1. Introduction
Jensen’s inequality is a central inequality in many areas of mathematics. Of the numerous applications of Jensen’s inequality, some are in the recent papers [
1,
2]. The aim of this work is to further develop offshoots of this pivotal inequality.
The focus of this paper is on three types of convexity: generalized uniform convexity,
-convexity and superquadracity. The similar structure of these types of convexity is
This common structure is such that when we apply the same processes on these types of convexity, we get further refinements of known inequalities.
We start with quoting some known definitions and theorems.
The first type of convexity we deal with is generalized uniform convexity:
Definition 1 ([
3])
. Let be an interval. A function is said to be generalized Φ-
uniformly convex if there exists a function such thatholds. If, in addition, , then f is said to be Φ-uniformly convex or uniformly convex with modulus Φ.
In the special case where , , f is a called strongly convex function. Remark 1. In this paper we apply a more restricted definition of uniform convexity (used in [Theorem 2.1] in [4]). A function is said to be Φ
-uniformly convex if there exists a function where Φ
is increasing and vanishes only at , such that (2) holds. Also, it is shown in [3,5] that the inequalityholds when . It is easy to verify that (3) holds for generalized uniformly convex functions and that in these cases . Another inequality satisfied by uniformly convex functions is proved in [Theorem 2.3] in [
4] as follows:
Let
be an uniformly convex function with modulus
be a sequence and
be a permutation on
such that
. Then, the inequality
holds for every convex combination
of points
.
Remark 2. The functions are uniformly convex on with a modulus (see [6]), (these functions are also superquadratic for any real ). Remark 3. In [Theorem 1, Inequality (23)] in [3], it is proved that the set of generalized uniformly convex functions f defined on and Φ
defined on which are both continuously differentiable, satisfy the inequalityfor . In [Theorem 2.1] in [5], (4) is proved for uniformly convex functions. Theorem 1 deals with strongly convex functions, that is, functions f for which :
Theorem 1 ([
7])
. Let be a differentiable and strongly convex function. Suppose and is a nonnegative n-tuple with . Let and . Thenandhold. The second type of convexity dealt with in this paper is -convexity.
Definition 2 ([
8])
. A real value function f defined on a real interval is called Φ
-convex if for all , it satisfieswhere is called the error function.
In [Theorem 3.1] in [
8] it is proved that
Corollary 1. Let f be a Φ
-convex function on , then there exists a function such that for all ,Also, when ,holds. The third type of convexity is superquadracity. It should be mentioned that there are two different concepts of superquadracity, see for instance [
9,
10] and the references cited there. The basic properties of superquadracity as used here were proved in [
11] in 2004. Since then, hundreds of works have been published on this subject.
Definition 3 ([
11])
. A function is superquadratic provided that for all there exists a constant such thatfor all . If the reverse of (5) holds, then φ is called subquadratic. Remark 4 ([
11])
. If φ is superquadratic, then the inequality on holds for and Lemma 1 and Theorem 2 are about superquadracity and its relation to superadditivity. In [
12], Beckenbach deals with superadditivity inequalities, and in [
13] Bruckner and Ostrow deal with some function classes related to the class of convex functions. Following these two important works, the authors of [
11] deal with conditions that constitute the scale of superquadracity. In this scale, the following is proved in [Lemma 3.1] in [
11].
Suppose is continuously differentiable and If is superadditive or is non-decreasing, then is superquadratic.
Also in [Lemma 4.1] in [
11], it is proved that a non-positive, non-increasing, superadditive function is superquadratic.
In
Section 2, by adding the definitions of
H-superadditivity, we get theorems for generalized uniformly convex functions and for
-convex functions.
Lemma 1 ([Lemma 1] in [
14])
. Let f be continuously differentiable on and be superadditive on . Then, the function is defined byis nonnegative on , nonincreasing on and nondecreasing on for If also we haveTaking , we see that f is superquadratic. Theorem 2 ([Theorem 1] in [
14])
. Let f be continuously differentiable on and be superadditive on Let be a real n-tuple satisfying and , be such that Then,a.holds for all , where b.
