Abstract
Integral inequalities with generalized convexity play a vital role in both theoretical and applied mathematics. The theory of integral inequalities is one of the branches of mathematics that is now developing at the quickest rate due to its wide range of applications. We define a new Hermite–Hadamard inequality for the novel class of coordinated ƛ-pre-invex fuzzy number-valued mappings (C-ƛ-pre-invex F N V Ms) and examine the idea of C-ƛ-pre-invex F N V Ms in this paper. Furthermore, using C-ƛ-pre-invex F N V Ms, we construct several new integral inequalities for fuzzy double Riemann integrals. Several well-known results, as well as recently discovered results, are included in these findings as special circumstances. We think that the findings in this work are new and will help to stimulate more research in this area in the future. Additionally, unique choices lead to new outcomes.
Keywords:
fuzzy mappings over a rectangle plane; ƛ-pre-invexity; Riemann–Liouville fractional integral operator; inequalities over coordinates MSC:
26A33; 26A51; 26D10
1. Introduction
Convexity is a key component of mathematical sciences and is useful in many domains, including optimization theory, economics, engineering, variational inequalities, management science, and Riemannian manifolds. Complex issues are simplified by convex sets and functions, which allow for effective computing solutions. Convex analysis-derived notions continue to be useful in a wide range of scientific and engineering fields. A strong mathematical idea known as convexity can be utilized to provide a theoretical framework for the development of efficient algorithms in a range of fields, as well as to simplify challenging mathematical issues. Convexity-derived integral inequalities provide a comprehensive understanding of the behavior of complex systems. Mathematical rigor is provided by these inequalities. They are essential tools for engineers and physicists because of their capacity to simulate, understand, and predict a broad variety of natural processes. The identification of new applications and connections made possible by this field of study will probably contribute to our comprehension of the physical universe. In conclusion, Jensen’s research and subsequent developments in convex analysis have made it clearer how useful convex functions are for comprehending optimization issues. This provides helpful methods and theoretical foundations for figuring out the optimal solutions in a range of scenarios. Mathematical convexity continues to be an important area of study and application in many different fields.
Generalized convexity ideas are used in approximation theory and probability distributions to approximate non-convex functions with convex functions. This approximation is useful for a wide range of computational and numerical techniques. To summarize, the mathematical framework for establishing and analyzing integral inequalities is shared by the closely related fields of study of generalized convexity and integral inequalities. The applications of these concepts in many domains, including physics, functional analysis, and optimization, highlight the importance of understanding the relationship between generalized convexity and integral inequality in theoretical and practical contexts. There are many different kinds of inequality in the literature. Hermite–Hadamard inequality, sometimes known as double inequality, is the most important component in optimization issues. In this context, we take into consideration the well-known inequality for convex functions due to Hadamard and Hermite separately; refer to Ref. [1].
Theorem 1.
Let be a convex mapping, where is a set of real number. Then, the following outcome holds:
One can check the concavity of the mappings by replacing the symbol “ with in (1).
The continuous investigation into improved versions of the double inequality demonstrates the mathematical significance of this discovery and its broad applicability in other fields; refer to Refs. [2,3]. Invex functions are now significant extensions of convex functions in mathematical optimization and related fields. The first introduction of invex functions, which generalized classical convex mappings, was made by the authors in [4], who also went over some of their intriguing characteristics. A modification and generalization of conventional convex mappings, the modified forms of invex sets and pre-invex functions, were introduced by Ben and Mond together in [5]. One characteristic that sets this type of invexity apart is that the differentiable pre-invex mappings are invex, but not the other way around. For further research related to pre-invexity and its applications, see [6,7,8] and the references therein.
