1. Introduction
A real valued function
is called convex on the interval
if the condition
holds for all
and
We say that
is concave if
is convex.
The concept of convexity in the sense of integral problems is a fascinating area for research. Therefore, many inequalities have been introduced as applications of convex functions such as Gagliardo–Nirenberg-type inequality [
1], Ostrowski-type inequality [
2], Olsen-type inequality [
3], midpoint-type inequality [
4] Hardy-type inequality [
5], and trapezoidal-type inequality [
6]. Among those, the Hermite–Hadamard inequality is an interesting outcome in convex analysis. The 𝐻𝐻-inequality [
7,
8] for convex function
on an interval
is defined in the following way:
for all
If
is concave, then inequality (2) is reversed. For more useful details, see [
9,
10] and the references therein.
In 2013, Sarika et al. [
11] introduced the following fractional
-inequality for convex function:
where
assumed to be a positive function on
,
with
, and
and
are the left sided and right sided Riemann–Liouville fractional of order
, and, respectively, are defined as follows:
If then from Equation (3), we obtain Equation (2). We can easily say that inequality (3) is a generalization of inequality (2). Many fractional inequalities have been introduced by several authors in the view of inequality (3) for different convex and nonconvex functions.
In 1966, the concept of interval analysis was first introduced by the late American mathematician Ramon E. Moore in [
9]. Since its inception, various authors in the mathematical community have paid close attention to this area of research. Interval analysis has been found to be useful in global optimization and constraint solution algorithms, according to experts. It has slowly risen in popularity over the last few decades. Scientists and engineers engaged in scientific computation have discovered that interval analysis is useful, especially in terms of accuracy, round-off error affects, and automatic validation of results. After the invention of interval analysis, the researchers working in the area of inequalities wants to know whether the inequalities in the abovementioned results can be found substituted with the inclusions relation. In certain cases, the question is answered correctively. In light of this, Zhao et al. [
12] arrived at the following conclusion for an interval-valued function (IVF).
Let
be a convex IVF given by
for all
, where
is a convex function and
is a concave function. If
is Riemann integrable, then
After that, several authors developed a strong relationship between inequalities and IVFs by means of an inclusion relation via different integral operators, as one can see in Costa [
13], Costa and Roman-Flores [
14], Roman-Flores et al. [
15,
16], and Chalco-Cano et al. [
17,
18], but also in more general set valued maps by Nikodem et al. [
19], and Matkowski and Nikodem [
20]. In particular, Zhang et al. [
21] derived the new version of Jensen’s inequalities for set-valued and fuzzy set-valued functions by means of a pseudo order relation, and proved that these Jensen’s inequalities are a generalized form of Costa Jensen’s inequalities [
13].
In the last two decades, in the development of pure and applied mathematics, fractional calculus has played a key role. Yet, it attains magnificent deliberation in the ongoing research work; this is because of its applications in various directions such as image processing, signal processing, physics, biology, control theory, computer networking, and fluid dynamics [
22,
23,
24,
25].
For inclusion relation, Budek [
26] established the following strong relation between convex IVF and interval
-inequality as a counter part of (6)
where
is assumed to be a positive IVF on
,
with
, and
and
are the left sided and right sided Riemann–Liouville fractional of order
.
If then from (7), we obtain (6). We can easily say that inequality (7) is a generalization of inequality (6). Many fractional inequalities have been introduced by several authors in the view of inequality (7) for different convex- and nonconvex-IVFs.
Due to the vast applications of convexity and fractional 𝐻𝐻-inequality in mathematical analysis and optimization, many authors have discussed the applications, refinements, generalizations, and extensions, see [
27,
28,
29,
30,
31,
32,
33,
34,
35,
36,
37,
38] and the references therein.