If, in addition, , then f is superquadratic andIn particular c.
If, in addition, and then f is convex increasing and superquadratic and Definition 4. Let the functions and be continuously differentiable. The function g is named H-superadditive if for all with , when the inequalityholds. In
Section 4, we prove the results on the difference between two normalized Jensen functionals for superquadratic and uniformly convex functions.
The Jensen functional is
about which S. S. Dragomir proved the following.
Theorem 3 ([
15])
. Consider the normalized Jensen functional where is a convex function on the convex set C in a real linear space, and are non-negative n-tuples satisfying . Thenprovided that In [
16], the following two theorems on normalized Jensen functionals are proven.
Theorem 4. Under the same conditions and definitions on m and M as in Theorem 3, if I is or and f is a superquadratic function on I, thenand Theorem 5. Let where , and . DenoteandIf is an increasing n-tuple in where I is an interval in thenwhere is a convex function on the interval I. In
Section 4, we replace the coefficients
with
and
m and
M with
and
. In this way, we get results resembling (
9) and (
10) in Theorem 4. There, we also extend Theorem 5 for functions that are offshoots of convexity.
The next sections are arranged as follows:
In
Section 2, we extend the results in Theorem 2 to include the three sets of functions that satisfy (
1).
In
Section 3, we extend the results in Theorem 1, there only on strongly convex functions, to include also generalized uniform convexity,
-convexity and superquadratic functions.
In
Section 4, we improve the result of Theorem 4 regarding the difference between two Jensen’s functionals.
Section 5 shows some examples of the relations between superquadracity and some other types of convex functions.
2. Improvement of Jensen–Steffensen Inequality for Generalized Uniformly Convex Functions and for -Convex Functions
Lemma 2 and Theorem 6 are related to generalized uniform convexity, where in Theorem 6 is -superadditive. We prove this lemma and this theorem similarly to the proofs of Lemma 1 and Theorem 2.
In Corollary 2, we deal with -convexity.
We prove, in detail, the results related to generalized uniform convexity.
Lemma 2. Let f be continuously differentiable on and be -superadditive. Let be continuously differentiable and . Then, the function defined byis nonnegative on , nonincreasing on and nondecreasing on for , and f satisfies (4) (which includes the set of generalized uniformly convex functions). Proof. It is given that
is
-superadditive, and
, therefore, if
, we get that
and if
, then we get that
Together, these show that for any
,
so, we conclude that
D is nonnegative on
and, according to Remark 3
f satisfies (
4), which includes generalized uniformly convex functions with
.
From
as
is
-superadditive for
we have
and similarly for
, we have
This completes the proof. □
Now, we present the main results of this section, where we show that inequality (
3) is satisfied, not only for nonnegative coefficients but also when
is satisfied (called Jensen–Steffensen coefficients) when
.
Theorem 6. Let f be continuously differentiable on and be -superadditive. Let also be continuously differentiable and Let be a real n-tuple satisfying (15) and , be such that . Then, f satisfies (4) andholds for all , where . In particular, when , we get the inequality If in addition f is uniformly convex and Φ
is convex, then Proof. It was proved in [
14] that
. From Lemma 2, we get that
f satisfies (
4).
Let .
From Lemma 2, we know that for all Comparing c with , we must consider three cases.
Case 1.
In this case, for all , hence, according to Lemma 2,
Denoting
, we get that
and therefore
Case 2.
In this case,
for all
hence
Denoting
, we get that
and
Therefore
Case 3.
In this case, there exists such that
By Lemma 2, we get that
and
and
From these three cases, we get that
and, therefore, (
16) holds. When
, then (
17) holds.
It is given in this paper that if
f is uniformly convex, then, by Remark 1,
is increasing and
. If
is also convex, we only need to show that under our conditions
holds.
We show that (
19) holds in the case that
,
We use the identity
As
is nonnegative, convex and
it follows that for
and for
,
Therefore, (
19) is satisfied and, together with (
16), we get that (
18) holds.
