1. Introduction and Preliminaries
Convex Analysis is the branch of mathematics in which we study the properties of convex sets and convex functions. The theory of convexity has played a vital role in different branches of pure and applied sciences through its numerous applications, and has played a significant role in the development of the theory of inequalities. Here, we recall the notions of convex sets and convex functions as follows:
A set
is said to be convex if
A function
is said to be convex if
Many famous inequalities known to us today are direct consequences of the applications of the convexity property of functions. A very useful result which has many applications in different fields of applied and engineering sciences is the Hermite–Hadamard inequality, which provides a necessary and sufficient condition for a function to be convex. This result was obtained independently by Hermite and Hadamard, and reads as follows:
Let
be a convex function; then,
In recent years, many different novel approaches have been used to obtain new variants of these inequalities. For example, Sarikaya et al. [
1] obtained the first fractional analogue of the Hermite–Hadamard inequality. In 1985, Ramik [
2] deduced inequalities through fuzzy numbers and used the inequality in fuzzy optimization. In [
3], the authors established Jensen-type inequalities for the notion of fuzzy interval valued mappings. Costa et al. [
4] used the concept of interval-valued fuzzy convex mappings to compute new integral inequalities. Zhao et al. [
5] presented the idea of generalized interval-valued convexity to investigate inequalities of the Jensen and Hermite–Hadamard types. In [
6], Liu et al. studied the modular inequalities of interval-valued soft sets with the aid of
J-inclusions. In 2021, Yang et al. [
7] formulated new inequalities of the Hermite–Hadamard type in association with exponential fuzzy interval-valued convex mappings. In [
8], the authors used the idea of interval-valued mappings to compute Ostrowski-type inequalities and apply them to numerical integration. In [
9], Santos-Gomez investigated coordinated inequalities via interval-valued fuzzy pre-invex functions. In [
10], Khan et al. derived new Hermite–Hadamard-like inclusions involving harmonically interval-valued fuzzy mappings. In [
11], the authors concluded that certain Hermite–Hadamard inequalities and their weighted forms, known as Fejer-type inclusions, involve generalized fractional operators with an exponential kernel. For recent developments and applications pertaining to the Hermite–Hadamard inequality, see [
12,
13,
14,
15,
16,
17,
18,
19].
Before we proceed further, let us first recall several well known concepts and results from fuzzy interval analysis.
First of all, suppose
is the space of all closed and bounded intervals of
and let
be defined as
If , then is said to be degenerate. Throughout the sequelae of this paper, all intervals are non-degenerate intervals. If , then is called a positive interval. The set of all positive intervals is denoted by and is defined as .
Let
and
be defined as
Then, the Minkowski difference
, addition
, and
for
are defined by
and
Definition 1 ([
20]).
The relation “” defined on is provided byif and only iffor all is a pseudo-order relation. The relation is coincident to on , where “≤” is partial order relation on .It can be seen that “” looks like “left and right” on the real line , thus, we say that “” is the “left and right” order (or “LR” order, in short).
Remark 1 ([
20,
21]).
A fuzzy set of is a function of . For each fuzzy set , ϑ-level sets of are denoted and defined as follows: , and by or , its support set, i.e., .Let be the family of all fuzzy sets and be a fuzzy set. Then, we define the following:
is said to be normal if there exists and ;
is said to be upper semi-continuous on if, for given , there exist and there exist such that for all with ;
is said to be fuzzy convex if is convex for every ;
is compactly supported if is compact.
A fuzzy set is called a fuzzy number or fuzzy interval if it satisfies the above-mentioned four properties, and denotes the family of all fuzzy intervals.
Let
be a fuzzy interval if, and only if,
-levels
is a nonempty compact convex set of
. From these definitions, we have
where
Thus, a fuzzy interval can be identified by a parameterized triplet. For more details, see [
22].
where the two end point functions
and
are used to characterize a real fuzzy interval.
Proposition 1. If , then the relation “≼” is defined on bythis relation is known as the partial order relation. For and , the sum , product , scalar product , and sum with scalar are defined by For such that , by this result we have the existence of a Hukuhara difference of γ and χ; we can say that is the H-difference of γ and χ, denoted by . If an H-difference exists, then Definition 2 ([
23]).
A fuzzy map is a fuzzy interval valued function for each for which the ϑ-levels define the family of . are provided by for all . Here, for each , the left and right real valued functions are called the lower and upper functions of . Definition 3 ([
24]).
