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Several new inequalities on operator means of non-negative maps and Khatri–Rao products of positive definite matrices
*Tel.: +966 595884164 zeyad1968@yahoo.com (Zeyad Abdel Aziz Al-Zhour) zalzhour@ud.edu.sa (Zeyad Abdel Aziz Al-Zhour)
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Received: ,
Accepted: ,
This article was originally published by Elsevier and was migrated to Scientific Scholar after the change of Publisher.
Peer review under responsibility of King Saud University.
Available online 11 May 2013
Abstract
In this paper, we provide some interested operator inequalities related with non-negative linear maps by means of concavity and convexity structure, and also establish some new attractive inequalities for the Khatri–Rao products of two or more positive definite matrices. These results lead to inequalities for Hadamard product and Ando’s and α-power geometric means, as a special case.
Keywords
Matrix products
Operator means
Positive definite matrices
Introduction
The geometric mean of two or more positive (semi) definite matrices arises naturally in several areas such as in Electrical Network Theory, Statistics, Engineering, and many fields of pure and applied Mathematics; and it has several properties (equalities and inequalities) of the geometric mean of positive scalars (Ando and Hiai, 1998; Bhatia and Kittaneh, 2000; Xiao and Zhang, 2003). Let R+ be positive real numbers and for every x,y ∈ R+, then the function M:R+ × R+ → R+ is said to be a mean if the following properties hold:
Before starting on the geometric means of positive definite matrices, we need to study some important basic concepts and results on matrices. Let us first introduce the definitions of Kronecker, Hadamard, Tracy–Singh and Khatri–Rao products of matrices which are defined, respectively, by (Al-Zhour and Kilicman, 2006; Al-Zhour, 2012; Cao et al., 2002; Kilicman and Al-Zhour, 2005; Liu, 2002; Liu, 1999; Zhang, 1999).
Note that if A and B are non-partitioned matrices, then AΘB is reduced to A ⊗ B and A∗B∣ is reduced to A∘B(Liu, 1999).
Let A and B be Hermitian matrices, then the relation A > B means that A − B > 0 is a positive definite matrix and the relation A ⩾ B means A − B ⩾ 0 is a positive semi-definite matrix. If A > 0, then A1/2 is called the positive definite square root of A. Zhang (1999) showed that if A > 0 and B > 0, then the relation A ⩾ B implies A−1 ⩽ B−1,A2 ⩾ B2 and A1/2 ⩾ B1/2.
Here the symbol Mm,n stands to the set of all m × n matrices over the field M and when m = n, we write Mm instead of Mm,n. The symbols AT,A∗,A−1 stand to, respectively, the transpose, conjugate transpose and inverse of matrix A. The Symbols Hn and are, respectively, the space of n-square Hermitian and n-square positive definite matrices. The linear map φ from Hn to Hm is said to be positive if it transforms to . The positive linear map φ is said to be unital or normalized if it transforms the identity In to the identity matrix Im and monotone if A ⩽ B implies φ(A) ⩽ φ(B). For more details, see (Ando, 1979).
The following formula is very important for getting our results which is studied by many researchers (Al-Zhour and Kilicman, 2006; Al-Zhour, 2012; Cao et al., 2002; Liu, 2002; Liu, 1999; Zhang, 1999) :
In particular, if m(i) = n(i), then there exists
matrix Z of zeros and ones such that ZTZ = Ir, and
Ando et al. (2004) found other desirable properties that should be required for a reasonable geometric mean of three positive definite matrices.
Hu et al. (2005) presented several kinds of mixed means for three or more positive definite matrices, and proved some related mixed mean inequalities. Lim (2008) described the maximal and minimal positive definite solutions of the non-linear matrix equation X = T − BX−1B in terms of Ando’s geometric mean A#B.
Jung et al. (2009) established some new properties of α-power mean and used this mean in the solution of non-linear matrix equation Xn = f(X).
