Abstract
The topic of convex and nonconvex mapping has many applications in engineering and applied mathematics. The Aumann and fuzzy Aumann integrals are the most significant interval and fuzzy operators that allow the classical theory of integrals to be generalized. This paper considers the well-known fuzzy Hermite–Hadamard (HH) type and associated inequalities. With the help of fuzzy Aumann integrals and the newly introduced fuzzy number valued up and down convexity (-convexity), we increase this mileage even further. Additionally, with the help of definitions of lower -concave (lower -concave) and upper -convex (concave) fuzzy number valued mappings (s), we have gathered a sizable collection of both well-known and new extraordinary cases that act as applications of the main conclusions. We also offer a few examples of fuzzy number valued -convexity to further demonstrate the validity of the fuzzy inclusion relations presented in this study.
Keywords:
fuzzy number valued mapping; fuzzy Aumann integral; up and down convex fuzzy number valued mapping; Hermite–Hadamard inequality; Hermite–Hadamard–Fejér inequality MSC:
26A33; 26A51; 26D10
1. Introduction
Many fields make use of the convexity of functions such as game theory, variational science, mathematical programming theory, economics, and optimal control theory. Convex analysis, a brand-new mathematics branch, started taking shape in the 1960s. Many writers have employed related concepts of convexity during the past 20 years and generalized other inequalities, including h-convex functions (see References [1,2,3,4,5,6,7,8,9,10]), log convex functions (see References [11,12,13,14,15,16,17,18,19], and coordinated convex functions (see References [20,21]). Convexity is a fundamental term in optimization theory applied in operations research, economics, control theory, decision-making, and management. Several writers have expanded and generalized integral inequalities using various convex functions; see Refs. [22,23]. For more information, see [24,25,26,27,28,29,30,31,32,33] and references therein.
Calculating mistakes in a numerical analysis has always been difficult. The interval analysis has received a lot of attention as a novel method for resolving uncertainty issues because of its capacity to reduce calculation errors and make calculations meaningless. Set-valued analysis, a set-centric approach to mathematics and topology, includes interval analysis. It deals with interval variables rather than point variables, and the computation results are expressed as intervals; therefore, it removes mistakes that lead to incorrect conclusions. Moore [34] first adapted an interval analysis to automatic error analysis to deal with data uncertainty in 1966. The work garnered a lot of attention from academics and led to an improvement in calculation performance. They are helpful in many applications because of their capacity to be expressed as uncertain variables, including computer graphics [35], automatic error analysis [36], decision analysis [37], etc. There are numerous great applications and results for readers interested in interval analysis in other branches of mathematics; see References [38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53].
On the other hand, a generalized convexity mapping has the potential to solve a wide range of issues in both a nonlinear and pure analysis. Recently, well-known inequalities such as Jensen, Simpson, Opial, Ostrowski, Bullen, and the famous Hermite–Hadamard that are extended in the setting of interval-valued functions () have been constructed using a variety of related classes of convexity. Chalco-Cano [54] established interval-based inequalities for the Ostrowski type using a derivative of the Hukuhara type. Opial-type inequalities for s were developed by Costa in [55]. The Minkowski inequalities for s were one of the inequalities suggested by Beckenbach and Roman-Flores [56]. According to the literature assessment, the majority of authors used an inclusion connection, similarly to in 2018, to evaluate inequality. These inequalities were created by Zhao et al. [57] for the harmonic h-convex s and the h-convex s. The authors who came after used both harmonical (, )-convex functions and (, )-convex functions to create these inequalities; for more information, see Refs. [58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75].
Using the radius and interval midpoint, Bhunia and his co-author defined the center-radius order in 2014; see Ref. [76]. The following findings for the cr-h-convex, harmonically cr-h-convex, and cr-h-GL functions were developed in 2022 by Wei Liu and his co-authors; see References [77,78,79,80,81,82,83,84,85,86,87,88]. Our examination of the literature showed that inclusion and fuzzy inclusion relations are the main sources of the majority of these discrepancies. The fundamental benefit of the up and down fuzzy relation for up and down functions is that the inequality term generated by employing these conceptions is more exact, and the argument’s validity can be supported by intriguing examples of illustrated theorems. For further study related to interval-valued functions and fuzzy mappings, see [89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111].
This study provides an introduced class of convexity based on the fuzzy inclusion order and is known as -convex s, and is inspired by Refs. [56,57]. We create new H.H. inequalities with the aid of these innovative ideas, and eventually, the Jensen inequality is developed. The study includes a variety of examples to help bolster the results reached.
2. Preliminaries
We recall a few definitions, which can be found in the literature and that will be relevant in the follow-up.
Let us consider that is the space of all closed and bounded intervals of , and that is given by
If , then is degenerate. In the follow-up, all intervals are considered non-degenerate. If , then is positive. We denote by the set of all positive intervals.