Let be a fuzzy interval-valued function. Then, the fuzzy Riemann integral of over , denoted by , is provided level-wise byfor all , where denotes the collection of Riemannian integrable functions of interval-valued functions. is -integrable over if . Theorem 1 ([
22])
Let be a fuzzy interval-valued function, and for all , ϑ-levels define the family of interval valued functions provided by for all . Then, is fuzzy Riemann integrable (-integrable) over if and only if and both are Riemann integrable (R-integrable) over . Moreover, if is -integrable over , thenfor all , where represent interval Riemann integration of . For all , denotes the collection of all -integrable fuzzy interval-valued functions over . Remark 2 ([
22]).
If is a fuzzy interval valued function, then is called a continuous function at if, for each , both the left and right real valued functions and are continuous at . Definition 4 ([
25]).
Let and be the collection of all Lebesgue measurable functions on . Then, the fractional integral related to the new fractional derivative with a nonlocal kernel of a mapping is defined as follows:The right-hand side of the integral operator is as follows: Here, is the gamma function and is called the normalization function, which satisfies the condition For more details, see [26,27]. Now, we define the fuzzy left and right fractional integral based on the left and right end-point functions.
Definition 5. Let and be the collection of all Lebesgue-measurable interval valued functions on . Then, the fuzzy fractional integral related to the new fractional derivative with a nonlocal kernel of a mapping is defined as follows:whereand Here, is the gamma function and is called the normalization function.
Similarly, the left and right end point functions can be used to define the right fractional integral.
Definition 6 ([
20]).
The fuzzy interval valued function is called an LR–convex fuzzy interval valued function on iffor all , where for all . If it is reversed, then is called an LR–concave fuzzy interval valued function on . Definition 7 ([
28]).
Let K be a non-empty set in real Hilbert Space H. Let be a continuous function and let be an arbitrary continuous function. Then, the set in real Hilbert space H is said to be a bi-convex set with respect to an arbitrary bifunction , if The biconvex set can be called a -connected set.
The main motivation of this paper is to derive generalization fractional integral inclusions involving a new class of convexity called LR–bi-convex fuzzy interval-valued functions. Our paper is organized as follows: in
Section 2, we derive new fractional analogues of Hermite–Hadamard inequalities involving a new fractional integral called an LR–AB fractional integral. We discuss several special cases that demonstrate that our results are quite unifying. In order to check the validity of our outcomes, we discuss several non-trivial numerical examples. In
Section 3, we present an application of our proposal to special means for positive real numbers. It is our hope that the technique presented in this paper will inspire interested readers and stimulate further research in following the same direction.
2. Main Results
In this section, we discuss our main results. First, we introduce the class of LR–bi-convex fuzzy interval-valued functions.
Definition 8. The fuzzy interval-valued function is called an LR–bi-convex fuzzy interval-valued function on iffor all , where for all . If it is reversed, then is called an LR–bi-concave fuzzy interval-valued function on . Remark 3. If , then we have inequality (1). If with , then we have the definition of a classical bi-convex function.
If with and , then we have the definition of a classical convex function.
Therefore, we need the following condition to obtain new results.
Condition M.
Assume that the bifunction
satisfies the following assumption:
For more details regarding condition M, see [
28].
Theorem 2. Let be a bi-convex set and be a fuzzy interval-valued function such thatfor all and . Then, is an LR–bi-convex-fuzzy interval-valued function on if and only if and both are fuzzy bi-convex functions. Proof. Assume that
are fuzzy bi-convex functions. Then, for all
, we have
and
From Definition 8 and order relation
, we have
that is,
Hence, is an LR–bi-convex fuzzy interval-valued function.
Conversely, let
be an LR–bi-convex fuzzy interval-valued function. Then, for all
and
, we have
that is,
thus, it follows that
and
This completes the proof. □
Now, we present the Hermite–Hadamard ineqaulity for an LR–bi-convex fuzzy-interval valued function.