With interval-valued analysis, one may successfully handle errors and uncertainties in a variety of computational tasks. This method is especially helpful for applications that demand precise forecasts and dependable outcomes, since it represents numerical values as intervals, ensuring that results are based on uncertainties in input data. It provides a practical and cautious method of computing by portraying numerical values as intervals. Interval analysis has several applications in various domains, including mathematics, computer science, engineering, and natural science, thanks to Moore’s [9] contributions (see [10]). Mathematicians are inspired to extend integral inequalities to interval-valued mappings due to exact results in a range of areas. In the beginning, writers in [11] linked Jensen-type and Hadamard-type results in the setup of set-valued functions using h-convex mappings. Afzal et al. [12] created three well-known inequalities that provide insight into the characteristics and behavior of stochastic processes inside a probability space by fusing the ideas of set-valued analysis and h-convex mappings. The concept of pre-invex functions was utilized by the authors in [13] to produce double inequality for set-valued mappings. The concept of pre-invex functions on coordinates is used by the authors in [14] to produce a number of double inequality conclusions on the rectangular plane. Sharma et al. [15] used fractional integral operators to build various novel product forms of the double inequality by utilizing the concept of (h1, h2)-pre-invex functions in the creation of set-valued mappings. In the setting of set-valued mappings, Zhou et al. [16] created an improved version of the double inequalities by utilizing pre-invex exponential type functions via fractional integrals. Up-down pre-invex mappings in a fuzzy setup were employed by Khan et al. [17] to obtain Fejér- and Hermite-type results. Noor et al. [18] produced several Hermite–Hadamard-type theorems linked to special functions using power mean integral inequalities, utilizing the idea of (h1, h2)-pre-invex mappings. Sun et al. [19] developed a number of novel double inequalities for h-pre-invex functions with applications by utilizing local fractional integrals. Using the idea of (s,m,φ)-type functions, the writers of [20] created a number of innovative forms of double inequalities for generalized pre-invex mappings that had some intriguing characteristics. Based on Godunova–Levin pre-invex mappings, Ali et al. [21] created several novel variations of Hermite-Hadamard-type findings using partial-order relations. Various novel Hermite–Hadamard- and Fejér-type findings were developed by Tariq et al. [22] by merging fractional operators and generalized pre-invex mappings. Sitho et al. [23] proved mid-point and trapezoidal inequality for differentiable pre-invex functions using the concept of quantum integrals. Applications of Trapezium-type inequalities for h-pre-invex functions to specific means were studied by Latif et al. [24]. The use by Delavar et al [25] of fractional integrals resulted in new limits for the mid-point type and Hermite–Hadamard’s trapezoid inequality. Further details on these findings and some other fascinating recent advances can be found in the references [26,27,28,29,30,31,32].
Additionally, we describe various applications for random variables within the context of error limits that also generalize other conclusions. This is the first time in the literature that we have established error bounds for quadrature-type formulas using this class of generalized fuzzy convexity. Since we cannot compare two intervals, the majority of the research is based on fuzzy partial-order or pseudo-order relationships, which show serious problems in several of the inequality results. The benefit of this order relationship is that intervals can be easily compared. More significantly, though, the interval difference’s endpoints are substantially closer together, allowing for a more accurate outcome. In 2023, different authors used distinct classes of convexities to derive different solutions utilizing Bhunias–Samanata-order relation; see [33,34,35].
Since the fuzzy set notion was introduced [36], fuzzy set theory has grown to be an effective tool for processing ambiguous or subjective data in mathematical models and for modeling uncertainty. The primary research areas have focused on a variety of applications in pressing issues, such as population dynamics [37,38], medicine [39,40,41], renewable and sustainable energy [42,43,44,45,46], engineering issues [47], and image processing [48,49]. In the same vein, fuzzy mathematical analysis is a crucial subject from both a theoretical and practical application standpoint [50,51]. For further study related to basic operations, Aumann’s integrations, and fractional integrals, where integrable mappings comprise interval-valued and fuzzy-number-valued mappings, see [52,53,54,55,56,57,58] and the references therein. Moreover, Khan et al. [59,60,61,62], and Matloka [63] presented the new versions of integral inequalities for fuzzy-number-valued mappings, real-valued mappings, and interval-value mappings with the help of coordinated convexity. Recently, Khan et al. [64] gave the fractional versions of inequalities via coordinated convexity and non-convexity, where integrable functions are fuzzy-number-valued mappings. For more information, see [65] and the reference therein.
This work is innovative and important because it uses various choices of bifunction “” to introduce a more generalized class of pre-invex functions called coordinated -pre-invex ···s, which unify diverse previously reported findings. A more generalized version of inequality is deduced with this class, since pre-invexity offers more good features than conventional convex maps, and convexity and pre-invexity are two independent concepts.