Recently, fuzzy interval analysis and fuzzy interval differential equations have been put forward to deal the ambiguity originating from insufficient data in some mathematical or computer models that find out real-world phenomena [
39,
40,
41,
42,
43,
44,
45,
46,
47,
48,
49,
50]. There are some integrals to deal with fuzzy-IVFs, where the integrands are fuzzy-IVFs for instance; recently, Costa et al. [
13] derived some Jensen’s integral inequality for IVFs and fuzzy-IVFs through Kulisch–Miranker and fuzzy order relation, see [
47]. Recently, Allahviranloo et al. [
48] introduced the following fuzzy interval Riemann–Liouville fractional integral operators:
Let
and
be the collection of all Lebesgue measurable fuzzy-IVFs on
. Then, the fuzzy interval left and right Riemann–Liouville fractional integral of
with order
are defined by
and
respectively, where
is the Euler γ function. The fuzzy interval left and right Riemann–Liouville fractional integral
based on left and right end point functions can be defined as
where
and
Similarly, we can define right Riemann–Liouville fractional integral of based on left and right end point functions.
Motivated by the ongoing research work and by the importance of the concept of fuzzy convexity, in
Section 2, we have discussed the preliminary notions, definitions and some related results.
Section 3, introduces discrete Jensen and Schur inequalities for convex fuzzy-IVF by means of fuzzy order relation. Basically, these inequalities are generalizations of convex fuzzy IVFs.
Section 4 derives the most important class of
-inequalities for convex fuzzy IVFs, known as fuzzy interval fractional
-inequalities, and establishes some related inequalities.
2. Preliminaries
In this section, we first give some definitions, preliminary notations, and results which will be helpful for further study. Then, we define new definitions and discuss some properties of convex fuzzy-IVFs.
Let
be the space of all closed and bounded intervals of
and
be defined by
If , then is said to be degenerate. In this article, all intervals will be non-degenerate intervals. If , then is called a positive interval. The set of all positive intervals is denoted by and defined as
Let
and
be defined by
Then, the Minkowski difference
, addition
and
for
are defined by
and
The inclusion “
” means that
Remark 1. [47] The relation “
”
defined on byfor all it is an order relation. For given we say that if and only if or .
For
the Hausdorff–Pompeiu distance between intervals
and
is defined by
It is a familiar fact that is a complete metric space.
Let be the set of real numbers. A fuzzy subset of is characterized by a mapping called the membership function, for each fuzzy set and , then -level sets of is denoted and defined as follows . If , then is called support of . By we define the closure of .
Let be the family of all fuzzy sets and denote the family of all nonempty sets. is a fuzzy set. Then, we define the following:
- (1)
is said to be normal if there exists and
- (2)
is said to be upper semicontinuous on if for given there exist there exist such that for all with
- (3)
is said to be fuzzy convex if is convex for every ;
- (4)
is compactly supported if is compact.
A fuzzy set is called a fuzzy number or fuzzy interval if it has properties (1)–(4). We denote by the family of all intervals.
Let
be a fuzzy interval if and only if
-levels
is a nonempty compact convex set of
. From these definitions, we have
where
Proposition 1. [
14]
If ,
then relation “
”
defined on bythis relation is known as partial order relation. For and , the sum , product , scalar product and sum with scalar are defined by:
Then, for all
we have
For
such that
then by this result we have existence of Hukuhara difference of
and
, and we say that
is the H-difference of
and
and denoted by
. If H-difference exists, then
A partition of
is any finite ordered subset
having the form
The mesh of a partition
is the maximum length of the subintervals containing
that is,
Let
be the set of all
such that mesh
For each interval
, where
, choose an arbitrary point
and taking the sum
where
. We call
a Riemann sum of
corresponding to
Definition 1. [10] A functionis called interval Riemann integrable (
-integrable) on if there exists such that, for each ,
there exists such thatfor every Riemann sum of corresponding to and for arbitrary choice of for Then, we say that is the -integral of on and is denoted by .
Moore [
9] first proposed the concept of Riemann integral for IVF, and it is defined as follows:
Theorem 1. [9] Ifis an IVF on such thatThen, is Riemann integrable over
if and only if and are both Riemann integrable over such thatThe collection of all Riemann integrable real valued functions and Riemann integrable IVF is denoted by and respectively. Definition 2. [30] A fuzzy mapis called fuzzy-IVF. For each whose
-levels define the family of IVFs are given by for all Here, for each the left and right real valued functions are also called lower and upper functions of .