Thus, the proof of Theorem 6 is complete. □
In order to show when Lemma 2 and Theorem 6 hold for -convex functions, we prove the following theorem:
Theorem 7. Let f be a Φ
-convex function on with an error function Φ
, f and Φ
both be continuously differentiable and . Then, f satisfies the inequalitywhere . Proof. It is easy to verify from (
21) that, because of the differentiability of
f and Φ, the inequality
holds. As
, then
and, therefore, we get from (
22) that
Hence,
The proof of the theorem is complete. □
Corollary 2. The results of Lemma 2 and (16) and (17) in Theorem 6 hold also when the function f is Φ
-convex in case that Theorem 7 holds and, therefore, Corollary 1 holds for and . 3. Jensen-Type Results on Generalized Uniformly Convex, -Convex and Superquadratic Functions
In this section, we deal with the same functions
as in
Section 2, which satisfy the following inequality:
We use an analogous technique to the proof of Theorem 1 and apply it on (
24) to obtain new results. Theorem 1 becomes a special case of the theorems proved in this section.
The cases included in (
24) are
a. Superquadratic functions
f where in (
24) we replace
with
f and
with
(see Definition 3);
b. Generalized uniformly convex functions
f where in (
24) we replace
with
(see Remark 3
c.
-convex functions
f where in (
24)
(see Corollary 1).
Theorem 8. Let satisfy (24) with . Suppose and is a nonnegative n-tuple with . Let . Then Proof. Applying the triangle inequality
to
satisfied by cases a., b. and c. above, we get
Setting
and
,
, we have
Now, multiplying (
27) by
, summing over
and dividing by
, we get
Because
, the inequalities (
28) and (
25) are the same and the proof of the theorem is complete. □
With the same technique as in Theorem 8 we get the other three theorems about functions that satisfy (
24), that is, superquadratic, generalized uniformly convex and
-convex functions.
Theorem 9. Let satisfy (24) with . Suppose and is a nonnegative n-tuple with . Let and . Thenhold. Similarly, we get an inequality that counterparts the Jensen inequality.
Theorem 10. Let satisfy (24) with . Suppose and is a nonnegative n-tuple with and . Then Proof. Inequality (
24) for
and
gives
Now, multiplying by
, summing over
and then dividing by
, we get
which is equivalent to (
29). □
Also, we get
Theorem 11. Let satisfy (24) with . Suppose and is a nonnegative n-tuple with and . Let . Then the inequalityholds. Comment. Theorems 8, 9, 10 and 11 hold in the special case when
f is a strongly convex function where
,
, as proved in ([Theorem 4] in [
7]) and quoted in Theorem 1.
5. >Examples: Relations Between Superquadracity and Some Other Types of Convex Functions
The following examples, in addition to Remark 2, emphasize the relations between superquadracity and other extensions of convexity. As shown in the examples, none of the three sets of convexity is completely included in another set. However, part of the uniformly convex functions are superquadratic (Remark 2) and some of the -convex functions are also superquadratic.
Example 1. Let , This function is superquadratic (see [11]) and negative on . Hence, f on is ϕ-convex where is defined on . Example 2. From Definitions 1 and 3 it is obvious that when a superquadratic function f is negative, the function f is ϕ-convex where .
Therefore, according to [Example 4.2] in [11], the functionsare superquadratic and negative. Hence, these functions are also ϕ-convex where . Example 3. As in Example 2, the same holds for the functions , , , which are ϕ-convex where .
Example 4. Let , , . Then, f is ϕ-convex where , and .
Example 5. satisfiesand is superquadratic (see [11]). Therefore, this function is superquadratic and because it is negative on it is also ϕ-convex for .
Example 6. The functionis superquadratic and where T satisfies and , . Therefore, on , is ϕ-convex where . Another set of functions f that are either -convex or strongly convex are
Example 7. Let , where φ is convex. It is easy to verify thatholds. Therefore, if it means that is ϕ-convex, and . If , it means that f is strongly convex and uniformly convex where .
In particular, the function where the function , is convex and satisfies . Therefore, f is ϕ-convex where .