# On Some Improvements of the Jensen Inequality with Some Applications

- M. Adil Khan
^{1}Email author, - M. Anwar
^{1}, - J. Jakšetić
^{2}and - J. Pečarić
^{1, 3}

**2009**:323615

https://doi.org/10.1155/2009/323615

© M. Adil Khan et al. 2009

**Received: **23 April 2009

**Accepted: **10 August 2009

**Published: **27 August 2009

## Abstract

An improvement of the Jensen inequality for convex and monotone function is given as well as various applications for mean. Similar results for related inequalities of the Jensen type are also obtained. Also some applications of the Cauchy mean and the Jensen inequality are discussed.

## Keywords

## 1. Introduction

The well-known Jensen's inequality for convex function is given as follows.

Theorem 1.1.

is valid for any convex function . In the case when is strictly convex on one has equality in (1.1) if and only if is constant almost everywhere on .

Here and in the whole paper we suppose that all integrals exist. By considering the difference of (1.1) for functional in [1] Anwar and Pečarić proved an interesting result of log-convexity. We can define this result for integrals as follows.

Theorem 1.2.

The following improvement of (1.1) was obtained in [2].

Theorem 1.3.

If is concave, then left-hand side of (1.4) should be .

In this paper, we give another proof and extension of Theorem 1.2 as well as improvements of Theorem 1.3 for monotone convex function with some applications. Also we give applications of the Jensen inequality for divergence measures in information theory and related Cauchy means.

## 2. Another Proof and Extension of Theorem 1.2

In fact, Theorem 1.2 for and was first of all initiated by Simić in [3].

In [1] we have given correct proof by using extension of (2.1), so that it is defined on .

Moreover, we can give another proof so that we use only (2.1) but without using convexity as in [3].

Proof of Theorem 1.2.

Consider the function defined, as in [3], by (2.1).

Since we have and , we also have that (2.4) is valid for . So is log-convex function in the Jensen sense on .

Let us note that it was used in [4] to get corresponding Cauchy's means. Moreover, we can extend the above result.

Theorem 2.1.

where define the determinant of order with elements and .

Proof.

Therefore, ( denote the matrix with elements ) is nonnegative semi definite and (2.6) is valid for . Moreover, since we have continuity of for all , (2.6) is valid for all .

Remark 2.2.

## 3. Improvements of the Jensen Inequality for Monotone Convex Function

In this section and in the following section, we denote and .

Theorem 3.1.

for monotone convex function . If is monotone concave, then the left-hand side of (3.1) should be .

Proof.

Now from (1.4), (3.3), and (3.4) we get (3.1).

The case when is nonincreasing can be treated in a similar way.

Of course a discrete inequality is a simple consequence of Theorem 3.1.

Theorem 3.2.

The following improvement of the Hermite-Hadamard inequality is valid [5].

Corollary 3.3.

Let be a differentiable convex. Then

holds. If is differentiable concave, then the left-hand side of (3.7) should be

holds. If is differentiable and monotone concave then the left-hand side of (3.8) should be .

## 4. Improvements of the Levinson Inequality

Theorem 4.1.

- (i)
As for -convex function the function is convex on , so by setting in the discrete case of [2, Theorem 2], we get (4.1).

- (ii)

Ky Fan Inequality

with equality sign if and only if .

Inequality (4.5) has evoked the interest of several mathematicians and in numerous articles new proofs, extensions, refinements and various related results have been published [7].

The following improvement of Ky Fan inequality is valid [2].

Corollary 4.2.

- (i)
- (ii)

From (4.9) we get (4.7).

## 5. On Some Inequalities for Csiszár Divergence Measures

Let be a measure space satisfying and a -finite measure on with values in . Let be the set of all probability measures on the measurable space which are absolutely continuous with respect to . For , let and denote the Radon-Nikodym derivatives of and with respect to respectively.

Csiszár introduced the concept of -divergence for a convex function, that is continuous at 0 as follows (cf. [8], see also [9]).

Definition 5.1.

is called the -divergence of the probability distributions and .

- (i)

- (v)

where and are positive integrable functions with

There are various other divergences in Information Theory and statistics such as Arimoto-type divergences, Matushita's divergence, Puri-Vincze divergences (cf. [12–14]) used in various problems in Information Theory and statistics. An application of Theorem 1.1 is the following result given by Csiszár and Körner (cf. [15]).

Theorem 5.2.

Proof.

By substituting and in Theorem 1.1 we get (5.13).

Similar consequence of Theorems 1.2 and 2.1 in information theory for divergence measures discussed above is the following result.

Theorem 5.3.

where define the determinant of order with elements and

As we said in [4] we define new means of the Cauchy type, here we define an application of these means for divergence measures in the following definition.

Definition 5.4.

Theorem 5.5.

Proof.

Also for we consider limiting case and the result follows from continuity of .

An application of Theorem 1.3 in divergence measure is the following result given in [16].

Theorem 5.6.

Proof.

By substituting and in Theorem 1.3, we get (5.20).

Theorem 5.7.

Proof.

By substituting and in Theorem 3.1(ii) we get (5.22).

Corollary 5.8.

Proof.

The proof follows by setting in Theorem 5.7.

Corollary 5.9.

Let be as given in (5.11), then

Proof.

The proof follows be setting to be as given in (5.11), in Theorem 3.1.

## Declarations

### Acknowledgments

This research work is funded by the Higher Education Commission Pakistan. The research of the fourth author is supported by the Croatian Ministry of Science, Education and Sports under the Research Grants 117-1170889-0888.

## Authors’ Affiliations

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