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# Some inequalities for unitarily invariant norms of matrices

Journal of Inequalities and Applications20112011:10

https://doi.org/10.1186/1029-242X-2011-10

• Received: 11 January 2011
• Accepted: 20 June 2011
• Published:

## Abstract

This article aims to discuss inequalities involving unitarily invariant norms. We obtain a refinement of the inequality shown by Zhan. Meanwhile, we give an improvement of the inequality presented by Bhatia and Kittaneh for the Hilbert-Schmidt norm.

### Mathematical Subject Classification

MSC (2010) 15A60; 47A30; 47B15

## Keywords

• Unitarily invariant norms
• Positive semidefinite matrices
• Convex function
• Inequality

## 1. Introduction

Let M m,n be the space of m × n complex matrices and M n = M n,n . Let denote any unitarily invariant norm on M n . So, for all AM n and for all unitary matrices U,VM n . For A = (a ij )M n , the Hilbert-Schmidt norm of A is defined by
where tr is the usual trace functional and s 1 (A) ≥ s 2 (A) ≥ ... ≥ s n-1 (A) ≥ s n (A) are the singular values of A, that is, the eigenvalues of the positive semidefinite matrix , arranged in decreasing order and repeated according to multiplicity. The Hilbert-Schmidt norm is in the class of Schatten norms. For 1 ≤ p < , the Schatten p-norm is defined as
For k = 1,...,n, the Ky Fan k-norm is defined as

It is known that these norms are unitarily invariant, and it is evident that each unitarily invariant norm is a symmetric guage function of singular values [1, p. 54-55].

Bhatia and Davis proved in  that if A,B,XM n such that A and B are positive semidefinite and if 0 ≤ r ≤ 1, then
Let A,B,XM n such that A and B are positive semidefinite. In , Zhan proved that
for any unitarily invariant norm and real numbers r,t satisfying 1 ≤ 2r ≤ 3,-2 < t ≤ 2. The case r = 1,t = 0 of this result is the well-known arithmetic-geometric mean inequality
Meanwhile, for r[0,1], Zhan pointed out that he can get another proof of the following well-known Heinz inequality

by the same method used in the proof of (1.2).

Let A,B,XM n such that A and B are positive semidefinite and suppose that

Then ψ is a convex function on [-1,1] and attains its minimum at v = 0 [4, p. 265].

was shown to hold for every unitarily invariant norm. Meanwhile, Bhatia and Kittaneh  asked the following.

### Question

Let A,BM n be positive semidefinite. Is it true that , ?

The case n = 2 is known to be true . (See also, [1, p. 133], [6, p. 2189-2190], [7, p. 198].)

Obviously, if A,BM n are positive semidefinite and AB = BA, then we have , .

## 2. Some inequalities for unitarily invariant norms

to obtain an inequality for unitarily invariant norms that leads to a refinement of the inequality (1.2). To do this, we need the following lemmas on convex functions.

### Lemma 2.1

Let A,B,XM n such that A and B are positive semidefinite. Then, for each unitarily invariant norm, the function

is convex on [0,2] and attains its minimum at r = 1.

### Proof

Replace v+1 by r in (1.3).□

### Lemma 2.2

Let ψ be a real valued convex function on an interval [a,b] which contains (x1,x2). Then for x1xx2, we have

### Proof

This is equivalent to the inequality (2.1).□

### Theorem 2.1

Let A,B,XM n such that A and B are positive semidefinite. If 1 ≤ 2r ≤3 and -2 <t ≤ 2, then

where r0 = min{r,2-r}.

### Proof

If , then by Lemma 2.1 and Lemma 2.2, we have
If , then by Lemma 2.1 and Lemma 2.2, we have

This completes the proof.□

Therefore, Theorem 2.1 is a refinement of the inequality (1.2).

Let A,B,XM n such that A and B are positive semidefinite. Then, for each unitarily invariant norm, the function

is a continuous convex function on [0,1] and attains its minimum at . See [4, p. 265]. Then, by the same method above, we have the following result.

### Theorem 2.2.

where r0 = min{v,1-v}. This is a refinement of the second inequality in (1.1).

Next, we will obtain an improvement of the inequality (1.4) for the Hilbert-Schmidt norm. To do this, we need the following lemma.

### Proof

This completes the proof.□

Let A,B,XM n such that A and B are positive semidefinite, for Hilbert-Schmidt norm, the following equality holds:

Taking in Theorem 2.3, and then we have the following result.

### Theorem 2.4.

Bhatia and Kittaneh proved in  that if A,BM n are positive semidefinite, then

Now, we give an improvement of the inequality (1.4) for the Hilbert-Schmidt norm.

### Proof

This completes the proof.□

## Declarations

### Acknowledgements

The authors wish to express their heartfelt thanks to the referees and Professor Vijay Gupta for their detailed and helpful suggestions for revising the manuscript. At the same time, we are grateful for the suggestions of Yang Peng. This research was supported by Natural Science Foundation Project of Chongqing Science and Technology Commission (No. CSTC, 2010BB0314), Natural Science Foundation of Chongqing Municipal Education Commission (No. KJ101108), and Scientific Research Project of Chongqing Three Gorges University (No. 10ZD-16).

## Authors’ Affiliations

(1)
School of Mathematics and Statistics, Chongqing Three Gorges University, Chongqing, 404000, People's Republic of China

## References 