Let and be given by
We consider the Minkowski sum, , product, , and difference, , for , as
Remark 1.
for all is a partial interval inclusion relation. Moreover, coincides with on The relation is of UD order [105].
(i) For given the relation , defined on by
(ii) For given the relation , defined on by if and only if or , is a partial interval order relation. Plus, we have that coincides with on The relation is of the left and right (LR) type [104,105].
Given the intervals their Hausdorff–Pompeiu distance is
We have that is a complete metric space [94,102,103].
Definition 1
([93,94]). A fuzzy subset of is a mapping , a denoted membership mapping of . We adopt the symbol to represent the set of all fuzzy subsets of .
Let us consider . If the following properties hold, then is a fuzzy number:
- (1)
- is normal if there exists and
- (2)
- is upper semi-continuous on if for a there exists and yielding for all with
- (3)
- is a fuzzy convex, meaning that for all and ;
- (4)
- is compactly supported, which means that is compact.
The symbol will be adopted to designate the set of all fuzzy numbers of .
Definition 2.
([93,94]). For , the -level, or -cut, sets of are for all , and .
Proposition 1.
is a LR order relation.
([96]). Let . The relation , defined on by
Proposition 2.
is an UD order relation.
([79]). Let . The relation , defined on by
If and , then, for every ,
result from Equations (4)–(6), respectively.
Theorem 1
is a complete metric space, where stands for the Hausdorff metric on a space of intervals.
([94]). For , the supremum metric
Theorem 2
([94,95]). If is an satisfying , then is Aumann integrable (IA-integrable) over when and only when and are integrable over , meaning
Definition 3
([104]). Let be a . The family of s, for every , is satisfying for every For every the lower and upper mappings of are the endpoint real-valued mappings .
Definition 4
([104]). Let be a . Then, is continuous at if for every is continuous when and only when and are continuous at
Definition 5
where for every Moreover, is -integrable over if
([95]). Let be a . The fuzzy Aumann integral (-integral) of over is
Theorem 3
[96]. Let be a , whose -levels define the family of s satisfying for every and is -integrable over when and only when and are integrable over . Moreover, if is -integrable over , then we have
for every
Breckner discussed the coming emerging idea of interval-valued convexity in [97].
An I∙V∙M is called convex I∙V∙M if
for all , where is the collection of real-valued intervals. If (17) is reversed, then is called concave.
Definition 6
([89]). The is called convex on if
for all where for all If (18) is reversed, then is called concave on . is affine if and only if it is both convex and concave .
Definition 7
for all where for all If (19) is reversed then, is called -concave on . is -affine if and only if it is both -convex and -concave .
([105]). The is called -convex on if
Theorem 4
for all and for all . Then, is -convex on if and only if, for all is a convex mapping and is a concave mapping.
([105]). Let be a , whose -cuts define the family of inteval-valued mappings are given by
Remark 2.
If and , then we obtain the inequality (17).
If and , then we obtain the classical definition of convex mappings.
Now we have obtained some new definitions from the literature which will be helpful to investigate some classical and new results as special cases of main results.
Definition 8.
for all and for all . Then, is lower -convex (concave) on if and only if, for all is a convex (concave) mapping and is an affine mapping.
([79]). Let be a , whose -cuts define the family of s are given by
Definition 9.
for all and for all . Then, is upper -convex (concave) on if and only if, for all is an affine mapping and is a convex (concave) mapping.
([79]). Let be a , whose -cuts define the family of s are given by
Remark 3.
Both concepts “-convex ” and classical “convex , see [41]” behave alike when is lower -convex .
3. Fuzzy Number Hermite–Hadamard Inequalities
In this section, we propose Hermite–Hadamard and Hermite–Hadamard–Fejér inequalities for -convex s, and verify with the help of nontrivial examples.
Theorem 5.
If concave , then (23) is reversed.
Let be a -convex on whose -cuts define the family of s are given by for all and for all . If , then
Proof.
Let be a -convex . Then, by hypothesis, we have
Therefore, for every , we have
Then
It follows that
That is
Thus,
In a similar way as above, we have
Combining (24) and (25), we have
Hence, the required result. □
Remark 4.
The following are some exceptional cases which can be obtained from inequality (23):
If one lays is lower -convex on then one acquires the following coming inequality, see [90]:
If one takes is lower -convex on and , then one achieves the following coming inequality, see [98]:
Let . Then, from Theorem 5, we acquire the following inequality, see [99]:
Let and . Then, from Theorem 5, we achieve the classical Hermite–Hadamard inequality.
Example 1.
Let , and the defined by
Then, for each we have . Since left and right end point mappings and , are convex and concave mappings, respectively, for each , then is -convex . We clearly see that and
Note that
and
Therefore,
Hence,
and Theorem 5 is verified.
Theorem 6.
Let be a -convex on whose -cuts define the family of s are given by for all and for all . If , then
where
and ,
Proof.