We are defining a new class of pre-invexity for the first time thanks to the literature on developed inequalities, and, in particular, these articles [13,14,63]. Using these ideas, we are developing a number of novel variants of the well-known double- and trapezoid-type inequalities and their relationship to Fejér ’s work. This essay is organized as follows: building on the preparatory work in Section 2, In Section 3, we introduce a new class of pre-invexity and discuss some of its fascinating aspects. The primary findings of this work are reported in Section 3, where we share modified Hermite–Hadamard–Fejér-type results, as well as establishing various versions of well-known double-type inequalities. Section 4 concludes with a review of some closing observations and recommendations for further research.
2. Preliminaries
In this section, we go over a few recent definitions and findings that might bolster the study’s main conclusions. Moreover, several concepts are utilized in articles without being clarified; refer to Refs. [50,52,53,54,55,56,57,58]. In the following results, to avoid confusion, we will not include the symbols , , , , or before the integral sign. Moreover, the notions , , and and will be used for a set of fuzzy number, set of positive fuzzy number, set of intervals, and collection of positive intervals, respectively.
Definition 1
([52,54]). Given , the level sets or cut sets are given by
and by
These sets are known as -level sets or -cut sets of .
Proposition 1
([40]). Let . Then, relation is given on by when and only when , for every which are left- and right-order relations.
Proposition 2
([37]). 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 given in the literature. If and , then, for every the arithmetic operation addition , multiplication , and scaler multiplication are defined by
The equations numbered from (4) to (6) directly influence these results.
Theorem 2
([55]). Let be an ···, its ··s are classified according to their -levels are given by and Then, is -integrable over if and only if and are both -integrable over . Moreover, if is -integrable over then
For all denotes the collection of all -integrable ···s over .
Here, we will give some definitions of Aumann’s fractional integrals over a rectangle plane.
Definition 2
([57]). Let be ·· and . Subsequently, the interval integrals of using the Riemann–Liouville approach are delineated as follows:
where and is the gamma function.
In a recent study by Allahviranloo et al. [58], they proposed a fuzzy adaptation of fractional integral formulations, which can be expressed as follows:
Definition 3.
Let and be the collection of all Lebesgue measurable ···s on . Then, the fuzzy left and right Riemann–Liouville fractional integral of with order are defined by
and
respectively, where is the Euler gamma function.
The fuzzy fractional -based integrals, both left and right, in the Riemann–Liouville framework, which rely on endpoint functions, can be characterized as follows:
where
and
The fuzzy Riemann–Liouville fractional integral on the right side, symbolized as , can likewise be formulated by incorporating both left and right endpoint functions.
Theorem 3
([59]). Let and
be a pre-invex
··· on whose
-cuts set up the sequence of ··s
are given by for all and for all . If
, then
Interval and fuzzy integrals in the style of Aumann are described as follows for -·· and -··· .
Theorem 4
([61]). Let be an ··· on coordinates, whose -cuts set up the sequence of ·· are given by for all and for all Then, is fuzzy double-integrable (-integrable) over if and only if and both are -integrable over Moreover, if is -integrable over then
for all
The family of all -integrable functions of ···s over coordinates and -integrable functions over coordinates are denoted by and for all
The primary specification for the fuzzy Riemann–Liouville fractional integral concerning the coordinates of the function is presented as follows.
Definition 4
([60]). Let and . The double fuzzy interval Riemann–Liouville-type integrals of order are defined by
Presented below is the recently formulated notion of --pre-invexity across fuzzy number space within the codomain through the fuzzy relation denoted by the following.
Definition 5.
The ··· is referred to as --pre-invex ··· on an invex set if
for all and where If inequality (22) is reversed, then is referred to as coordinate -pre-concave ··· on .
Lemma 1.
Let be a -··· on . Then, is --pre-invex ··· on if and only if there exist two --pre-invex ···s , and , .
Theorem 5.
Let be an ··· on . Subsequently, derived from ɤ-levels, we acquire the set of ·· , which are expressed as
for all and for all . Then, is --pre-invex ··· on if and only if, for all and both are --pre-invex.
Proof.
Assume that for each and are --pre-invex on . Then, from Equation (22), for all and , we have
and
Then, by Equations (4), (6), and (23), we obtain
That is,
hence, is --pre-invex ··· on .