Remark 2. Ifis a fuzzy-IVF, thenis called continuous function atif for eachboth left and right real valued functionsandare continuous at
The following conclusion can be drawn from the above literature review, see [
10,
14,
30]:
Definition 3. Letis called fuzzy-IVF. The fuzzy Riemann integral ofoverdenoted by,
it is defined level by levelfor allwherecontains the family of left and right functions of IVFs.is-integrable overifNote that, if left and right real valued functions are Lebesgue-integrable, thenis fuzzy Aumann-integrable overdenoted by,
see [30]. Theorem 2. Letbe a fuzzy-IVF, whose-levels obtain the collection of IVFsare defined byfor alland for allThen,is-integrable overif and only ifandboth are-integrable over. Moreover, if is
-integrable over thenfor all For each ,
and denote the collection of all -integrable fuzzy-IVFs and, -integrable left and right functions over .
Definition 4. [4] A real valued functionis called-convex function iffor allIf (27) is reversed, thenis called-concave. Definition 5. [6] The fuzzy-IVFis called convex fuzzy-IVF oniffor allwherefor allIf (28) is reversed, thenis called concave fuzzy-IVF on.
is affine if and only if it is both convex and concave fuzzy-IVF. Remark 3. Ifand, then we obtain the inequality (1).
3. Fuzzy-Interval Jensen and Schur Inequalities
In this section, the discrete Jensen and Schur inequalities for convex fuzzy-IVF are proposed. Firstly, we give the following results connected with Jensen inequality for convex fuzzy-IVF by means of fuzzy order relation.
Theorem 3. (Jensen inequality for convex fuzzy-IVF) Let,
andbe a convex fuzzy-IVF, whose-levels define the family of IVFsare given byfor alland for all. Then,whereIfis concave, then inequality (29) is reversed. Proof. When
k = 2 then inequality (29) is true. Consider inequality (28) is true for
then
Now, let us prove that inequality (29) holds for
Therefore, for each
we have
Similarly, for
, we have
From (30) and (31), we have
that is,
and the theorem has been proved. □
If then Theorem 3 reduces to the following result:
Corollary 1. Letandbe a convex fuzzy-IVF, whose-levels define the family of IVFsare given byfor alland for all. Then,Ifis a concave fuzzy-IVF, then inequality (32) is reversed. The next Theorem 4 gives the Schur inequality for convex fuzzy-IVFs.
Theorem 4. (Schur inequality for convex fuzzy-IVF)Let
be a convex fuzzy-IVF, whose
-levels define the family of IVFs
are given by
for all
and for all
. If, for
, such that
and
,
, we have
If
is concave, then inequality (33) is reversed.
Proof. Let
and
Consider
, then
Since
is a convex fuzzy-IVF, then, by hypothesis, we have
Therefore, for each
we have
Similarly, for
, we have
From (35) and (36), we have
That is
Hence, the result has been proved. □
Now we obtain a refinement of Schur inequality for convex fuzzy-IVF is given in the following result.
Theorem 5. Let,
andbe a convex fuzzy-IVF, whose-levels define the family of IVFsare given byfor alland for all. IfthenwhereIfis concave, then inequality (37) is reversed. Proof. Consider
,
. Then, by hypothesis, we have
Therefore, for each
, we have
Above inequality can be written as,
Taking sum of all inequalities (38) for
we have
Similarly, for
, we have
From (39) and (40), we have
Thus,
this completes the proof. □
We now consider some special cases of Theorems 3 and 5.
If with then Theorems 3 and 5 reduce to the following results:
Corollary 2. (Jensen inequality for convex function) Let,
and letbe a non-negative real-valued function. Ifis a convex function, thenwhere If
is concave function, then inequality (41) is reversed. Corollary 3. Let,
andbe a non-negative real-valued function. Ifis a convex function andthenwhereIfis a concave function, then inequality (42) is reversed. 4. Fuzzy-Interval Fractional Hermite–Hadamard Inequalities
In this section, we shall continue with the following fractional -inequality for convex fuzzy-IVFs, and we also give fractional –Fejér inequality for convex fuzzy-IVF through fuzzy order relation. In what follows, we denote by the family of Lebesgue measurable fuzzy-IVFs.
