Abstract
In this paper, we use the generalized version of convex functions, known as strongly convex functions, to derive improvements to the Jensen–Mercer inequality. We achieve these improvements through the newly discovered characterizations of strongly convex functions, along with some previously known results about strongly convex functions. We are also focused on important applications of the derived results in information theory, deducing estimates for -divergence, Kullback–Leibler divergence, Hellinger distance, Bhattacharya distance, Jeffreys distance, and Jensen–Shannon divergence. Additionally, we prove some applications to Mercer-type power means at the end.
Keywords:
convex and strongly convex functions; Jensen inequality; Jensen–Mercer inequality; strong f-divergences MSC:
26D15; 26D20; 94A15; 94A17
1. Introduction
The class of convex functions is widely used in numerous scientific fields. Several generalizations and variants of this class of functions have been introduced from different perspectives [1]. One important generalization of convex functions is the class of strongly convex functions. This class, initially developed by Polyak in 1966, has attracted the interest of numerous mathematicians due to its significant applications in various branches of mathematics, particularly in optimization theory [2].
Let I denote a real interval. It states that if
holds for all , and for some real number , then f is said to be a strongly convex function with modulus c. If is strongly convex, then we say that a function f is strongly concave. In other words, f is strongly concave if
holds for all and
Throughout this manuscript, SCF is the abbreviation for strongly convex function. It is evident that ; therefore, every SCF is also convex but the converse does not hold in general. Likewise, strong concavity implies ordinary concavity, but the reverse implication does not hold in general.
Example 1.
Let and the functions be defined by and Then, are SCFs with modulus , and respectively, and clearly, the function i is convex but not an SCF.
Remark 1.
If are three points in I such that then (1) is equivalent to
Compared with convex functions, the SCFs possess stronger versions of the analogous properties. Some of their useful characterizations are given in the following lemmas (see [3] (p. 268) as well as [4,5] and the references given therein).
Lemma 1.
If is a function, then the function is convex for some if and only if f is SCF with modulus c.
The second lemma is a characterization for twice differentiable functions.
Lemma 2.
Let be a function such exists. Then, f is SCF with modulus if and only if
While SCFs have been extensively researched and appear frequently in the literature, strongly concave functions are poorly represented, although their application can be widely used. We will give special emphasis to such a class of functions by stating our main results that also include strongly concave functions.
If we interpret the previous characterizations in terms of strongly concave functions, we can conclude the following. A function f is strongly concave with modulus if the function is concave. Also, a twice differentiable function f is strongly concave with modulus if Using this fact, considering specific strongly concave functions in the last section, we derive new estimates for Mercer-type means.
Below, we mention several variants of the well-known inequalities that are valid for SCFs. The first of them is Jensen’s inequality (see [5]).
Theorem 1.
Let be an SCF with modulus . Let and be such that and Then
Remark 2.
Since is non-negative, from (4), we deduce Jensen’s inequality for convex functions as ([6])
and hence inequality (4) provides a better upper bound for than (5).
By taking as the difference between right and left sides of (4), we deduce Jensen’s functional in the form
Note that the non-negativity of Jensen’s functional (6) is a consequence of Jensen’s inequality (4).
If f is strongly concave with modulus c, then
In that case, Jensen’s functional has the form
and it holds
The following variant of Jensen’s inequality, known as the Jensen–Mercer inequality, is presented in [7].
Theorem 2.
Let be an SCF with modulus Then
where such that , and is a non-negative n-tuple with and .
Remark 3.
Specifically, for inequality (9) becomes
i.e., we get the Jensen–Mercer inequality for convex function proved in [8].
In [9], the authors applied the Jensen-Mercer inequality and derived several Hermite-Hadamard type inequalities for stochastic fractional integrals. Some properties of the Jensen-Mercer functional have been presented in [10]. This inequality has also been proved for the class of uniformly convex functions [11]. For more results about the Jensen–Mercer inequality, see for example [12,13,14,15,16].
Now, we quote recently obtained interpolating Jensen-type inequalities for SCFs [4] that present an improvement in results from [17], which has been pivotal in numerous recent investigations.
Theorem 3.