Take we have
Therefore, for every , we have
In consequence, we obtain
That is
It follows that
In a similar way as above, we have
Combining (31) and (32), we have
By using Theorem 5, we have
Therefore, for every , we have
that is
hence, the result follows. □
Example 2.
We consider the defined by, as in Example 1, then is -convex and satisfying (10). We have and . We now compute the following
Then we obtain that
Hence, Theorem 6 is verified.
We now obtain some HH-inequalities for the product of -convex s. These inequalities are refinements of some known inequalities, see [57].
Theorem 7.
Let be two -convex s on , whose -cuts are defined by and for all and for all . If , then
where and and
Proof.
Since , then we have
And
From the definition of -convex s, it follows that and , so
Integrating both sides of the above inequality over [0, 1], we get
It follows that,
that is
Thus,
And the theorem has been established. □
Example 3.
Let and the s defined by
Then, for each we have and Since left and right end point mappings and , are convex and concave mappings, respectively, and and are convex and concave mappings, respectively, for each , then and both are -convex s. We clearly see that and
Note that
Therefore, we have
It follows that
and Theorem 7 has been demonstrated.
Theorem 8.
Let be two -convex s, whose -cuts define the family of s are given by and for all and for all , respectively. If , then
where and and
Proof.
By hypothesis, for each we have
Taking integration over we have
that is
Hence, the required result. □
Example 4.
We consider the s . Then, for each we have and as in Example 3, then and both are -convex mappings. We have and , , then
Therefore, we have
It follows that
and Theorem 8 has been demonstrated.
We now give HH-Fejér inequalities for -convex s. Firstly, we obtain the second HH-Fejér inequality for -convex .
Theorem 9.
Let be a -convex with , whose -cuts define the family of s are given by for all and for all . If and symmetric with respect to then
Proof.
Let be a -convex . Then, for each we have
And
After adding (36) and (37), and integrating over we get
Since is symmetric, then
Since
Then from (38), we have
that is
hence
Next, we construct first HH-Fejér inequality for -convex , which generalizes first HH-Fejér inequalities for classical convex mapping. □
Theorem 10.
Let be a -convex with , whose -cuts define the family of s are given by for all and for all . If and symmetric with respect to and , then
Proof.
Since is a -convex, then for we have
Since , then by multiplying (41) by and integrating it with respect to over we obtain
Since
Then from (43), we have
from which, we have
that is
This completes the proof. □
Remark 5.
From Theorem 9 and Theorem 10, we clearly see that:
If , then we acquire the inequality (23).
If is lower -convex on then we acquire the following coming inequality, see [90]:
If is lower -convex on with , then from (35) and (40) we acquire the following coming inequality, see [99]:
If is lower -convex on with , then from (35) and (40) we acquire the following coming inequality, see [99]:
Let . Then from (35) and (40), we acquire the following inequality, see [56]:
Let and . Then, from(35) and (40), we obtain the following classical Fejér inequality:
Example 5.
We consider the defined by
Then, for each we have . Since end point mappings and are convex and concave mappings, respectively, for each , then is -convex . If
then , for all .
Sinceand . Now
we compute the following:
And
From (49) and (50), we have
Hence, Theorem 9 is verified.
For Theorem 10, we have
From (51) and (52), we have
Hence, Theorem 10 has been verified.
4. Conclusions
This paper provides the introduced class -convex concept for s. The H.H. and Jensen-type inequalities were developed utilizing this idea and a fuzzy-inclusion relation. This study expands on several recent findings made by Zhao et al. [56,57] and the writers who came after them, Refs. [61,62]. Furthermore, some nontrivial cases are provided to verify our primary conclusions’ accuracy. In the future, it will be fascinating to look into how analogous inequalities are established for other convexity types and by employing various integral operators. Our study of interval integral operator-type integral inequalities will broaden their practical applications because integral operators are widely used in engineering technology, such as various forms of mathematical modeling, and because different integral operators are suitable for different forms of practical problems. Convex optimization theory may take a new turn as a result of this idea. Other researchers working on a range of scientific subjects may probably find the idea useful.
Author Contributions
Conceptualization, M.B.K.; methodology, M.B.K.; validation, A.M.A.; formal analysis, H.A.O. and A.M.A.; investigation, M.B.K.; resources, H.A.O. and A.M.A.; data curation, M.G.V.; writing—original draft preparation, M.B.K.; writing—review and editing, M.B.K. and A.M.A.; visualization, M.G.V.; supervision, M.B.K. and L.A.; project administration, M.B.K. and L.A.; funding acquisition, A.M.A. and M.G.V. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Acknowledgments
The authors would like to thank the Rector, COMSATS University Islamabad, Islamabad, Pakistan. The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: 22UQU4330052DSR11. This research was also supported by Office of Research Management, Universiti Malaysia Terengganu, Malaysia.
Conflicts of Interest
The authors declare no conflict of interest.
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