Recently, Lee et al. (2011) defined a family of weighted geometric means of n-tuples positive definite matrices and showed that these weighted geometric means satisfied multidimensional versions of all properties that one would expect of a two-variable weighted geometric mean. Fujii et al. (2010) presented the Cauchy–Schwraz and Holder inequalities involving geometric means of positive definite matrices. Kim et al. (2011) defined a new family of matrix means such as a resolvent mean which is defined of m positive definite matrices A = (A1,A2, …,Am) with weight vector ω = (w1,w2,…,wm) as follows: and this mean satisfies several desirable properties that are mentioned in (Kim et al., 2011). Note that for μ = ∞, the resolvent mean is the weighted arithmetic mean.
Ito et al. (2011) described some geometric properties of positive definite matrices cone with respect to the Thompson metric. More Recently, Lim (2012) introduced a new class of (metric) geometric means of positive definite matrices varying over Hermitian unitary matrices and gave some basic properties comparable to those geometric means. Finally, Bhatia and Grover (2012) presented the norm inequalities related to the geometric mean of positive definite matrices.
Here in this paper, we recover Ando’s geometric mean to the case of operator mean and derive some desirable properties which play a central role for establishing our results. Several inequalities related to operator means and Khatri–Rao products are established by applying concavity and convexity structures. Finally, the results lead to inequalities for Hadamard Product, and Ando’s and α-power geometric means, as a special case.
Further properties and connections
In this section, we study some interested properties and connections which are very important to obtain our results in next section.
Let Ai > 0 and Bi > 0(i = 1,2) be n × n compatible partitioned matrices. Then for any real number α,
(i) In order to see if this indeed is true, let and . Then Similarly, we can prove part (ii).
Let Ai > 0 (i = 1,2) be n × n compatible partitioned matrices. Then
Let Ai > 0(1 ⩽ i ⩽ k,k ⩾ 2) be n × n compatible partitioned matrices. Then
The proof is straightforward by using Theorem 2.2 and induction on k.
Let Ai> and Bi > 0(1 ⩽ i ⩽ k,k ⩾ 2) be n × n compatible partitioned matrices. Then
The proof follows immediately by induction on k.
Let Ai > 0 and Bi > 0(1 ⩽ i ⩽ k,k ⩾ 2) be n × n compatible partitioned matrices and let f(t) be a non-negative operator monotone function on [0,∞) such that
for any matrices Di (1 ⩽ i ⩽ k,k ⩾ 2). Then
The proof is straightforward by induction on k and Eqs. 1-13,1-15 and 1-16.
Several types of inequalities on operator means and Khatri–Rao products
For many years mathematicians have been interested in inequalities involving geometric means of positive semi-definite matrices (Ando, 1983; Ando, 1979; Ando et al., 2004; Hu et al., 2005; Furuichi et al., 2005; Furuta, 2006; Hernandez et al., 2001; Kilicman and Al-Zhour, 2005; Micic et al., 2000; Mond et al., 1996; Qi and Guo, 2003; Sagae and Tanabe, 1994; Satnoianu, 2002; Xiao and Zhang, 2003, Lim and Yamazaki, 2013; Fujii et al., 2010; Bhatia and Grover, 2012). In this section, we present many attractive inequalities involving geometric means and Khatri–Rao products of positive definite matrices based on the properties of convexity and concavity structures.
Let and 0 < λ < 1. Then the map φ from to Hm is said to be :
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Convex if
(3-1) -
Concave if the map (A1,…,Ak) ↦ − φ(A1,…,Ak) is convex
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Affine if
(3-2)
Let f be a real valued continuous function . Then
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f is Supermultiplicative if
(3-3) -
f is Submultiplicative if
(3-4)
Let φ be a normalized positive linear map and σbe an operator mean which has the representation function f which is not affine (f is an operator-monotone on (0,∞)). If A and B are positive definite matrices, then the following statements are equivalent:
It suffices to show that (ii) implies (i). Consider the map ψ defined by
let φ be a positive linear map and let A and B be positive definite matrices. Then
Consider the map ψ defined by By a nice technique in the proof of Lemma 3.3, set f(t) = tα for any real number α, we get 3-8.
The following results as in Theorems 3.5 and 3.6 are referring to Ando (1979).