Let be an SCF with modulus Let and be a non-negative n-tuple, be a positive n-tuple with and Then
If, in addition, and then (11) becomes
Important and interesting applications of the class SCFs are evidenced by numerous papers published recently. In [18], the authors proved several inequalities for SCFs related to their first-order divided differences and as applications derived from related majorization-type inequalities, addressing majorized tuples and applying conditions for majorization inequalities, as described by Maligranda et al. Interesting examples illustrate and support the obtained results. Related integral results are presented in [19]. Some companion inequalities to Jensen’s inequality, specifically Slater-type inequalities for SCFs, are given in [20]. More advanced and enhanced forms of SCFs have been presented in the literature. The concept of coordinate SCFs has been introduced in [21], and several inequalities have been derived for this new class of functions. In [22], a thorough study conducted and presented some interesting background and history of the generalized forms of SCFs. The SCFs play a certain role in quantum calculus, and numerous results can be found for quantum integral using the class SCFs. In [23], several Trapezoidal-type inequalities are given through integral identities obtained by using Hölder’s and power mean inequalities. The main findings are verified through graphical illustrations. As an interesting reference, we mention the paper [24] that deals with applications of SCFs with problems for differential equations. Using the support line inequality a converse of the Jensen’s inequality has been obtained in [25]. Some generalized and advanced applications of convexity are given in [26,27]. Several integral inequalities for the class of extended SCFs are established in [28,29].
In this paper, we highlight more accurate characterizations for SCFs, which can be derived as easy consequences of recently obtained result (12). Those characterizations enable us to obtain new improvements to the Jensen–Mercer-type inequality. To specify, we obtain improvements of the Jensen–Mercer inequality (10) and, moreover, the improvement of (9). We use the obtained results to derive new estimates for strong f-divergences, the concept of Csiszár f-divergences for SCFs introduced in [30]. As outcomes, we deduce estimates for particular cases as -divergence, Kullback–Leibler divergence, Hellinger distance, Bhattacharya distance, Jeffreys distance and Jensen–Shannon divergence. As applications of our main results, we also derive new estimates for Shannon entropy. In the last section, we consider Mercer-type power means. Using improved Mercer-type inequalities, we establish new Mercer-type means inequalities that improve Mercer’sresult from [31]. The application of newly obtained inequalities for strongly concave functions is particularly interesting in our proof.
2. Main Results
At the beginning of this section, we extract an important consequence from Theorem 3, which will play a key role in proving our main results.
Remark 4.
Note that the second inequality in (12) improves Jensen’s inequality (4) for SCFs. Precisely stated, for an SCF with modulus a non-negative n-tuple with and by (12) we have
where
Choosing for and from (13) we get
To demonstrate the main results, we need the following lemma.
Lemma 3.
Let and such that . Then, for every , an SCF with modulus we have
where
If f is strongly concave, then
where
Proof.
For every there exists a unique such that
Since
then the series of inequalities (17) hold.
The last statement is a consequence of the fact that if f is strongly concave, then is an SCF. □
Remark 5.
Lemma 3 presents an improvement of Theorem 2 for
Now, we present generalization and improvement of the Jensen–Mercer inequality (10) as well as an improvement of inequality (9).
Theorem 4.
Let and such that Let be a non-negative n-tuple with and and be defined by (18), (20), respectively. Then, for every an SCF with modulus we have
If f is strongly concave, then
Proof.
Applying (13), we get
Using Lemma 3, we have
The last statement is a consequence of the fact that if f is strongly concave, then is an SCF. □
Theorem 5.
Let and be a non-negative n-tuple with and Let and be defined by (18) and (20), respectively. Then, for every an SCF with modulus we have
If f is strongly concave, then
3. Applications to Strong -Divergences and the Shannon Entropy
Let be the set of all complete finite discrete probability distributions. The restriction to positive distributions is only for convenience. If we take for some in the following results, we need to interpret undefined expressions as and
I. Csiszár [32] introduced an important class of statistical divergences by means of convex functions.
Definition 1.
Let be a convex function and . The Csiszár f-divergence is defined as
It has deep and fruitful applications in various branches of science (see, e.g., [33,34] with the references given therein) and is involved in the following Csiszár–Körner inequality (see [35]).
Theorem 6.
Let . If is a convex function, then
Remark 6.
If f is normalized, i.e., then it follows from (32) that
Two distributions and are very similar if is very close to zero.
Recently, in [30], a new concept of f-divergences was introduced.
Definition 2.
Let be an SCF with modulus and Strong f-divergence is defined as
Accordingly, in [30], the following improvement of the Csiszár–Körner inequality for strong f-divergences was obtained, as follows.
Theorem 7.
Let . If is an SCF with modulus then
where
Remark 7.
Here, denotes strong chi-squared distance obtained for SCF with modulus
Additionally, if , then from (35), we have
In the sequel, we make use of the results from the previous sections in order to prove new estimates for strong f-divergences .
Corollary 1.
Let and such that . Let us denote and be defined by (18). Then, for every an SCF with modulus we have
Corollary 2.