Theorem 3. Let be an LR–bi-convex fuzzy interval-valued function on , with its ϑ-levels defining the family of interval-valued functions provided by for all and for all . If φ satisfies Condition M and , then If is an LR–bi-concave fuzzy interval-valued function, then Proof. Let
be an LR–bi-convex fuzzy-interval valued function. If Condition M holds true, then by hypothesis, we have
Therefore, for every
, we have
Multiplying both sides of the above inequalities by
and then integrating the obtained result with respect to
over
, we have
Let
and
. Then, we have
By multiplying both sides of the last inequality by
, we obtain
and
In a similar way as above, we have
Combining inequalities (
2) and (
4), we obtain the result. □
Remark 4. From Theorem 3, we can clearly see that
If then from Theorem 3, we have the following result in fuzzy fractional calculus: Taking in Theorem 3, we obtain the result for an LR–bi-convex fuzzy interval-valued function: Choosing and in Theorem 3, we then obtain the result for an LR–convex fuzzy interval-valued function: Example 1. Let and consider a fuzzy interval valued function with , where is the family of all fuzzy intervals. Now, we can define the fuzzy interval valued function by Then, for each , we have . Because the end point functions are bi-convex functions with respect to , for each , is bi-convex fuzzy interval-valued function. Then,andand Hence, Theorem 3 is verified.
Theorem 4. Let be an LR–bi-convex fuzzy interval-valued function with , the ϑ-levels of which define the family of interval valued functions provided by for all and for all . Let ; , and is symmetric with respect to . If φ satisfies Condition M, then If is an LR–bi-concave fuzzy interval-valued function, then the above inequalities are reversed.
Proof. Let
be an LR–bi-convex fuzzy interval-valued function. Then, for each
, we have
and
After adding the above inequalities (
5) and (
6) and integrating with respect to
over
, we have
and
Because
is symmetric, we have the following successive equalities:
By multiplying both sides of the last inequality by
and then adding the term
, we obtain
From equalities (
7) and (
8), we have
and
□
Example 2. Let and consider a fuzzy interval valued function with , defined by Then, for each , we have . Because end point functions are bi-convex functions with respect to for each , is a bi-convex fuzzy interval-valued function. Ifthen for all . If , thenand Hence, Theorem 4 is verified.
Theorem 5. Let be an LR–bi-convex fuzzy interval-valued function with , the ϑ-levels of which define the family of interval valued functions provided by for all and for all . Let and , and is symmetric with respect to . If φ satisfies Condition M, then If is LR-bi-concave-fuzzy interval valued function, then above inequality is reversed.
Proof. Because
is an LR–bi-convex fuzzy interval-valued function, then for
, we have
Because
, by multiplying above inequalities by
and integrating it with respect to
over
, we obtain
Let
. Then, we have
By multiplying both sides of the last inequality by
, and then adding the term
, we obtain
Now,
from which we have
that is,
This completes the proof. □
Remark 5. If then from Theorem 4 and Theorem 5, we obtain Theorem 3.
Example 3. Let and consider a fuzzy interval-valued function with , defined by Then, for each , we have . Because end point functions are bi-convex functions with respect to for each , is bi-convex fuzzy interval-valued function. Ifthen for all . If , thenand Hence, Theorem 5 is verified.
Theorem 6. Let be two LR–bi-convex fuzzy interval-valued functions with , the ϑ-levels of which define the family of interval valued functions provided by and for all and for all . Let and let φ satisfy Condition M; then,
Proof. Because
are both LR–bi-convex fuzzy interval-valued functions and Condition M holds for
, for each
we have
and
From the definition of LR–bi-convex fuzzy interval-valued functions, it follows that
and
, thus,
Adding the above inequalities (
9) and (
10), we have
By multiplying the above inequality by
and integrating the obtained result with respect to
over
, we have
and
This completes the proof. □
Example 4. Let and consider a fuzzy interval-valued function with , and define and by Then, for each , we have and . Because left and right end point functions and are bi-convex functions with respect to for each , and are bi-convex fuzzy interval-valued functions. Then,andand Hence, Theorem 6 is verified.
Theorem 7. Let be two LR–bi-convex fuzzy interval-valued function with , the ϑ-levels of which define the family of interval valued functions provided by and for all and for all . Let and let φ satisfy Condition M; then,where are defined as in Theorem 6. Proof. Consider
are LR–bi-convex fuzzy interval-valued functions. Then, by hypothesis, for each
we have
By multiplying the last inequalities by
and integrating over
, we obtain
and
This completes the proof. □
Example 5. Under the assumptions of Example 4, we haveandandand Hence, Theorem 7 is verified.
Remark 6. If we take , and in our results, then we obtain classical results.