Theorem 6. Letbe a convex fuzzy-IVF onwhose-levels define the family of IVFsare given byfor alland for all. If, then Ifis concave fuzzy-IVF, then Proof. Let
be a convex fuzzy-IVF. Then, by hypothesis, we have
Therefore, for every
, we have
Multiplying both sides by
and integrating the obtained result with respect to
over
, we have
Let
and
Then, we have
Similarly, for
, we have
From (45) and (46), we have
In a similar way as above, we have
Combining (47) and (48), we have
Hence, the required result. □
Remark 4. From Theorem 6 we clearly see that
LetThen, Theorem 6 reduces to the result for convex-IVF given in [31]:Ifandthen from Theorem 6 we get inequality (3). Letand. Then, from Theorem 6 we obtain inequality (2).
Example 1. Let, and the fuzzy-IVFdefined byThen, for eachwe have. Since left and right end point functionsare convex functions for each, thenis convex fuzzy-IVF. We clearly see thatandNote thatThereforeand Theorem 6 is verified. Now, we derive fractional HH–Fejér inequality for convex fuzzy-IVF, which generalize the classical fractional
and
–Fejér inequality. Firstly, we give the following result connected with the right part of the classical
–Fejér inequality for convex fuzzy-IVF through fuzzy order relation, which is known as second fuzzy fractional
–Fejér inequality.
Theorem 7. Second fuzzy fractional–Fejér inequality) Letbe a convex fuzzy-IVF with, whose-levels define the family of IVFsare given byfor alland for all. Ifandsymmetric with respect tothenIfis concave fuzzy-IVF, then inequality (49) is reversed. Proof. Let
be a convex fuzzy-IVF and
. Then, for each
we have
and
After adding (50) and (51), and integrating over
we get
Since
is symmetric, then
Similarly, for
, we have
From (54) and (55), we have
Now, we obtain the following result connected with left part of classical
–Fejér inequality for convex fuzzy-IVF through fuzzy order relation, which is known as first fuzzy fractional
–Fejér inequality. □
Theorem 8. (First fuzzy fractional–Fejér inequality) Letbe a convex fuzzy-IVF with, whose-levels define the family of IVFsare given byfor alland for all. Ifandsymmetric with respect tothenIfis concave fuzzy-IVF, then inequality (56) is reversed. Proof. Since
is a convex fuzzy-IVF, then for
we have
Since
, then, by multiplying (57) by
and integrating it with respect to
over
we obtain
Let
. Then, we have
Similarly, for
, we have
From (60) and (61), we have
that is
This completes the proof. □
Remark 5. Ifthen from Theorem 7 and Theorem 8, we get Theorem 3.
Let
. Then, we obtain the following
–Fejér inequality for convex fuzzy-IVF, which is also new one.
If and , then from Theorems 7 and 8, we get the classical -inequality (2).
If
and
, then from Theorems 7 and 8, we obtain the classical
–Fejér inequality [
50].
Example 2. We consider the fuzzy-IVFdefined by, Then, for each
we have
. Since end point functions
are convex functions for each
, then
is convex fuzzy-IVF. If
then
, for all
. Since
and
. If
, then we compute the following:
and
From (62) and (63), we have
for each
Hence, Theorem 8 is verified.
From (63) and (64), we have
From Theorems 9 and 10, we obtain some fuzzy-interval fractional integral inequalities related to fuzzy-interval fractional -inequalities.
Theorem 9. Letbe two convex fuzzy-IVFs onwhose-levelsare defined byandfor alland for all. Ifand, thenwhereandand Proof. Since
both are convex fuzzy-IVFs, then for each
we have
and
From the definition of convex fuzzy-IVFs it follows that
and
, so
Analogously, we have
Adding (65) and (66), we have
Taking multiplication of (67) by
and integrating the obtained result with respect to
over (0,1), we have
Similarly, for
, we have
From (68) and (69), we have
That is
and the theorem has been established. □
Example 3. Let,
, and.
Then, for eachwe haveandSince left and right end point functions,
andare convex functions for each, thenandboth are convex fuzzy-IVF. We clearly see thatandNote thatTherefore, we haveIt follows thatand Theorem 9 has been demonstrated. Theorem 10. Letbe two convex fuzzy-IVFs, whose-levels define the family of IVFsare given byandfor alland for all. If, thenwhereandand Proof. Consider
are convex fuzzy-IVFs. Then, by hypothesis, for each
we have
Taking multiplication of (67) with
and integrating over
we get
Similarly, for
, we have
From (71) and (72), we have
That is
Hence, the required result. □