1. Introduction
Mathematical inequalities are today acknowledged and taught as some of the most practical areas of mathematics as they have successfully influenced numerous scientific and engineering domains. Their practical applications can be found in information theory, economics, engineering, and finance [
1,
2,
3]. They have particularly contributed to the study of numerical and partial differential equations. Fractional analysis is a striking idea used to explore hidden information that cannot be explored while dealing with classical analysis. Fractional integral inequalities are crucial in establishing the existence and uniqueness of solutions for certain fractional differential equations. The subject of fractional analysis has been studied by many mathematicians. These mathematicians have studied several fractional derivatives and integrals using a variety of methods and techniques.
Convex functions have witnessed a surge in popularity during the past few years. They are utilized frequently in a variety of contemporary analytic fields across the spectrum of mathematical specializations. They are particularly useful in terms of optimization theory due to the fact that they exhibit a large number of useful properties in this field.
Theory of inequalities pertaining to convex functions exhibit strong influence on one another. Convex functions have allowed for the discovery of a large number of noteworthy and beneficial inequalities that are quite useful in applied sciences. The most noteworthy classical definition of a convex function on convex sets in terms of line segments can be considered as:
where
is a mapping valid for all
and
A fundamental and well-known inequality for convex functions is the Jensen inequality and its related inequalities. This is because the Jensen inequality and its associated inequalities can be used in a wide variety of fields, including optimization, probability theory, information theory, and computer challenges [
4,
5,
6,
7]. Numerous well-known inequalities for convex functions are frequently used in current research.
Fractional calculus is an effective vision that may be used to explain physical events and issues that occur in daily life. This branch of mathematics is included in the broad category of applied mathematics. The fractional integral and derivative operators helps with improving the relationships between mathematics and other specializations by providing solutions that are more closely related to real-world problems. Fractional integral operators and fractional derivative operators have contributed new concepts to fractional analysis in terms of their application areas and spaces [
8,
9,
10]. They do not share the same singularity, locality, or kernel qualities with one another. One way to define fractional calculus is an extension of the notion of the derivative operator, where the notion of the derivative operator can be extended from the integer order to any rational order. With the help of fractional integrals, one may solve a wide range of issues that come up in the fields of science and engineering. The theory is developed through the introduction of the most useful fractional integral operators, named Riemann–Liouville fractional integral operators, which lay the foundation of fractional calculus in the broader sense.
Definition 1 ([
8])
. The formula for the left- and right-sided Riemann–Liouville fractional integral operators of order is as follows, where :andwhere . Here, The fractional integral simplifies to the classical integral for . The definition of majorization that we use to explain our results is as follows:
Definition 2 ([
11])
. Let and be two -tuple of real numbers such that , then is said to be majorized by (or is a said to be majorized by , symbolically , ifand Definition 3 ([
12])
. Let be a convex function on the given interval and , and let be the non-negative weight such that , Then, Jensen’s inequality can be given as:for all , for Jensen’s inequality (
3) recaptures the concept of a convex function when t = 2. Jensen’s inequality has numerous significant applications in the fields of optimization, statistics, finance, and economics, but it is particularly useful in foretelling the estimates of the limits of distance functions in information theory [
13,
14,
15].
The Jensen–Mercer inequality, an important mathematical inequality deducted from Jensen’s inequality, is widely used in the fields of optimization and convex analysis. By giving the convex combination an upper limit, it provides a limitation on the convex combination of a function over a set of variables where the weights of the variables form a probability distribution. A wide range of disciplines, including economics, statistics, and machine learning, have used the Jensen–Mercer inequality. It is frequently used in various fields to create crucial boundaries and validate important conclusions.
A fundamental idea in convex analysis, the Jensen–Mercer inequality has several applications in various branches of mathematics. It provides a result that is unavoidably necessary in order to understand how convex functions behave when they are used with convex combinations. McD Mercer in 2003 [
16] first proposed an intriguing variation of Jensen’s inequality, namely, the Jensen–Mercer inequality, which is described as follow:
Definition 4 ([
16])
. Under the assumption of Definition 3, if is a convex function on , thenholds for all finite positive increasing sequences Numerous academics have conducted in-depth research on the Jensen–Mercer inequality. Several methods have been used, including increasing its dimension, obtaining it for convex operators with all of its multiple refinements, obtaining operator variations for super-quadratic functions, upgrading, and performing several generalizations with implications for information theory [
17,
18,
19].
