Research

# Some inequalities for unitarily invariant norms of matrices

Shaoheng Wang, Limin Zou* and Youyi Jiang

Author Affiliations

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

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Journal of Inequalities and Applications 2011, 2011:10 doi:10.1186/1029-242X-2011-10

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

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

### 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 Mm,n be the space of m × n complex matrices and Mn = Mn,n. Let denote any unitarily invariant norm on Mn. So, for all AMn and for all unitary matrices U,VMn. For A = (aij)∈Mn, the Hilbert-Schmidt norm of A is defined by

where tr is the usual trace functional and s1(A) ≥ s2(A) ≥ ... ≥ sn-1(A) ≥ sn(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 [2] that if A,B,XMn such that A and B are positive semidefinite and if 0 ≤ r ≤ 1, then

(1.1)

Let A,B,XMn such that A and B are positive semidefinite. In [3], Zhan proved that

(1.2)

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,XMn such that A and B are positive semidefinite and suppose that

(1.3)

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

In [5], for positive semidefinite n × n matrices, the inequality

(1.4)

was shown to hold for every unitarily invariant norm. Meanwhile, Bhatia and Kittaneh [5] asked the following.

#### Question

Let A,BMn be positive semidefinite. Is it true that

, ?

The case n = 2 is known to be true [5]. (See also, [1, p. 133], [6, p. 2189-2190], [7, p. 198].)

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

### 2. Some inequalities for unitarily invariant norms

In this section, we first utilize the convexity of the function

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,XMn 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 x1 x x2, we have

(2.1)

#### Proof

Since ψ is a convex function on [a,b], for a x1 x x2 b, we have

This is equivalent to the inequality (2.1).□

#### Theorem 2.1

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

(2.2)

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

#### Proof

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

That is

(2.3)

It follows from (1.2) and (2.3) that

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

That is

(2.4)

It follows from (1.2) and (2.4) that

It is equivalent to the following inequality

This completes the proof.□

Now, we give a simple comparison between the upper bound in (1.2) and the upper bound in (2.2).

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

Let A,B,XMn 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.[8]

Let A,B,XMn such that A and B are positive semidefinite. If 0 ≤ v ≤ 1, then

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.

#### Lemma 2.3.[9]

Let A,B,XMn such that A and B are positive semidefinite. If 0 ≤ v ≤ 1, then

#### Theorem 2.3

Let A,B,XMn such that A and B are positive semidefinite. If 0 ≤ v ≤ 1, then

#### Proof

Let

So,

By Lemma 2.3, we have

That is,

Hence,

This completes the proof.□

Let A,B,XMn 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.[10]

Let A,B,XMn such that A and B are positive semidefinite. Then

Bhatia and Kittaneh proved in [5] that if A,BMn are positive semidefinite, then

(2.5)

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

#### Theorem 2.5

Let A,BMn be positive semidefinite. Then

#### Proof

Let

Then, by Theorem 2.4, we have

(2.6)

It follows form (2.5) and (2.6) that

That is,

This completes the proof.□

### Competing interests

The authors declare that they have no competing interests.

### Authors' contributions

SW and LZ designed and performed all the steps of proof in this research and also wrote the paper. YJ participated in the design of the study and suggest many good ideas that made this paper possible and helped to draft the first manuscript. All authors read and approved the final manuscript.

### 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).

### References

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