Conversely, let be --pre-invex ··· on Then, for all and we have
Therefore, again from Equation (23), for each , we have
Again, using Equations (4) and (6), we obtain
for all and Then, by the --pre-invexity of , we have for all and the case that
and
for each Hence, the result follows. □
Example 1.
We consider the ··· defined by
Then, for each we have . Since endpoint functions are --pre-invex functions for each , is a --pre-invex ···.
Upon examination of Lemma 1 and Example 1, it becomes apparent that every -pre-invex ··· satisfies --pre-invex ··· conditions, utilizing and . However, the reverse assertion does not hold.
Remark 1.
When setting with , is deemed a classical --pre-invex function if it satisfies the specified inequality outlined here:
We define , along with with , leading to the classification of as a classical -pre-invex function, provided that satisfies the following inequality:
By setting and with , and is an affine function and is a pre-concave function. If the stated inequality is true here, see [62]:
Definition 6.
Let be an ··· on , given by
for all and for all . If and are --pre-invex (pre-concave) and affine functions on , for all respectively, then is a -left--pre-invex (pre-concave) ·· on .
Definition 7.
Let be an ··· on , defined by
for all and for all . If and are --affine and -pre-invex (pre-concave) functions on , respectively, then is a -right--pre-invex (pre-concave) ··· on .
Theorem 6.
Let be a coordinated invex set, and let be an ···. Subsequently, derived from ɤ-levels, we acquire the set of ·· , which are expressed as
for all and for all . Then, is --pre-concave ··· on if and only if, for all and are --pre-concave and -pre-invex functions, respectively.
Proof.
The proof for Theorem 6 follows a methodology akin to that of Theorem 5. □
Example 2.
We consider the ···s defined by
Then, for each we have . Since endpoint functions are coordinate -pre-concave and -affine functions with and by means of and , for each , is --pre-concave ···.
3. Main Results
Below, we present the primary findings for -integral inequalities applicable to the Hermite–Hadamard type, employing fuzzy fractional operators within --pre-invex ···s.
Theorem 7.
Let be a coordinate -pre-invex ··· on , where . Subsequently, derived fromɤ-levels, we acquire the set of ·· , which are expressed as for all and for all . If , then the following inequalities hold:
If --pre-concave ···, then,
Proof.
Let be a --pre-invex ···. Then, by hypothesis, we have
By using Theorem 5, for every , we have
By using Lemma 1, we have
and
From (32) and (33), we have
and
It follows that
and
Since and , are both --pre-invex-··s, from inequality (16), for every , for inequalities (34) and (35), we have
and
Since , (36) can be written as
That is,
Multiplying double inequality (38) by and integrating with respect to over we have
Again, multiplying double inequality (38) by and integrating with respect to over we have
From (39), we have
From (40), we have
This is because, derived from ɤ-levels, we acquire the set of ·· , which are expressed as
and
Similarly, since then, from (37), (43) and (44), we have
and
The second, third, and fourth inequalities of (30) will be the consequence of adding the inequalities (43)–(46).
The second, third, and fourth inequalities within Equation (30) arise from the incorporation of the inequalities (43)–(46).
Now, considering any , we encounter the left segment of inequality (16).
and
The subsequent inequality emerges from the combination of the two inequalities (47) and (48):
Likewise, as we acquire the collection of ··s for , the inequality can be articulated in the following manner:
This is the primary inequality in Equation (30).
Now, considering any , we observe the right section of inequality (16):
Summing inequalities (50)–(53), and then multiplying the resultant with , we have
This is because, derived from ɤ-levels, we acquire the set of ·· , which is expressed as
Here lies the ultimate inequality within (30), marking the establishment of the conclusion. □
Example 3.
We consider the ···s , characterized by
then, for each we have . Consequently, the endpoint functions are --pre-invex functions for each with respect to and . Hence, is a --pre-invex ··· with respect to and .
That is,
Hence, Theorem 7 has been verified.
Remark 2.
Setting and , and , from (30), as a result, there will be inequity; see [62]:
If we set and , and is -left--pre-invex, then from (30), as a result, there will be inequity:
By setting and with and being -left--pre-invex, following Equation (30), we manage to introduce the forthcoming inequality, as illustrated in [14]:
If we set and with and is -left--pre-invex, following Equation (30), we manage to introduce the forthcoming inequality, as illustrated in [14]:
By setting and with , following Equation (30), we manage to introduce the forthcoming inequality, as illustrated in [63]:
In the subsequent results, we anticipate discovering intriguing findings derived from the product of two --pre-invex ···s. These inequalities are recognized as Pachpatte inequalities.