Let with and be defined by (18). Then, for every an SCF with modulus we have
By utilizing earlier corollaries with the relevant generating SCF f, we can obtain new estimates for certain well-known divergences, which are specific cases of strong f-divergence (34). Here we analyze some of the most widely used divergences.
Example 2.
Strong Kullback–Leibler divergence of is defined by
where the generating function is for Fix . Since we have on and is a SCF with modulus
Example 3.
Strong squared Hellinger divergence of is defined by
where the generating function is for Fix . Since we have on and is an SCF with modulus
Example 4.
Strong Bhattacharya distance of is defined by
where the generating function is for Fix . Since we have on and is an SCF with modulus
Example 5.
Strong Jeffreys distance of is defined by
where the generating function is for Fix . Since we have on and is an SCF with modulus
Example 6.
Strong Jensen–Shannon divergence of is defined by
where the generating function is for Fix . Since we have on and is a SCF with modulus
In the sequel, we consider Shannon’s entropy [36], defined in terms of its probability distribution for a random variable X as
It quantifies the unevenness in and satisfies the inequality
We derive new estimates for Shannon’s entropy using results from the previous sections.
Corollary 3.
Let and such that . Then
where we denote and
Corollary 4.
Let and be such that Let be defined as in (42). Then
4. Estimates for Mercer-Type Means
Let us recall that for and a non-negative n-tuple such that the weighted power mean of order is defined by
By slightly modifying (44), we get Mercer type power mean of order as follows
(see [31]). In the same paper, Mercer proved that for ,, it holds:
Applying Theorem 4 to some specified strongly convex and strongly concave functions, we get an improvement of the previous Mercer’s result. More precisely, we derive a series of inequalities that refine (45).
Theorem 8.
Let and such that Let be non-negative n-tuple with and
Let such that Then, the following cases hold.
CASE 1: If then
where
and
CASE 2: If then inequalities (46) hold with replaced by
CASE 3: If then inequalities (46) hold with replaced by .
CASE 4: If then
where and
CASE 5: If , then
where and
Proof.
CASE 1: Let Then
By definition of we have
Then also
We apply inequality (21), to an SCF with modulus and replacing with respectively, we get
where for , we have and for
We can write equivalently
Since rising to the power we get
which we need to prove.
CASE 2: Let . Then
In this case, we have
Applying (22) to the function strongly concave with modulus and replacing with respectively, we get
where
Since , raising to the power we get
CASE 3: Let . Then
In this case, we have
Applying (21) to an SCF with and replacing with respectively, we get
where
Since , raising to the power we again get (50).
CASE 4: Let .
In this case
Applying (21) to the SCF with and replacing with respectively, we get
where i.e., we get
which is equivalent to (48).
CASE 5: Let .
In this case
Since , raising to the power we get (49).
This ends the proof. □
Remark 8.
Analogously, we can apply Theorem 5 for new estimates, but we leave those results to the reader to derive them.
5. Conclusions
In the literature, numerous results are presented for Jensen’s and the Jensen–Mercer-type inequalities pertaining convex functions, focusing on their generalizations, refinements, and improvements using different approaches and tools. After the appearance of Jensen’s and Jensen–Mercer inequalities for the class of SCFs, a natural question arises: is it possible to provide related results for these inequalities for the class of SCFs? Most of the results are not straightforward to obtain such related results. In this manuscript, we derived improvements of the Jensen–Mercer inequality using some earlier results of the class of SCFs. Also, for the desired inequalities, we elaborated interesting properties of the SCFs. Our main results also hold for strongly concave functions, which are poorly represented in the literature, although their application can be very useful. By observing such functions and using their characterizations, we derived interesting results involving Mercer-type means. We also gave applications of the main inequalities in information theory.
Author Contributions
Conceptualization, M.A.K., S.I.B. and H.A.M.; Validation, S.I.B. and H.A.M.; Writing—original draft, S.I.B.; Writing—review & editing, M.A.K. and H.A.M.; Supervision, M.A.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Data Availability Statement
No new data were created or analyzed in this study.
Acknowledgments
The authors are very much grateful to the anonymous reviewers for their comments and suggestions that improved the manuscript. The authors extend their appreciation to King Saud University for funding this work through Researchers Supporting Project number (RSPD2024R1006), King Saud University, Riyadh, Saudi Arabia. This research is partially supported through KK.01.1.1.02.0027, a project co-financed by the Croatian Government and the European Union through the European Regional Development Fund—the Competitiveness and Cohesion Operational Programme.
Conflicts of Interest
The authors declare no conflicts of interest.
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