authors have no conflicts of interest.
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