In [
20], Niezgoda introduced the extended version of the Jensen–Mercer inequality by deploying a majorization scheme as follows:
Theorem 1. Consider as the continuous convex function defined on I and a real matrix with as a -tuple such that for all and . Moreover, let weight functions such that . If ζ majorizes every row of , then Taking into account the above notable Niezgoda inequality, Faisal et al. [
21] recently introduced the below-mentioned Hermite–Hadamard-type inequality for majorized tuples:
Theorem 2. Let , and be three -tuples such that , for all , , , and let be a continuous convex function defined on I. If and , then: Researchers that work with various integrals or convex functions have a unique opportunity to apply the notion of majorization. Experts from a range of fields are fascinated by this occurrence. Numerous majorization ideas have been adopted and used in a variety of academic fields, including graph theory, optimization, and economics. Within the discipline of mathematics, the idea of majorization makes up a sizable domain.
The famous Simpson’s inequality error estimates are described as follows:
Definition 5 ([
22])
. Consider as a continuously differentiable mapping of order four on and . Then, the following estimation holds: Numerous mathematicians have explored different possibilities to give estimations of Simpson-type inequalities. One of the possibilities is to use convex mappings because convexity theory provides a consistent and effective method for addressing a wide variety of problems. A wide variety of phenomena can be attributed to the several subfields that make up pure and applied sciences. For closely approximating the results of convex functions in theoretical and applied mathematics, the Simpson and Newton approaches have become popular due to their usefulness and variety. Specifically, a range of Simpson-type inequalities, applicable to first-, second-, and third-order differentiable functions have been formulated by utilizing fractional integral operators. Dragomir et al. presented a noteworthy finding [
22] when they utilized the quadrature formula for numerical integration in conjunction with the Simpson-type inequality. Alomari et al. [
23] and Sarikaya et al. [
24] have developed several additional Simpson-type inequalities for s-convex functions, which offer enhanced bounds. Later, researchers have utilized Riemann–Liouville fractional integral operators to establish Simpson-type inequalities for diverse families of differentiable and convex functions. For the first time, Chen and Huang presented some new inequalities of the Simpson type by employing s-convexity via fractional integrals [
25]. Later on, Kermausuor [
26] employed Katugampola fractional integral operators to investigate Simpson-type integral inequalities for s-convex functions in the second sense. In recent years, research on Simpson-type inequalities for twice-differentiable functions has seen significant growth. Sarikaya et al. [
27] first presented some Simpson-type inequalities for functions whose second derivatives absolute values are convex. Hezanci et al. [
28] established an identity involving twice-differentiable functions using Riemann–Liouville fractional integral operators, from which a series of Simpson-type inequalities were derived. Butt et al. [
29] explored a new fractional Mercer–Simpson-type inequality for twice-differentiable convex functions pertaining to nonsingular or nonlocal kernels with various applications. Also, there are a number of results reported in the literature on integral inequalities involving three-times-differentiable convex functions to give estimations of Simpson-type integral inequalities. Liu and Chun et al. investigated Simpson-type inequalities with respect to third derivatives being h-convex and extended s-convex [
30], considering Riemann–Liouville integrals. Similarly, using Riemann–Liouville fractional integrals, Hezenci et al. [
31] established Simpson-type inequalities containing three-times-differentiable convex functions. For the first time, Niezgoda [
20] used the concept of majorization and extended the Jensen–Mercer inequality. Based on this extended concept of majorization, Butt et al. gave new bounds for Newton–Simpson-type inequalities in [
32]. There are scarce results presented in the literature concerning Simpson’s inequality for majorized tuples.
The primary objective of this study is to employ majorization-type outcomes for Simpson-type inequalities through the use of Riemann–Lioville fractional integrals, wherein a convex function is implicated. We present novel estimates of Simpson’s fractional inequalities utilizing convexity and the Niezgoda–Jensen–Mercer inequality, which are related to majorization. The present paper presents Simpson’s estimations expressed in terms of special q-digamma and Bessel functions, which establish elegant connections.
2. Main Results
We start by establishing novel auxiliary identity for the Riemann–Liouville integral operator using majorized tuples, which can be used to derive future advancements.
Lemma 1. Let , and be three tuples such that for all and with and being the continuous differentiable functions on interval . If and ζ majorize , , and , then the following identity:is satisfied for Proof. Let us start with
where
and
are given as
Now, by applying integration in parts on
, we obtain
By substituting the variables, we obtain
Similarly, by applying integration on
, we have
Before using the concept of the fractional integral definition, it is necessary to show that
and .