Let . Then the map
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A ↦ Ap is concave if 0 < p ⩽ 1 and is convex if 1 ⩽ p ⩽ 2 or −1 ⩽ p < 0.
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A ↦ log[A] is concave, while the map A ↦ Alog[A] is convex.
Let φ be a normalized positive linear map from Hn to Hm and A > 0. Then
Let
be commutative compatible partitioned matrices. Then
The proof is straightforward by setting in Theorem 3.6.
Let
be commutative compatible partitioned matrices. Then
The proof follows immediately by applying 1-11 and 1-12 on Theorem 3.7.
Let Ai and
, (1 ⩽ i ⩽ k,k ⩾ 2) be compatible partitioned matrices. Let σ be an operator mean with supermultiplicative representing function f. Then
(i) Set
, then it follows from supermultiplicative of f that
Let φ be a positive linear map, then for any compatible partitioned matrices Ai and
It follows by replacing A by A1ΘB1 and B by A2 ΘB2 in Theorem 3.4.
Let φ be a positive linear map, then for any compatible partitioned matrices Ai and
The proof is straightforward by using Corollary 3.10 and induction on k.
Let Ai and
be compatible partitioned matrices. Then
Due to Corollary 3.10, Lemma 2.1 and using 1-11 and 1-12, then there is a normalized positive linear map φ such that
Let Ai and
be compatible partitioned matrices. Then
The proof is straightforward by using Corollary 3.12 and induction on k.
Let
be compatible partitioned matrices. Then for −∞ < p,q < ∞,
Due to Theorem 2.3, Theorem 3.4 and using 1-11 and 1-12, then there is a normalized positive linear map φ such that
Let A and
be compatible partitioned matrices such that A∗B = B∗A. Then
Since A∗B = B∗A, then Since A#B = B#A and from Corollary 3.12, then we have
Let Ai and
be compatible partitioned matrices and let φi be a concave map from
to
. Then the map
It suffices to show the convexity when λ = 1/2. Since the map under consideration is continuous, then
Let
be compatible partitioned matrices and let 0 ⩽ pi ⩽ 1 (1 ⩽ i ⩽ k). Then the map
The proof is straightforward by applying Theorems 3.16 and 3.5.
Let
be compatible partitioned matrices and let 0 ⩽ pi ⩽ 1 (1 ⩽ i ⩽ k) such that
. Then the map
The proof is by induction on k. If k = 1, then the result is true by Theorem 3.5. Suppose that Eq. 3-35 is true for the case k − 1. If pk = 1, then pi = 0 (1 ⩽ i ⩽ k − 1) and the map becomes , which is concave. If pk = 0, then the map becomes which is concave. Now suppose 0 < pk < 1. Then the map is concave by the induction assumption. Now with , the map is concave.
Let
be compatible partitioned matrices and let 1 ⩽ q ⩽ 2, 0 ⩽ pi ⩽ 1 (1 ⩽ i ⩽ k) such that
. Then the map
The map is concave, while the map is affine, and the Corollary 3.19 follows by using the following result (Ando, 1979): If φ and ψ are maps from to ; and if φ is concave and ψ is affine. Then the map A ↦ ψ(A)φ(A)−1ψ(A) is convex.
All results obtained in Section 3 is quite general. These results lead to inequalities involving the Hadamard and Kronecker product for non partitioned matrices Ai(i = 1,2, … k,k ⩾ 2); and Ando’s mean by setting α = 1/2, as a special case.
Conclusion
Several new attractive and interested inequalities related to operator means associated with non-negative linear maps and Khatri–Rao products of positive definite matrices are established by using means of concavity and convexity theorems. Some important special cases of these inequalities are also discussed. The satisfactory definition of geometric mean of positive definite matrices which satisfy properties from (i) to (viii) and properties from (1) to (12) that are mentioned, respectively, Kilicman and Al-Zhour (2005) and Kim et al. (2011), and many other desirable new properties still need further researches.
Acknowledgements
The author express his sincere thanks to Dr. Rizwan Irshad and referees for careful reading of the manuscript and several helpful suggestions. The author also gratefully acknowledges that this research was supported by Deanship of Scientific Research/University of Dammam Saudi Arabia.
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