# Schur-Convexity for a Class of Symmetric Functions and Its Applications

- Wei-Feng Xia
^{1}and - Yu-Ming Chu
^{2}Email author

**2009**:493759

**DOI: **10.1155/2009/493759

© W.-F. Xia and Y.-M. Chu. 2009

**Received: **16 May 2009

**Accepted: **14 September 2009

**Published: **12 October 2009

## Abstract

## 1. Introduction

Throughout this paper we use to denote the dimensional Euclidean space over the field of real numbers and . In particular, we use to denote

For the sake of convenience, we use the following notation system.

The notion of Schur convexity was first introduced by Schur in 1923 [1]. It has many important applications in analytic inequalities [2–7], combinatorial optimization [8], isoperimetric problem for polytopes [9], linear regression [10], graphs and matrices [11], gamma and digamma functions [12], reliability and availability [13], and other related fields. The following definition for Schur convex or concave can be found in [1, 3, 7] and the references therein.

Definition 1.1.

where denotes the th largest component in . is called Schur concave if is Schur convex.

The notation of multiplicative convexity was first introduced by Montel [14]. The Schur multiplicative convexity was investigated by Niculescu [15], Guan [7], and Chu et al.[16].

However is called Schur multiplicatively concave if is Schur multiplicatively convex.

In paper [17], Anderson et al. discussed an attractive class of inequalities, which arise from the notion of harmonic convex functions. Here, we introduce the notion of Schur harmonic convexity.

Definition 1.3.

for each pair of tuples and on , such that . is called a Schur harmonic concave function on if (1.6) is reversed.

The main purpose of this paper is to discuss the Schur convexity, Schur multiplicative convexity, and Schur harmonic convexity of the following symmetric function:

where , , and are positive integers. As applications, some inequalities are established by use of the theory of majorization.

## 2. Lemmas

In order to establish our main results we need several lemmas, which we present in this section.

The following lemma is so-called Schur's condition which is very useful for determining whether or not a given function is Schur convex or Schur concave.

for all and . Also is Schur concave if and only if (2.1) is reversed for all and . Here, is a symmetric function in meaning that for any and any permutation matrix .

Remark 2.2.

for all . Also is Schur multiplicatively concave if and only if (2.3) is reversed.

Lemma 2.4.

for all . Also is Schur harmonic concave if and only if (2.4) is reversed.

Proof.

From Definitions 1.1 and 1.3, we clearly see the fact that is Schur harmonic convex if and only if is Schur concave.

This fact, Lemma 2.1 and Remark 2.2 together with elementary calculation imply that Lemma 2.4 is true.

Lemma 2.6 (see [6]).

Lemma 2.7 (see [19]).

## 3. Main Results

Theorem 3.1.

For the symmetric function is Schur concave in .

Proof.

The proof is divided into four cases.

Case 1.

Case 2.

Case 3.

Case 4.

Therefore, (3.1) follows from Cases 1–4 and the proof of Theorem 3.1 is completed.

For the Schur multiplicative convexity or concavity of , we have the following theorem

Theorem 3.2.

It holds that is Schur multiplicatively concave in

Proof.

for all and Then proof is divided into four cases.

Case 1.

Case 2.

Case 3.

Case 4.

Therefore, Theorem 3.2 follows from (3.10) and Cases 1–4.

Remark 3.3.

From (3.11) and (3.12) we know that is Schur multiplicatively concave in and is Schur multiplicatively convex in .

Theorem 3.4.

For the symmetric function is Schur harmonic convex in .

Proof.

The proof is divided into four cases.

Case 1.

Case 2.

Case 3.

Case 4.

Therefore, (3.15) follows from Cases 1–4 and the proof of Theorem 3.4 is completed.

## 4. Applications

In this section, we establish some inequalities by use of Theorems 3.1, 3.2 and 3.4 and the theory of majorization.

Theorem 4.1.

Proof.

Theorem 4.2.

Proof.

Therefore, Theorem 4.2 follows from (4.3) and Theorem 3.1 together with (1.7), and Theorem 4.2 follows from (4.3) and Theorem 3.4 together with (1.7).

If we take in Theorem 4.2 and in Theorem 4.2, respectively, then we have the following corollary.

Corollary 4.3.

Remark 4.4.

Theorem 4.5.

Proof.

Therefore, Theorem 4.5 follows from (4.7) and Theorem 3.4 together with (1.7), and Theorem 4.5 follows from (4.7) and Theorem 3.1 together with (1.7).

If we take and in Theorem 4.5, respectively, then we get the following corollary.

Corollary 4.6.

Theorem 4.7.

Proof.

Therefore, Theorem 4.7 follows from (4.10), Theorem 3.2, and (1.7).

If we take and in Theorem 4.7, respectively, then we get the following corollary.

Corollary 4.8.

Remark 4.9.

From Remark 3.3 and (4.10) together with (1.7) we clearly see that inequality in Corollary 4.8 is reversed for and inequality in Corollary 4.8 is true for .

Theorem 4.10.

Proof.

Theorem 4.11.

Proof.

Therefore, Theorem 4.11 follows from (4.15), Theorems 3.1, 3.4, and (1.7).

Remark 4.12.

Mitrinovi et al. [21, pages 473–479] established a series of inequalities for and , . Obvious, our inequalities in Theorem 4.11 are different from theirs.

Theorem 4.13.

Proof.

Therefore, Theorem 4.13 and follows from (4.17), Theorems 3.1, 3.4, and (1.7).

Therefore, Theorem 4.13 follows from (4.18), Theorem 3.2, and (1.7).

Therefore, Theorem 4.13 follows from (4.19), and Theorem 3.2 together with (1.7).

Therefore, Theorem 4.13 follows from (4.21), Theorem 3.2, and (1.7).

## Declarations

### Acknowledgment

This work was supported by the National Science Foundation of China (no. 60850005) and the Natural Science Foundation of Zhejiang Province (no. D7080080, Y607128, Y7080185).

## Authors’ Affiliations

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