Theorem 8.
Let be two --pre-invex ···s on and let Subsequently, derived from ɤ-levels, we acquire the set of ·· , which are expressed as and for all and for all . If , then the following inequalities hold:
If and are both --pre-concave ···s on , then the aforementioned inequality can be formulated as follows:
where
and for each , , and are defined as follows:
Proof.
Let and be two - and -pre-invex ···s on , respectively. Then,
and
Since and are both - and -pre-invex ···s on , respectively, for any , we have
Taking the multiplication of the above fuzzy inclusion with and then taking the double integration of the resultant over with respect to (), it is the case that
From the expression on the right side of (63), we obtain
Combining (63) and (64), for each , we have
Moreover, we have
Hence, the required result. □
Remark 3.
Suppose we assume , along with and . Consequently, based on (61), an inequality emerges, as described in [62]:
If is -left--pre-invex with and one assumes that and , then from (61), as a result, there will be inequity:
If is -left--pre-invex with with and then, following Equation (61), we manage to introduce the forthcoming inequality, as illustrated in [14]:
If and with , then following Equation (61), we manage to introduce the forthcoming inequality, as illustrated in [63]:
If and with and , then following Equation (61), we manage to introduce the forthcoming inequality, as illustrated in [63]:
Theorem 9.
Let be a --pre-invex ··· on and let . Subsequently, derived from ɤ-levels, we acquire the set of ·· , which are expressed as and for all and for all . If , then the following inequalities hold:
If and are both coordinate -pre-concave ···s on , then the inequality above can be expressed as follows:
where , , , and are given in Theorem 8.
Proof.
Since are two -pre-invex ···s, from inequality (18) and for each we have
Multiplying the above fuzzy inclusion with and then taking the double integration of the resultant over with respect to (), we have
which implies that
as , following simplification, we arrive at the desired conclusion. □
Remark 4.
If one assumes that and and , then from (70), as a result, there will be inequity; see [62]:
If is -left--pre-invex with and one assumes that and , then from (70), as a result, there will be inequity:
If with and , then following Equation (70), we manage to introduce the forthcoming inequality, as illustrated in [14]:
If with and , then following Equation (70), we manage to introduce the forthcoming inequality, as illustrated in [14]:
If and with and , then following Equation (70), we manage to introduce the forthcoming inequality, as illustrated in [63]:
4. Conclusions
This work studies various inequalities related to a novel class of pre-invexity via --pre-invex type ···s via fuzzy-set-valued functions. First, under the full-order relation, we define the --pre-invex fuzzy mappings and investigate some of their induced features. Through the use of arbitrary non-negative functions and related bifunctions of Hermite, Hadamard, and Fejér-type inequalities, we construct unique forms and expand greatly on previously published results. Several unique instances of these inequities are also covered. Several numerical examples are provided to further show the correctness of the results. The notions and concepts presented in this paper can be used to investigate additional types of convex inequalities, with possible applications in problems such as optimization and differential equations with convex shapes attached.
Author Contributions
Conceptualization, H.A.; validation, L.-I.C. and A.A.; formal analysis, L.-I.C. and A.A.; investigation, H.A. and V.-D.B.; resources, H.A. and V.-D.B.; writing—original draft, H.A. and V.-D.B.; writing—review and editing, H.A., O.M.A. and A.A.; visualization, V.-D.B., O.M.A., and L.-I.C.; supervision, V.-D.B. and O.M.A.; project administration, V.-D.B., L.-I.C., and O.M.A. All authors have read and agreed to the published version of the manuscript.
Funding
The work is supported by King Saud University (Supporting Project number RSPD2024R860), Riyadh, Saudi Arabia).
Data Availability Statement
Data are contained within the article.
Acknowledgments
The authors would like to extend their sincere appreciation to Supporting Project number (RSPD2024R860) King Saud University, Riyadh, Saudi Arabia.
Conflicts of Interest
The authors declare no conflicts of interest.
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