Given that . Furthermore, and . Then
. Moreover,
Adding
on both sides yields
Similarly, one can construct
Adding
and
, we have
Multiply by
on both sides of (
11) and then simplifying the identity, we obtain (
7) which is desired result. □
Remark 1. If we set in (7), we get the subsequent equality:which is a Mercer equality via the Riemann–Liouville fractional integral, which is new in the literature. Remark 2. If we set in Remark 1, we obtain the following classical Simpson–Mercer result:which is a new identity in the literature. Remark 3. For and in Remark 1, we obtain a lemma via a Riemann–Liouville fractional integral operator, which was for the first time proved by Chen and Huang in [25]. If we set , and in Remark 1, one can recapture the classical Simpson lemma that was proved by Sarikaya et al. in [24]. Some new fractional Simpson-type inequalities via majorization based on Lemma 1 for convex function are presented hereafter.
Theorem 3. Under the consideration of Lemma 1, if is continuous convex function on I, then for all , one obtains the following fractional integral inequality: Proof. By utilizing Lemma 1, along with the modulus property, we have
By utilizing Theorem 1, for
,
, and
, we have
□
Remark 4. If we put in Theorem 3, we obtain the following inequality:which is the majorized estimation of the fractional Mercer inequality in the literature. Remark 5. If we put and into Theorem 3, we acquire the classical Simpson–Mercer estimates proved by Butt et al. in [32]. If we put and in Remark 4, we obtain fractional estimates of the Simpson-type inequality given in [25] for . Putting , , and in Remark 4, we acquire classical Simpson estimates for the differentiable convex function given by Sarikaya et al. in [24] for . Now, we give some new fractional estimates of the Simpson-type inequality by using some well-known inequalities, i.e, Hölder’s inequality, the power mean inequality, and Young’s inequality.
Theorem 4. Under the considerations of Lemma 1, if is continuous convex on I and , then for all , we obtain the below-mentioned majorized fractional integral inequality:where and . Proof. By applying the modulus and Hölder’s inequality to Lemma 1, we have:
By utilizing the convexity of
and applying Theorem 1, for
,
, and
, we have
Here, we use
for
and
. □
Remark 6. Here, by substituting in Theorem 4, we have the following inequality:which gives new fractional Mercer estimates. Remark 7. Selecting in Remark 6, we obtain the following bound of classical Mercer inequality in terms of conjugate exponent: Remark 8. By substituting and in the previous Remark 6, we recapture the fractional Simpson estimates proved in [25] for . If we choose , , and in Remark 6, we obtain the classical results that were proved by Sarikaya et al. [24] for . For convex mappings, we give a new approach to construct the upper bound for the right-hand side of the majorized Simpson’s inequality in the following Theorem.
Theorem 5. Under consideration of Lemma 1, if is continuous convex on I and , then for all , one obtains the undermentioned fractional integral inequality:that is satisfied for and . Proof. It is easy to obtain the inequality (
16) as mentioned in the proof of Theorem 4. Since
is convex on
I, by employing Theorem 2 for
, it is easy to obtain
By substituting (
18) and (
19) into (
16), we obtain (
17), which is the required result. □
Remark 9. Here, by using in Theorem 5, we obtain the following fractional Mercer-type estimates, Remark 10. By substituting and into Remark 9, we obtain the estimates of the fractional Simpson inequality pertaining to Riemann–Liouville integral operators proved by Chen and Huang in [25] for . If we substitute , and in Remark 9, we deduce the classical form of the inequality given by Sarikaya et al. in [24] for . Theorem 6. In consideration of Lemma 1, if is a continuous convex function on I and , then, for all , one obtains the following fractional integral inequality:that is satisfied for and . Proof. By utilizing the power mean inequality on Lemma 1, we have
Since
is convex on
I, by applying Theorem 1, for
,
and
, we obtain
Now, by substituting (
22) and (
23) into (
21) and then by simplifying the integrals, we obtain (
20), which finishes the proof. □
Remark 11. The above inequality (20) leads to develop the following new variant of fractional Simpson–Mercer inequality by substituting in Theorem 6.which is new in the literature. Remark 12. By substituting in Remark 11, we obtain the following classical form of the inequality:which is a new result in the literature. Remark 13. From the previous result for , , and , we obtain the bounds for the classical Simpson inequality for the differentiable convex functions results proved by Sarikaya et al. in [25] for . Theorem 7. In consideration of Lemma 1, if is a continuous convex function on I and , for all , we obtain the fractional integral inequality given as: Proof. From Lemma 1, by applying Young’s inequality on (
13), we have
By applying the convexity of
and using Theorem 1, for
,
and
, we have
By simplifying the integrals in (25), we deduce (
24), which completes the proof. □
Remark 14. If we substitute in Theorem 6, then we obtain the following new Mercer estimates using Young’s inequality: Remark 15. If we set and in Theorem 6, then we obtain the following bounds by using Young’s inequality: Remark 16. If we set , , and in Remark 15, we obtain the following bounds of Simpson’s inequality in the classical sense: