Improvements on Geometric Means Related to the Tracy-Singh Products of Positive Matrices

Size: px
Start display at page:

Download "Improvements on Geometric Means Related to the Tracy-Singh Products of Positive Matrices"

Transcription

1 MATEMATIKA, 2005, Volume 21, Number 2, pp c Department of Mathematics, UTM. Improvements on Geometric Means Related to the Tracy-Singh Products of Positive Matrices 1 Adem Kilicman & 2 Zeyad Al Zhour Department of Mathematics and Institute for Mathematical Research, University Putra Malaysia UPM, 43400, Serdang, Selangor, Malaysia 1 ailic@fsas.upm.edu.my / 2 zeyad1968@yahoo.com Abstract In this paper, a family of geometric means for positive matrices is studied; we discuss possible definitions of the geometric means of positive matrices, and some counter examples are given. It is still an open problem to find a completely satisfactory definition. Other problems are how to define the geometric, arithmetic, harmonic, power and operator means of finitely many positive matrices. We generalize these means of two positive matrices to arrive the definitions of the weighted means of positive matrices. We recover and develop the relationship between the Ando s geometric mean and the Kronecer product to the Tracy-Singh product and other means. Some new attractive inequalities for the Tracy-Singh product, Khatri-Rao product and geometric means of several positive matrices are established. The results lead to the case of Kronecer and Hadamard products of any finite numbers of matrices. Keywords Tracy-Singh product, Khatri-Rao product, Kronecer product, Hadamard product, Positive definite matrix, Geometric means, weighted geometric means. 1 Introduction and Preliminary Results Consider matrices A [a ij ] and C [c ij ] of order m n and B [b l ] of order p q. Let A and B be partitioned as A [A ij ] and B [B l ] 1 i t, 1 j c, where A ij is an t c t c m i n j matrix and B l is a p q l matrix m m i, n n j, p p i, q q j. Let A B, A B, AΘB and A B be the Kronecer, Hadamard, Tracy-Singh and Khatri- Rao products, respectively. The definitions of the mentioned four matrix products are given by Liu in [8, 9] as A B a ij B ij, A C a ij c ij ij C A, A B A ij B ij ij and AΘB A ij ΘB ij A ij B l l ij.additionally, Liu [8] shows that the Khatri-Rao product can be viewed as a generalized Hadamard product and the Tracy-Singh product as a generalized Kronecer product, i.e., for non-partitioned matrices A and B, their AΘB is A B and their A B is A B. The Khatri-Rao and Tracy-Singh products of matrices A i 1 i, 2 will be denoted by respectively. A i A 1 A 2... A and ΘA i A 1 ΘA 2 Θ...ΘA,

2 50 Adem Kilicman & Zeyad Al Zhour For Hermitian matrices A and B, the relation A > B means that A B > 0 is a positive definite and the relation A B means A B 0 is a positive semi-definite. Given a positive definite matrix A, its positive definite square root is denoted by. Notice that for positive definite matrices A and B, the relation A B implies B 1/2, A 2 B 2 and B 1 A 1. Let us introduce some notations. The notation M m,n M m,n + is the set of all m n positive definite matrices over M and when m n, we write M m M m + instead of M m,n M m,n +. The notations A T, A, A 1 are the transpose, conjugate transpose and inverse of matrix A, respectively. The Khatri - Rao and Tracy - Singh products are related by the following relation [5,14]: A i Z1 T ΘA i Z Here, A i M mi,ni 1 i, 2 are compatibly partitioned matrices, t c t m mi, n ni, r m j i, s n j i, mi m j i, ni t n j i. Z 1 and Z 2 are real matrices of order m r and n s, respectively such that Z T 1 Z I 1, Z T 2 Z I 2, where I 1 and I 2 are identity matrices of order r r and s s, respectively. In particular, if mi ni, we then have A i Z T ΘA i Z. 1-2 We shall mae frequent use the following properties of the Tracy-Singh and Khatri- Rao products and their proofs are straightforward by using induction on. Let A i and B i 1 i, 2 be compatible partitioned matrices, we have a ΘA i ΘB i ΘA i B i. 1-3 b ΘA i r c ΘA i ΘA i and A i A i. 1-4 ΘA r i if A i M m + i 1 i, 2 and r is any real number. 1-5 d A i ΘB i A i Θ B i. 1-6 In this paper, the results are established in three ways. First, we discuss possible definitions of the geometric means of two matrices and weighted means of positive matrices, provided some counter examples are given. It is still an open problem to find a completely

3 Improvements on Geometric Means Related to the Tracy-Singh Products 51 satisfactory definition. Second, we discover some interesting nenequalities involving Tracy-Singh product, Khatri-Rao product and geometric means of several positive matrices. Finally, the results lead to the case of Kronecer and Hadamard products of any finite numbers of positive matrices. 2 Geometric Means of Two Positive Matrices If A and B are arbitrary n n matrices, then the arithmetic mean is defined by as A B 1 A + B Similarly, when A and B are positive n n matrices, their harmonic mean can be defined A!B } A 1 + B However, it is not at all obvious how to define the geometric mean of positive matrices. In what follows, if A M n + and is any real number, then A will denote its unique positive semidefinite th power. Ideally, the geometric mean AB of two positive matrices and should satisfy the following properties: i AB AB 1/2 when A and B commute 2-3 ii AB 0 Positive property 2-4 iii AB BA Symmetry property iv A!B AB A B Arithmetic-Geometric-Harmonic inequality 2-5 The obvious candidates for AB are not satisfactory. We now explain the problems which arise with some of these candidates in the case that A and B are positive n n matrices. Candidate 1 : A 1 B AB 1/2 2-6 This has the drawbac need not a positive semi definite square root unless AB BA. To see this, observe that if A > 0, B > 0 and AB 0, then AB AB B A BA So, if AB BA, we must conclude that AB 0 and so it is impossible for any square root of AB to be positive semi definite. Candidate 2 : A 2 B B 1/2 2-7 Again, there are problems with the positivity property. Notice that if A > 0, B > 0 and B 1/2 B 1/2, then AB BA. Hence if AB BA, we must conclude that B 1/2 B 1/2, and so, as above, A 2 B B 1/2. Candidate 3 : A 3 B A 1/4 B 1/2 A 1/4 2-8

4 52 Adem Kilicman & Zeyad Al Zhour Certainly A 3 B satisfies property i and by design, also satisfies the positivity property ii. However, A 3 B need not be symmetric. To see this, consider [ ] [ ] A, B For these matrices, mathematica shows that: [ A B ], B 3 A [ and these are clearly different. In 1979, Ando [2] gave an apparently complicated variant of candidate3, which turns out to be very useful and satisfy all properties i-iv. When A and B are positive n n matrices, Ando s geometric mean is defined by AB D 1/2 with D B. 2-9 It is clear to show that Ando s geometric mean enjoy the following properties: aaa A. ba p A q A p+q/2, for all < p, q < caba 1 AB B dab 1 AB A e ABB 1/2 is a unitary matrix Now we use the fact that if X and Y are positive n n matrices, then if and only if X 2 Y 2. This follows from the observation that a positive n n matrix has a unique positive square root. Thus AB BA is equivalent to B 1/2 B 1/2 B 1/2 2 B 1/2 AB 1/ To see that this is indeed true, expand out the square B 1/2 B 1/2 B 1/2 2 ] B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 B 1 B 1/2 B 1/2 B 1/2 B 1/2 B 1/2 AB 1/2. Ando s geometric mean also satisfies the arithmetic-geometric-harmonic mean inequality. The geometric-arithmetic means inequality can be established by taing inverses. Let A > 0 and B > 0 be n n matrices and write D B. It is clear that D > 0. Now AB A B B} 1/2 1 A + B 2 B} 1/2 1 2 A1/2 I + B B} 1/2 1 2 I + A 1/2 B D 1/2 1 I + D

5 Improvements on Geometric Means Related to the Tracy-Singh Products 53 This last inequality is the arithmetic-geometric mean inequality for the positive definite matrix. Since A > 0, there are a unitary matrix U and a diagonal matrix S diag[s 1,..., s n ] with s i > 0 1 i n satisfying A U SU. Thus D 1/2 1 2 I + D is equivalent to s 1/2 i s i, for all 1 i n and this is obviously true. Ando [2] established further nice properties of his geometric mean of two positive n n matrices vc ABC C ACC BC, for all C M n. Distributive property 2-15 via 1 B 1 A 2 B 2 A 1 A 2 B 1 B 2. Mixed property 2-16 viiab 1 A 1 B 1. Inverse property 2-17 We examine these in the next section, where we will give some extensions and generalizations. Now, we have A BA!B AB 2-18 To see that this is indeed true: A BA!B I DI!D} 1/2I + D 1/2 1/2I + D 1 1/2 } D 1/2 AB. Ando used his definition to study monotone functions of matrices and obtained many nice results [2]. In spite of all this, Ando s definition is not completely satisfactory. It fails to satisfy one very desirable property: viii AB 2 is similar to AB. Eigenvalue property 2-19 [ ] [ ] To see why this is a problem, consider A and B. Then, computing with Mathematica, we find AB [ ] [ 4 2, and AB 1 1 Here, the eigenvalues of AB are and , while the eigenvalues of AB 2 are and Hence AB 2 is not similar to AB, since similar matrices must have the same eigenvalues. In 1997, Fiedler and Pta [7] succeeded in finding a definition of geometric mean that does satisfy the eigenvalue property. They used A B A 1 B 1/2 AA 1 B 1/2, 2-20 for positive matrices A and B. Unfortunately, this definition fails the arithmetic-geometric -harmonic inequality. It satisfy properties from i to iii and from v to viii. We give a new direct proof of the eigenvalue property, assuming that A B B A, for positive matrices A and B. Observe that A B A 1 B 1/2 AA 1 B 1/2 B 1 BB ]

6 54 Adem Kilicman & Zeyad Al Zhour Thus, by property iii of Ando s geometric mean, we have A B 2 A BB A A 1 B 1/2 AA 1 B 1/2 B 1 BB 1 A 1 B 1/2 A B 1 1 B 1 BB 1 A 1 B 1/2 ABB 1 B 1 1 ABB 1 Q 1 ABQ Q B 1. Hence, A B 2 is not similar to AB. In 1998, Ando and Hiai [4] succeeded in generalizing Ando s geometric mean to the power mean that satisfy properties from i to vii. They used A B D with D B, 2-22 for any real number and positive matrices A and B. In particular if 1 2, we get Ando s geometric mean, i.e., A B AB. 1/2 It is clear to show that power mean enjoy the following new properties: aa A A ba p A q A 1 p+q, for all < p, q < In 2000, Micic, Pecaric and Seo [10] succeeded in generalizing the power mean to the operator mean. They used AσB fd with D B, 2-24 for any non-negative operator monotone function ft on [0, and positive matrices A and B. As a matter of fact, the power means are determined by the operator monotone function ft t when 0 < 1 or by the operator monotone function ft t 1/ when 1 <. It remains an open question whether there is a definition of the geometric mean of two positive n n matrices that satisfies all of properties from i - viii. We extend the mixed property to include the Tracy-Singh product as in next result. Lemma 2.1 Let A i and B i M + n i 1, 2 be compatible partitioned matrices. Then a A 1 ΘB 1 A 2 ΘB 2 A 1 A 2 ΘB 1 B b A 1 B 1 ΘA 2 B 2 A 1 ΘA 2 B 1 ΘB

7 Improvements on Geometric Means Related to the Tracy-Singh Products 55 Proof a By using 1-4 and 1-5, we have A 1 ΘB 1 A 2 ΘB 2 A 1 ΘB 1 1/2 A 1 ΘB 1 1/2 A 2 ΘB 2 A 1 ΘB 1 1/2} 1/2 A1 ΘB 1 1/ } 1/2 1 ΘB 1/2 1 A 2 ΘB 2 1 ΘB 1/2 1/2 1 A } 1 A 2 1 ΘB 1/2 1 B 2 B 1/ A 2 1 1/2 ΘB 1/2 1 A 2 1 1/2} 1 A 1 A 2 ΘB 1 B 2. We can prove b in a similar manner B 2 B 1/2 1 1/2} B 1/2 1 B 2 B 1/2 1 1/2} B 1/2 1 We extend also Lemma 2.1 to include the Fiedler and Pta definition and power mean definition related to the Tracy -Singh product as in next result. Lemma 2.2 For any real number and positive compatible partitioned matrices A i and B i i 1, 2, we have aa 1 ΘB 1 A 2 ΘB 2 A 1 A 2 ΘB 1 B ba 1 ΘB 1 A 2 ΘB 2 A 1 B 1 ΘA 2 B ca 1 B 1 ΘA 2 B 2 A 1 ΘA 2 B 1 ΘB da 1 B 1 ΘA 2 B 2 A 1 ΘA 2 B 1 ΘB Proof a To see that this is indeed true, let Then A 1 ΘB 1 A 2 ΘB 2 D 1 1 A 2 1 and D 2 B 1/2 1 B 2 B 1/2 1. A 1 ΘB 1 1/2 A 1 ΘB 1 1/2 A 2 ΘB 2 A 1 ΘB 1 1/2 A1 ΘB 1 1/2 1 1 ΘB 1/2 1 A 2 ΘB 2 1 ΘB 1/2 1/2 A 1 1 A 2 1 ΘB 1/2 1 B 2 B 1/ } 1 1 D 1 ΘD D 1 1 D 1 B 1/2 A 1 A 2 ΘB 1 B 2. 1

8 56 Adem Kilicman & Zeyad Al Zhour ba 1 ΘB 1 A 2 ΘB 2 A 1 ΘB 1 1 A 2 ΘB 2 } 1/2 A1 ΘB 1 A 1 ΘB 1 1 A 2 ΘB 2 } 1/2 A 1 1 ΘB 1 1 A 2ΘB 2 } 1/2 A1 ΘB 1 A 1 1 ΘB 1 1 A 2ΘB 2 } 1/2 A 1 1 A 2ΘB1 1 B 2 } 1/2 A1 ΘB 1 A 1 1 A 2 B1 1 B 2 } 1/2 A 1 1 A 2 1/2 ΘB1 1 B 2 1/2} A 1 ΘB 1 A 1 1 A 2 1/2 ΘB1 1 B 2 1/2} A 1 1 A 2 1/2 A 1 A 1 1 A 2 1/2} Θ A 1 B 1 ΘA 2 B 2 B 1 1 B 2 1/2 B 1 B 1 1 B 2 1/2} We can prove c and d in a similar manner. 3 Geometric Means of Several Positive Matrices Other problems are how to define the geometric, arithmetic, harmonic and operator means of finitely many positive matrices. We generalize these means of two positive matrices to arrive the definitions of the weighted means of positive matrices. Definition 3.1 Let w 1, w 2,..., w be positive numbers such that 1 and let A i M n + 1 i, 2. The weighted geometric, weighted arithmetic and weighted harmonic means of A i 1 i, 2 are defined by a Weighted geometric mean: A i w A 1 2 s+1 where u s 1 w s+1 / w j b Weighted arithmetic mean: u1 1/2 A 2 u2 } u 1 1/ A 1 A 1/2 1/2 A, 3-1 for s 1, 2,..., 1. A i w 1 A w A w c Weighted harmonic mean:! w A i w 1 A w 1 A 1 1 A i. 3-2 A 1 i

9 Improvements on Geometric Means Related to the Tracy-Singh Products 57 Definition 3.2 Let A i M n + 1 i, 2 and f j 1 j 1 be non-negative operator monotone functions on [0,. The weighted operator mean of A i 1 i is defined by σ A i w f f A 1/2 3 2 f 1 2 A }. 3-4 In fact, the weighted geometric means are determined by the operator monotone functions f 1 t t u 1, f 2 t t u 2,..., f 2 t t u2, f 1 t t u1 where u j 1 w j+1 / j+1 w r, 0 < u j 1, for j 1, 2,, 1 and w 1, w 2,, w r1 are positive numbers such that 1. Note that also the weighted geometric mean becomes Ando s geometric mean, weighted arithmetic mean becomes arithmetic mean and weighted harmonic mean becomes harmonic mean when 2 and w 1 w These general definitions have many good properties analogous. The fundamental relationship is no surprise. Theorem 3.3 Let w 1, w 2,..., w be positive numbers such that 1 and let A i M n 1 i, 2 be positive matrices. Then see [12].! w A i A i w A i 3-5 w All the inequalities are strict unless A 1 A 2... A. Surprisingly, it has not previously been observed that the geometric mean for more than two matrices fails the symmetry property. For example, if 3 and w 1 w 2 w 3 1 3, corresponding geometric means of three positive matrices A 1, A 2 and A 3 are A 3 A 2 A 1 1 and A 1 A 2 A 3 3 [ ] 3 1 But if A 1, A mathematica, we find A 3 A 2 A A 3 2 1/ [ [ A 1 2 1/2 ] [ 1 1 and A ] and A 1 A 2 A } 2/3 A 1/2 1, } 2/3 A 1/2 3. ]. Then, after computing with [ In other words, A 3 A 2 A 1 A 1 A 2 A 1. Even though property iii fails, properties vi and vii are still applicable. ].

10 58 Adem Kilicman & Zeyad Al Zhour 4 Further Developments and Applications In this section, we prove several theorems related to geometric means and Tracy-Singh products for compatible partitioned matrices. Theorem 4.1 Let A i and B i M n +, 1 i be compatible partitioned matrices. Then a A i ΘB i A i Θ B i b ΘA i B i ΘA i ΘB i. Proof a The proof is by induction on ; we now by 2-28 that A 1 ΘB 1 A 2 ΘB 2 A 1 B 1 ΘA 2 B 2. This gives the claimed when 2. Suppose that 1 holds. Then A i ΘB i 1 1 A i ΘB i A i A ΘB A Θ We can prove b in a similar manner. 1 A i ΘB i 1 A i Θ A i Θ B i B i A ΘB B i B A i Θ B i. In general case, for any real number and positive matrices A i and B i i 1, 2. The power mixed property related to the Tracy-Singh product can be extended to any finite number of positive matrices as in next results Theorem 4.2 and Theorem 4.3. Theorem 4.2 Let A i and B i M n + 1 i, 2 be compatible partitioned matrices. Then a A i ΘB i A i Θ B i. 4-2 b ΘA i B i ΘA i ΘB i. Proof Follows immediately by on induction on. When A i M + n 1 i, 2 and i are real scalars, we have defined Now continue recurrently, setting A 1 A 2 A A 2 2 A 1 A A A i A 1 A , 2,..., 1 1, 2,..., 1 1, 2,..., 1 1, 2,..., 1 A

11 Improvements on Geometric Means Related to the Tracy-Singh Products , 2,..., 2 A i }. 4-4 Theorem 4.3 Let A i and B i M n + 1 i and be compatible partitioned matrices and let i 1 i be real scalars. Then for any m 2, m m m A i ΘB i Θ , 2,..., m 1 A i 1, 2,..., m 1 B i 1, 2,..., m 1 Proof We use induction on m. In particular, when m 2 we obtain Theorem 4.2. Suppose that A i ΘB i A i Θ B i holds. 1, 2,..., 1 1, 2,..., 1 1, 2,..., 1 Then +1 A i ΘB i A i ΘB i A +1 ΘB +1 1, 2,..., 1, 2,..., 1, 2,..., } A +1 ΘB +1 1/2 A +1 ΘB +1 1/2 A i ΘB i A +1 ΘB +1 1/2 1, 2,..., 1 +1 ΘB1/ ΘB 1/ ΘB1/ A i 1, 2,..., 1, 2,..., 1 A i 1, 2,..., 1 Θ B 1/2 +1 A i 1, 2,..., 1 Θ Θ +1 B 1/2 +1 B i 1, 2,..., Θ +1 ΘB 1/ B i 1, 2,..., 1 +1 B 1/2 +1 } +1 B i 1, 2,..., 1 B 1/2 +1 B i 1, 2,..., 1 A +1 ΘB +1 1/2 } +1 ΘB1/ ΘB1/2 +1 B 1/2 +1 } B 1/ Theorem 4.3 can be extended by using induction as given in the following theorem.

12 60 Adem Kilicman & Zeyad Al Zhour Theorem 4.4 Let A ij M n + 1 i m, 1 j be compatible partitioned matrices and let i 1 i m be real scalars. Then ΘA 1j... ΘA mj 1, 2,..., m 1 1, 2,..., m 1 Θ A 1j... A mj , 2,..., m 1 1, 2,..., m 1 Property vii for two positive matrices namely A B 1 A 1 B 1, can be extended to power mean of two positive matrices: To see that this is indeed true: A 1 B 1 A A 1 B A 1 B A B 1 A A A A 1 2 BA 1 2 } 1 A A 1 2 A 1 2 BA } 1 1 A 2 A B 1. A quic induction is sufficient to extend this to means of more than two matrices. Lemma 4.5 Let A i M + n 1 i, 2 and let i 1 i be real scalars. Then A 1 i 1, 2,..., 1 A i 1, 2,..., We now turn to a generalization of the arithmetic-geometric-harmonic mean inequality related to Tracy-Singh Product. Theorem 4.6 Let A ij M n + 1 i m, 1 j be compatible partitioned matrices and let 1 i m be positive scalars such that m 1. Let u i 1 i 1, 2,..., m 1. Then a b ΘA 1 ij 1 1 i m ΘA 1 i jj i 1,,i m1 +1 / i+1 s1 w s Θ A 1j A mj ΘA ij. 4-9 w w 1 ΘA ij ΘA mj w w i 11 Equalities in 4-9 and 4-10 hold if and only if 1 m i m1 ΘA 1j... ΘA mj., ΘA ijj 4-10

13 Improvements on Geometric Means Related to the Tracy-Singh Products 61 Proof By Theorem 3.3 and Theorem 4.4, we have Θ A 1j... A mj ΘA 1j... w w w w Substituting A 1 ij for A ij and using Lemma 4.5, we obtain ΘA mj 1 Θ A 1j... A mj Θ A 1 1j... A 1 mj w w w w ΘA ij. ΘA 1 ij. Now, we have and hence Θ A 1j... A mj w w ΘA 1 ij 1 1 ΘA 1 ij, Θ A 1j... A mj. w w Similarly, we have the second inequalities b. Since ΘA 1j... ΘA mj Θ A 1j... A mj w w w w m Θ A ij m i 11 i m1 ΘA ijj. We shall use Theorem 4.6 to extend a result of Ando [2, pp. 229, Theorem 12] in the following theorem. Theorem 4.7 Let A i M + n 1 i m be commutative matrices. Then see [2] m m 1 m A i m A i We need some further ingredients before we address Ando s result. In 1997, Alic and others [1] gave a generalization of Theorem 3.3 in the negative weight case. The various means are defined just as before, even if there are negative weights. Theorem 4.8 Let A i 1 i be positive matrices and let 1 i be real numbers such that w 1 > 0, < 02 i and 1. Then see [1] A i w A i w

14 62 Adem Kilicman & Zeyad Al Zhour If A 1 i > 0, then A i w! w Equalities hold if and only if A 1... A. Developing Theorem 4.8, we have the following. A i Theorem 4.9 Let A ij M n + 1 i m, 1 j and let 1 i m be real numbers such that w 1 > 0, < 02 i m, and 1. Let u i 1 +1 / i+1 s1 w s, for i 1, 2,, m 1. Then a b... i 11 If w 1 ΘA ij Θ A 1j... A mj w w 1...m ΘA ijj i m1 ΘA 1j w m ΘA 1j ΘA mj > 0, then c Θ A 1j... A mj w w d ΘA ij... ΘA mj w w Equalities hold if and only if ΘA 1 ij... w w 1 ΘA 1j... ΘA mj. ΘA mj 1...i m ΘA 1 i jj i 1,...,i m Using Theorem 4.6 and the connection between the Khatri-Rao and Tracy-Singh products in 1-1, we have the following two theorems: Theorem 4.10 Let A ij M n + 1 i m, 1 j be compatible partitioned matrices and let 1 i m be positive real numbers such that m 1. Let u i 1 +1 / i+1 s1 w s, for i 1, 2,, m 1. Then a b A 1j... A mj w w A 1j... A mj w w... i 11 A ij m i m1 A ijj 4-19 Equalities hold if and only if A 1j... A mj. Proof It follows immediately by using Theorem 4.6 and Eq.1-1.

15 Improvements on Geometric Means Related to the Tracy-Singh Products 63 Finally, we arrive at our extension and generalization of Ando s result quoted in Theorem 4.7. Theorem 4.11 Let A i M n + 1 i m be compatible partitioned matrices. Then A 1... A m A 2... A m A 1... A m A 1... A m 1 w w w w w w w w m A i Proof In Theorem 4.10, let Then A 1... A m w w A 11,..., A m1 A 1,..., A m A 12,..., A m2 A 2,..., A m, A 1 A 1m, A 2m,..., A mm A m, A 1,..., A m 1. A 2... w w A m A 1... w A 1j... A mj w w A m A 1... A m 1 w w w m A ij A j m A i To see how Ando s result is a special case of Theorem 4.11, note that if A i 1 i m commute and non-partitioned matrices, then A 1... A m A 2... A m A 1 w w w w w A m A 1... A m w w w Theorem 4.12 Let A i M + n i 1, 2 be compatible partitioned matrices. Then for any real number and for all < p, q < A 1 ΘA 2 p A 1 ΘA 2 q A 1 ΘA 2 1 p+q 4-22

16 64 Adem Kilicman & Zeyad Al Zhour In particular if 1 2, we have A 1 ΘA 2 p A 1 ΘA 2 q A 1 ΘA 2 p+q/ Proof By using 1-5, 2-23 and Lemma 2.1, we have A 1 ΘA 2 p A 1 ΘA 2 q A p 1 ΘAp 2 A q 1 ΘAq 2 Ap 1 A q 1 ΘAp 2 A q 2 A 1 p+q 1 ΘA 1 p+q 2 A 1 ΘA 2 1+p+q. Theorem 4.13 Let A i M n + 1 i be compatible partitioned matrices. Then for any real number and for all < p, q < 4-24 ΘA p i In particular if 1 2, we have ΘA p i ΘA q i ΘA q i ΘA 1 p+q i ΘA p+q/2 i 4-25 Proof The proof is a consequence of Theorem This gives the claimed when 2. We can now proceed by induction on. Assume that the corollary holds for products of 1 matrices. Then ΘA p i ΘA q i [ 1 1 ΘA p i ΘA 1 p+q i 1 ΘA q i ] Θ[A p A q ] ΘA 1 p+q ΘA 1 p+q i Remar: The results obtained in Section 2, Section 3 and Section 4 are quite general. As a special case, consider the matrices in above sections are non-partitioned, we then have equalities and inequalities involving Kronecer and Hadamard products by replacing Θ by and by.. 5 Conclusion In the present wor we study a family of geometric means for positive matrices. It is still an open problem to find a completely satisfactory definition that satisfies all properties i-viii. To find a completely satisfactory definition is subject for the future study. In this study we have succeeded to find new connections between geometric means and Tracy- Singh products and establish several equalities and inequalities for geometric means and Tracy-Singh products of several positive matrices. Acnowledgment The authors gratefully acnowledge that this research has been partially supported by Universiti Putra Malaysia UPM under the Grant IRPA EA001.

17 Improvements on Geometric Means Related to the Tracy-Singh Products 65 References [1] M. Alic, B. Mond, J. Pecaric & V. Volence, The Arithmetic-Geometric-Harmonic Mean and Related Matrix Inequalities Lin. Alg. and its Appl , [2] T. Ando, Concavity of Certain Maps on Positive Definite Matrices and Applications to Hadamard Products, Lin. Alg. and its Appl , [3] T. Ando, On Arithmetic-Geometric-Harmonic Mean Inequalities for Positive Definite Matrices, Lin. Alg. and its Appl. 52/ , [4] T. Ando & F. Hiai, Holder Type Inequalities for Matrices, Math. Ineq. Appl ,1-30. [5] C.-G. Cao, X. Zhang & Z.-P. Yang Some Inequalities for the Khatri-Rao Products of Matrices, Elec. J. Lin. Alg. ISSN , , [6] B. Q. Feng, Matrix inequalities, Ph.D. Thesis, Kent State University [7] M. Fiedler & V. Pta, A New Positive Definite Geometric Mean of Two Positive definite Matrices, Lin. Alg. and its Appl., , [8] S. Liu, Matrix Results on the Khatri-Rao and Tracy-Singh Products, Lin. Alg. and its Appl , [9] S. Liu, Several Inequalities Involving Khatri-Rao Products of Positive Semi-definite Matrices, Lin. Alg. and its Appl , [10] J. Micic, J. Pecaric & Y. Seo, Complementary Inequalities to Inequalities of Jensen and Ando based on the Mond-Pecaric Method, Lin. Alg. and its Appl , [11] B. Mond, J. Pecaric & Zagreb, A mixed Arithmetic-Mean-Harmonic-Mean Matrix Inequality, Lin. Alg. and its Appl. 237/ , [12] M. Sagae & K. Tanabe, Upper and Lower Bounds for the Arithmetic-Geometric- Harmonic Means of Positive Definite Matrices, Lin. Multilin. Alg , [13] Zeyad Al Zhour & Adem Kilicman, Extension and Generalization Inequalities Involving the Hadamard Product of Several Matrices, ICREM 2, 2005, [14] Zeyad Al Zhour & Adem Kilicman, New Holder-Type Inequalities for the Tracy-Singh and Khatri-Rao Products of Positive Matrices, IRCMSA 2005, Proceeding ISBN: , 1-7.

Journal of Inequalities in Pure and Applied Mathematics

Journal of Inequalities in Pure and Applied Mathematics Journal of Inequalities in Pure and Applied Mathematics http://jipam.vu.edu.au/ Volume 7, Issue 1, Article 34, 2006 MATRIX EQUALITIES AND INEQUALITIES INVOLVING KHATRI-RAO AND TRACY-SINGH SUMS ZEYAD AL

More information

Inequalities Involving Khatri-Rao Products of Positive Semi-definite Matrices

Inequalities Involving Khatri-Rao Products of Positive Semi-definite Matrices Applied Mathematics E-Notes, (00), 117-14 c ISSN 1607-510 Available free at mirror sites of http://www.math.nthu.edu.tw/ amen/ Inequalities Involving Khatri-Rao Products of Positive Semi-definite Matrices

More information

SOME INEQUALITIES FOR THE KHATRI-RAO PRODUCT OF MATRICES

SOME INEQUALITIES FOR THE KHATRI-RAO PRODUCT OF MATRICES SOME INEQUALITIES FOR THE KHATRI-RAO PRODUCT OF MATRICES CHONG-GUANG CAO, XIAN ZHANG, AND ZHONG-PENG YANG Abstract. Several inequalities for the Khatri-Rao product of complex positive definite Hermitian

More information

Ann. Funct. Anal. 5 (2014), no. 2, A nnals of F unctional A nalysis ISSN: (electronic) URL:www.emis.

Ann. Funct. Anal. 5 (2014), no. 2, A nnals of F unctional A nalysis ISSN: (electronic) URL:www.emis. Ann. Funct. Anal. 5 (2014), no. 2, 147 157 A nnals of F unctional A nalysis ISSN: 2008-8752 (electronic) URL:www.emis.de/journals/AFA/ ON f-connections OF POSITIVE DEFINITE MATRICES MAREK NIEZGODA This

More information

A matrix over a field F is a rectangular array of elements from F. The symbol

A matrix over a field F is a rectangular array of elements from F. The symbol Chapter MATRICES Matrix arithmetic A matrix over a field F is a rectangular array of elements from F The symbol M m n (F ) denotes the collection of all m n matrices over F Matrices will usually be denoted

More information

Journal of Inequalities in Pure and Applied Mathematics

Journal of Inequalities in Pure and Applied Mathematics Journal of Inequalities in Pure and Applied Mathematics KANTOROVICH TYPE INEQUALITIES FOR 1 > p > 0 MARIKO GIGA Department of Mathematics Nippon Medical School 2-297-2 Kosugi Nakahara-ku Kawasaki 211-0063

More information

ELA ON A SCHUR COMPLEMENT INEQUALITY FOR THE HADAMARD PRODUCT OF CERTAIN TOTALLY NONNEGATIVE MATRICES

ELA ON A SCHUR COMPLEMENT INEQUALITY FOR THE HADAMARD PRODUCT OF CERTAIN TOTALLY NONNEGATIVE MATRICES ON A SCHUR COMPLEMENT INEQUALITY FOR THE HADAMARD PRODUCT OF CERTAIN TOTALLY NONNEGATIVE MATRICES ZHONGPENG YANG AND XIAOXIA FENG Abstract. Under the entrywise dominance partial ordering, T.L. Markham

More information

2. reverse inequalities via specht ratio To study the Golden-Thompson inequality, Ando-Hiai in [1] developed the following log-majorizationes:

2. reverse inequalities via specht ratio To study the Golden-Thompson inequality, Ando-Hiai in [1] developed the following log-majorizationes: ON REVERSES OF THE GOLDEN-THOMPSON TYPE INEQUALITIES MOHAMMAD BAGHER GHAEMI, VENUS KALEIBARY AND SHIGERU FURUICHI arxiv:1708.05951v1 [math.fa] 20 Aug 2017 Abstract. In this paper we present some reverses

More information

On a decomposition lemma for positive semi-definite block-matrices

On a decomposition lemma for positive semi-definite block-matrices On a decomposition lemma for positive semi-definite bloc-matrices arxiv:10.0473v1 [math.fa] Feb 01 Jean-Christophe ourin, Eun-Young Lee, Minghua Lin January, 01 Abstract This short note, in part of expository

More information

Matrix operator inequalities based on Mond, Pecaric, Jensen and Ando s

Matrix operator inequalities based on Mond, Pecaric, Jensen and Ando s http://wwwournalofinequalitiesandapplicationscom/content/0//48 RESEARCH Matrix operator inequalities based on Mond, Pecaric, Jensen and Ando s Xue Lanlan and Wu Junliang * Open Access * Correspondence:

More information

On (T,f )-connections of matrices and generalized inverses of linear operators

On (T,f )-connections of matrices and generalized inverses of linear operators Electronic Journal of Linear Algebra Volume 30 Volume 30 (2015) Article 33 2015 On (T,f )-connections of matrices and generalized inverses of linear operators Marek Niezgoda University of Life Sciences

More information

InequalitiesInvolvingHadamardProductsof HermitianMatrices y

InequalitiesInvolvingHadamardProductsof HermitianMatrices y AppliedMathematics E-Notes, 1(2001), 91-96 c Availablefreeatmirrorsites ofhttp://math2.math.nthu.edu.tw/»amen/ InequalitiesInvolvingHadamardProductsof HermitianMatrices y Zhong-pengYang z,xianzhangchong-guangcao

More information

Banach Journal of Mathematical Analysis ISSN: (electronic)

Banach Journal of Mathematical Analysis ISSN: (electronic) Banach J Math Anal (009), no, 64 76 Banach Journal of Mathematical Analysis ISSN: 75-8787 (electronic) http://wwwmath-analysisorg ON A GEOMETRIC PROPERTY OF POSITIVE DEFINITE MATRICES CONE MASATOSHI ITO,

More information

Elementary linear algebra

Elementary linear algebra Chapter 1 Elementary linear algebra 1.1 Vector spaces Vector spaces owe their importance to the fact that so many models arising in the solutions of specific problems turn out to be vector spaces. The

More information

YONGDO LIM. 1. Introduction

YONGDO LIM. 1. Introduction THE INVERSE MEAN PROBLEM OF GEOMETRIC AND CONTRAHARMONIC MEANS YONGDO LIM Abstract. In this paper we solve the inverse mean problem of contraharmonic and geometric means of positive definite matrices (proposed

More information

Quantum Computing Lecture 2. Review of Linear Algebra

Quantum Computing Lecture 2. Review of Linear Algebra Quantum Computing Lecture 2 Review of Linear Algebra Maris Ozols Linear algebra States of a quantum system form a vector space and their transformations are described by linear operators Vector spaces

More information

Abstract. In this article, several matrix norm inequalities are proved by making use of the Hiroshima 2003 result on majorization relations.

Abstract. In this article, several matrix norm inequalities are proved by making use of the Hiroshima 2003 result on majorization relations. HIROSHIMA S THEOREM AND MATRIX NORM INEQUALITIES MINGHUA LIN AND HENRY WOLKOWICZ Abstract. In this article, several matrix norm inequalities are proved by making use of the Hiroshima 2003 result on majorization

More information

Trace Inequalities for a Block Hadamard Product

Trace Inequalities for a Block Hadamard Product Filomat 32:1 2018), 285 292 https://doiorg/102298/fil1801285p Published by Faculty of Sciences and Mathematics, University of Niš, Serbia Available at: http://wwwpmfniacrs/filomat Trace Inequalities for

More information

ELEMENTARY LINEAR ALGEBRA

ELEMENTARY LINEAR ALGEBRA ELEMENTARY LINEAR ALGEBRA K R MATTHEWS DEPARTMENT OF MATHEMATICS UNIVERSITY OF QUEENSLAND First Printing, 99 Chapter LINEAR EQUATIONS Introduction to linear equations A linear equation in n unknowns x,

More information

NORM INEQUALITIES FOR THE GEOMETRIC MEAN AND ITS REVERSE. Ritsuo Nakamoto* and Yuki Seo** Received September 9, 2006; revised September 26, 2006

NORM INEQUALITIES FOR THE GEOMETRIC MEAN AND ITS REVERSE. Ritsuo Nakamoto* and Yuki Seo** Received September 9, 2006; revised September 26, 2006 Scientiae Mathematicae Japonicae Online, e-2006, 209 24 209 NORM INEQUALITIES FOR THE GEOMETRIC MEAN AND ITS REVERSE Ritsuo Nakamoto* and Yuki Seo** Received September 9, 2006; revised September 26, 2006

More information

Generalized Schur complements of matrices and compound matrices

Generalized Schur complements of matrices and compound matrices Electronic Journal of Linear Algebra Volume 2 Volume 2 (200 Article 3 200 Generalized Schur complements of matrices and compound matrices Jianzhou Liu Rong Huang Follow this and additional wors at: http://repository.uwyo.edu/ela

More information

Matrix Arithmetic. j=1

Matrix Arithmetic. j=1 An m n matrix is an array A = Matrix Arithmetic a 11 a 12 a 1n a 21 a 22 a 2n a m1 a m2 a mn of real numbers a ij An m n matrix has m rows and n columns a ij is the entry in the i-th row and j-th column

More information

The Metric Geometry of the Multivariable Matrix Geometric Mean

The Metric Geometry of the Multivariable Matrix Geometric Mean Trieste, 2013 p. 1/26 The Metric Geometry of the Multivariable Matrix Geometric Mean Jimmie Lawson Joint Work with Yongdo Lim Department of Mathematics Louisiana State University Baton Rouge, LA 70803,

More information

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Chemistry 5.76 Revised February, 1982 NOTES ON MATRIX METHODS

MASSACHUSETTS INSTITUTE OF TECHNOLOGY Chemistry 5.76 Revised February, 1982 NOTES ON MATRIX METHODS MASSACHUSETTS INSTITUTE OF TECHNOLOGY Chemistry 5.76 Revised February, 198 NOTES ON MATRIX METHODS 1. Matrix Algebra Margenau and Murphy, The Mathematics of Physics and Chemistry, Chapter 10, give almost

More information

n Inequalities Involving the Hadamard roduct of Matrices 57 Let A be a positive definite n n Hermitian matrix. There exists a matrix U such that A U Λ

n Inequalities Involving the Hadamard roduct of Matrices 57 Let A be a positive definite n n Hermitian matrix. There exists a matrix U such that A U Λ The Electronic Journal of Linear Algebra. A publication of the International Linear Algebra Society. Volume 6, pp. 56-61, March 2000. ISSN 1081-3810. http//math.technion.ac.il/iic/ela ELA N INEQUALITIES

More information

Matrix Arithmetic. a 11 a. A + B = + a m1 a mn. + b. a 11 + b 11 a 1n + b 1n = a m1. b m1 b mn. and scalar multiplication for matrices via.

Matrix Arithmetic. a 11 a. A + B = + a m1 a mn. + b. a 11 + b 11 a 1n + b 1n = a m1. b m1 b mn. and scalar multiplication for matrices via. Matrix Arithmetic There is an arithmetic for matrices that can be viewed as extending the arithmetic we have developed for vectors to the more general setting of rectangular arrays: if A and B are m n

More information

MATRIX REPRESENTATIONS FOR MULTIPLICATIVE NESTED SUMS. 1. Introduction. The harmonic sums, defined by [BK99, eq. 4, p. 1] sign (i 1 ) n 1 (N) :=

MATRIX REPRESENTATIONS FOR MULTIPLICATIVE NESTED SUMS. 1. Introduction. The harmonic sums, defined by [BK99, eq. 4, p. 1] sign (i 1 ) n 1 (N) := MATRIX REPRESENTATIONS FOR MULTIPLICATIVE NESTED SUMS LIN JIU AND DIANE YAHUI SHI* Abstract We study the multiplicative nested sums which are generalizations of the harmonic sums and provide a calculation

More information

Math 360 Linear Algebra Fall Class Notes. a a a a a a. a a a

Math 360 Linear Algebra Fall Class Notes. a a a a a a. a a a Math 360 Linear Algebra Fall 2008 9-10-08 Class Notes Matrices As we have already seen, a matrix is a rectangular array of numbers. If a matrix A has m columns and n rows, we say that its dimensions are

More information

JENSEN S OPERATOR AND APPLICATIONS TO MEAN INEQUALITIES FOR OPERATORS IN HILBERT SPACE

JENSEN S OPERATOR AND APPLICATIONS TO MEAN INEQUALITIES FOR OPERATORS IN HILBERT SPACE JENSEN S OPERATOR AND APPLICATIONS TO MEAN INEQUALITIES FOR OPERATORS IN HILBERT SPACE MARIO KRNIĆ NEDA LOVRIČEVIĆ AND JOSIP PEČARIĆ Abstract. In this paper we consider Jensen s operator which includes

More information

Kantorovich type inequalities for ordered linear spaces

Kantorovich type inequalities for ordered linear spaces Electronic Journal of Linear Algebra Volume 20 Volume 20 (2010) Article 8 2010 Kantorovich type inequalities for ordered linear spaces Marek Niezgoda bniezgoda@wp.pl Follow this and additional works at:

More information

We could express the left side as a sum of vectors and obtain the Vector Form of a Linear System: a 12 a x n. a m2

We could express the left side as a sum of vectors and obtain the Vector Form of a Linear System: a 12 a x n. a m2 Week 22 Equations, Matrices and Transformations Coefficient Matrix and Vector Forms of a Linear System Suppose we have a system of m linear equations in n unknowns a 11 x 1 + a 12 x 2 + + a 1n x n b 1

More information

Jensen s Operator and Applications to Mean Inequalities for Operators in Hilbert Space

Jensen s Operator and Applications to Mean Inequalities for Operators in Hilbert Space BULLETIN of the Malaysian Mathematical Sciences Society http://math.usm.my/bulletin Bull. Malays. Math. Sci. Soc. 351 01 1 14 Jensen s Operator and Applications to Mean Inequalities for Operators in Hilbert

More information

IMPROVED ARITHMETIC-GEOMETRIC AND HEINZ MEANS INEQUALITIES FOR HILBERT SPACE OPERATORS

IMPROVED ARITHMETIC-GEOMETRIC AND HEINZ MEANS INEQUALITIES FOR HILBERT SPACE OPERATORS IMPROVED ARITHMETI-GEOMETRI AND HEINZ MEANS INEQUALITIES FOR HILBERT SPAE OPERATORS FUAD KITTANEH, MARIO KRNIĆ, NEDA LOVRIČEVIĆ, AND JOSIP PEČARIĆ Abstract. The main objective of this paper is an improvement

More information

Matrix Inequalities by Means of Block Matrices 1

Matrix Inequalities by Means of Block Matrices 1 Mathematical Inequalities & Applications, Vol. 4, No. 4, 200, pp. 48-490. Matrix Inequalities by Means of Block Matrices Fuzhen Zhang 2 Department of Math, Science and Technology Nova Southeastern University,

More information

Norm inequalities related to the matrix geometric mean

Norm inequalities related to the matrix geometric mean isid/ms/2012/07 April 20, 2012 http://www.isid.ac.in/ statmath/eprints Norm inequalities related to the matrix geometric mean RAJENDRA BHATIA PRIYANKA GROVER Indian Statistical Institute, Delhi Centre

More information

The matrix arithmetic-geometric mean inequality revisited

The matrix arithmetic-geometric mean inequality revisited isid/ms/007/11 November 1 007 http://wwwisidacin/ statmath/eprints The matrix arithmetic-geometric mean inequality revisited Rajendra Bhatia Fuad Kittaneh Indian Statistical Institute Delhi Centre 7 SJSS

More information

ECE 275A Homework #3 Solutions

ECE 275A Homework #3 Solutions ECE 75A Homework #3 Solutions. Proof of (a). Obviously Ax = 0 y, Ax = 0 for all y. To show sufficiency, note that if y, Ax = 0 for all y, then it must certainly be true for the particular value of y =

More information

Lecture 4: Purifications and fidelity

Lecture 4: Purifications and fidelity CS 766/QIC 820 Theory of Quantum Information (Fall 2011) Lecture 4: Purifications and fidelity Throughout this lecture we will be discussing pairs of registers of the form (X, Y), and the relationships

More information

A lower bound for the Laplacian eigenvalues of a graph proof of a conjecture by Guo

A lower bound for the Laplacian eigenvalues of a graph proof of a conjecture by Guo A lower bound for the Laplacian eigenvalues of a graph proof of a conjecture by Guo A. E. Brouwer & W. H. Haemers 2008-02-28 Abstract We show that if µ j is the j-th largest Laplacian eigenvalue, and d

More information

ON THE HÖLDER CONTINUITY OF MATRIX FUNCTIONS FOR NORMAL MATRICES

ON THE HÖLDER CONTINUITY OF MATRIX FUNCTIONS FOR NORMAL MATRICES Volume 10 (2009), Issue 4, Article 91, 5 pp. ON THE HÖLDER CONTINUITY O MATRIX UNCTIONS OR NORMAL MATRICES THOMAS P. WIHLER MATHEMATICS INSTITUTE UNIVERSITY O BERN SIDLERSTRASSE 5, CH-3012 BERN SWITZERLAND.

More information

A FIRST COURSE IN LINEAR ALGEBRA. An Open Text by Ken Kuttler. Matrix Arithmetic

A FIRST COURSE IN LINEAR ALGEBRA. An Open Text by Ken Kuttler. Matrix Arithmetic A FIRST COURSE IN LINEAR ALGEBRA An Open Text by Ken Kuttler Matrix Arithmetic Lecture Notes by Karen Seyffarth Adapted by LYRYX SERVICE COURSE SOLUTION Attribution-NonCommercial-ShareAlike (CC BY-NC-SA)

More information

Numerical Analysis Lecture Notes

Numerical Analysis Lecture Notes Numerical Analysis Lecture Notes Peter J Olver 3 Review of Matrix Algebra Vectors and matrices are essential for modern analysis of systems of equations algebrai, differential, functional, etc In this

More information

a 11 x 1 + a 12 x a 1n x n = b 1 a 21 x 1 + a 22 x a 2n x n = b 2.

a 11 x 1 + a 12 x a 1n x n = b 1 a 21 x 1 + a 22 x a 2n x n = b 2. Chapter 1 LINEAR EQUATIONS 11 Introduction to linear equations A linear equation in n unknowns x 1, x,, x n is an equation of the form a 1 x 1 + a x + + a n x n = b, where a 1, a,, a n, b are given real

More information

Massachusetts Institute of Technology Department of Economics Statistics. Lecture Notes on Matrix Algebra

Massachusetts Institute of Technology Department of Economics Statistics. Lecture Notes on Matrix Algebra Massachusetts Institute of Technology Department of Economics 14.381 Statistics Guido Kuersteiner Lecture Notes on Matrix Algebra These lecture notes summarize some basic results on matrix algebra used

More information

Journal of Inequalities in Pure and Applied Mathematics

Journal of Inequalities in Pure and Applied Mathematics Journal of Inequalities in Pure and Applied Mathematics MATRIX AND OPERATOR INEQUALITIES FOZI M DANNAN Department of Mathematics Faculty of Science Qatar University Doha - Qatar EMail: fmdannan@queduqa

More information

Algebra I Fall 2007

Algebra I Fall 2007 MIT OpenCourseWare http://ocw.mit.edu 18.701 Algebra I Fall 007 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. 18.701 007 Geometry of the Special Unitary

More information

Regular operator mappings and multivariate geometric means

Regular operator mappings and multivariate geometric means arxiv:1403.3781v3 [math.fa] 1 Apr 2014 Regular operator mappings and multivariate geometric means Fran Hansen March 15, 2014 Abstract We introduce the notion of regular operator mappings of several variables

More information

Lectures on Linear Algebra for IT

Lectures on Linear Algebra for IT Lectures on Linear Algebra for IT by Mgr Tereza Kovářová, PhD following content of lectures by Ing Petr Beremlijski, PhD Department of Applied Mathematics, VSB - TU Ostrava Czech Republic 3 Inverse Matrix

More information

Convergent Iterative Algorithms in the 2-inner Product Space R n

Convergent Iterative Algorithms in the 2-inner Product Space R n Int. J. Open Problems Compt. Math., Vol. 6, No. 4, December 2013 ISSN 1998-6262; Copyright c ICSRS Publication, 2013 www.i-csrs.org Convergent Iterative Algorithms in the 2-inner Product Space R n Iqbal

More information

Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition

Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Prof. Tesler Math 283 Fall 2018 Also see the separate version of this with Matlab and R commands. Prof. Tesler Diagonalizing

More information

ON WEIGHTED PARTIAL ORDERINGS ON THE SET OF RECTANGULAR COMPLEX MATRICES

ON WEIGHTED PARTIAL ORDERINGS ON THE SET OF RECTANGULAR COMPLEX MATRICES olume 10 2009, Issue 2, Article 41, 10 pp. ON WEIGHTED PARTIAL ORDERINGS ON THE SET OF RECTANGULAR COMPLEX MATRICES HANYU LI, HU YANG, AND HUA SHAO COLLEGE OF MATHEMATICS AND PHYSICS CHONGQING UNIERSITY

More information

Foundations of Matrix Analysis

Foundations of Matrix Analysis 1 Foundations of Matrix Analysis In this chapter we recall the basic elements of linear algebra which will be employed in the remainder of the text For most of the proofs as well as for the details, the

More information

On Hadamard and Kronecker Products Over Matrix of Matrices

On Hadamard and Kronecker Products Over Matrix of Matrices General Letters in Mathematics Vol 4, No 1, Feb 2018, pp13-22 e-issn 2519-9277, p-issn 2519-9269 Available online at http:// wwwrefaadcom On Hadamard and Kronecker Products Over Matrix of Matrices Z Kishka1,

More information

NOTES FOR LINEAR ALGEBRA 133

NOTES FOR LINEAR ALGEBRA 133 NOTES FOR LINEAR ALGEBRA 33 William J Anderson McGill University These are not official notes for Math 33 identical to the notes projected in class They are intended for Anderson s section 4, and are 2

More information

Lecture notes: Applied linear algebra Part 1. Version 2

Lecture notes: Applied linear algebra Part 1. Version 2 Lecture notes: Applied linear algebra Part 1. Version 2 Michael Karow Berlin University of Technology karow@math.tu-berlin.de October 2, 2008 1 Notation, basic notions and facts 1.1 Subspaces, range and

More information

This is a submission to one of journals of TMRG: BJMA/AFA EXTENSION OF THE REFINED JENSEN S OPERATOR INEQUALITY WITH CONDITION ON SPECTRA

This is a submission to one of journals of TMRG: BJMA/AFA EXTENSION OF THE REFINED JENSEN S OPERATOR INEQUALITY WITH CONDITION ON SPECTRA This is a submission to one of journals of TMRG: BJMA/AFA EXTENSION OF THE REFINED JENSEN S OPERATOR INEQUALITY WITH CONDITION ON SPECTRA JADRANKA MIĆIĆ, JOSIP PEČARIĆ AND JURICA PERIĆ3 Abstract. We give

More information

Geometry of the Special Unitary Group

Geometry of the Special Unitary Group Geometry of the Special Unitary Group The elements of SU 2 are the unitary 2 2 matrices with determinant 1. It is not hard to see that they have the form a 1) ) b, b ā with āa+bb = 1. This is the transpose

More information

ON WEIGHTED PARTIAL ORDERINGS ON THE SET OF RECTANGULAR COMPLEX MATRICES

ON WEIGHTED PARTIAL ORDERINGS ON THE SET OF RECTANGULAR COMPLEX MATRICES ON WEIGHTED PARTIAL ORDERINGS ON THE SET OF RECTANGULAR COMPLEX MATRICES HANYU LI, HU YANG College of Mathematics and Physics Chongqing University Chongqing, 400030, P.R. China EMail: lihy.hy@gmail.com,

More information

Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition

Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Linear Algebra review Powers of a diagonalizable matrix Spectral decomposition Prof. Tesler Math 283 Fall 2016 Also see the separate version of this with Matlab and R commands. Prof. Tesler Diagonalizing

More information

JENSEN S OPERATOR INEQUALITY AND ITS CONVERSES

JENSEN S OPERATOR INEQUALITY AND ITS CONVERSES MAH. SCAND. 100 (007, 61 73 JENSEN S OPERAOR INEQUALIY AND IS CONVERSES FRANK HANSEN, JOSIP PEČARIĆ and IVAN PERIĆ (Dedicated to the memory of Gert K. Pedersen Abstract We give a general formulation of

More information

Moore Penrose inverses and commuting elements of C -algebras

Moore Penrose inverses and commuting elements of C -algebras Moore Penrose inverses and commuting elements of C -algebras Julio Benítez Abstract Let a be an element of a C -algebra A satisfying aa = a a, where a is the Moore Penrose inverse of a and let b A. We

More information

Some Properties of Conjugate Unitary Matrices

Some Properties of Conjugate Unitary Matrices Volume 119 No. 6 2018, 75-88 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu ijpam.eu Some Properties of Conjugate Unitary Matrices A.Govindarasu and S.Sassicala

More information

Wavelets and Linear Algebra

Wavelets and Linear Algebra Wavelets and Linear Algebra () (05) 49-54 Wavelets and Linear Algebra http://wala.vru.ac.ir Vali-e-Asr University of Rafsanjan Schur multiplier norm of product of matrices M. Khosravia,, A. Sheikhhosseinia

More information

A property concerning the Hadamard powers of inverse M-matrices

A property concerning the Hadamard powers of inverse M-matrices Linear Algebra and its Applications 381 (2004 53 60 www.elsevier.com/locate/laa A property concerning the Hadamard powers of inverse M-matrices Shencan Chen Department of Mathematics, Fuzhou University,

More information

arxiv:math/ v2 [math.fa] 29 Mar 2007

arxiv:math/ v2 [math.fa] 29 Mar 2007 On the Araki-Lieb-Thirring inequality arxiv:math/0701129v2 [math.fa] 29 Mar 2007 Koenraad M.R. Audenaert Institute for Mathematical Sciences, Imperial College London 53 Prince s Gate, London SW7 2PG, United

More information

Symmetric and anti symmetric matrices

Symmetric and anti symmetric matrices Symmetric and anti symmetric matrices In linear algebra, a symmetric matrix is a square matrix that is equal to its transpose. Formally, matrix A is symmetric if. A = A Because equal matrices have equal

More information

MAT 2037 LINEAR ALGEBRA I web:

MAT 2037 LINEAR ALGEBRA I web: MAT 237 LINEAR ALGEBRA I 2625 Dokuz Eylül University, Faculty of Science, Department of Mathematics web: Instructor: Engin Mermut http://kisideuedutr/enginmermut/ HOMEWORK 2 MATRIX ALGEBRA Textbook: Linear

More information

Math 4377/6308 Advanced Linear Algebra

Math 4377/6308 Advanced Linear Algebra 2.3 Composition Math 4377/6308 Advanced Linear Algebra 2.3 Composition of Linear Transformations Jiwen He Department of Mathematics, University of Houston jiwenhe@math.uh.edu math.uh.edu/ jiwenhe/math4377

More information

Diagonalization by a unitary similarity transformation

Diagonalization by a unitary similarity transformation Physics 116A Winter 2011 Diagonalization by a unitary similarity transformation In these notes, we will always assume that the vector space V is a complex n-dimensional space 1 Introduction A semi-simple

More information

Two Results About The Matrix Exponential

Two Results About The Matrix Exponential Two Results About The Matrix Exponential Hongguo Xu Abstract Two results about the matrix exponential are given. One is to characterize the matrices A which satisfy e A e AH = e AH e A, another is about

More information

ELEMENTARY LINEAR ALGEBRA

ELEMENTARY LINEAR ALGEBRA ELEMENTARY LINEAR ALGEBRA K R MATTHEWS DEPARTMENT OF MATHEMATICS UNIVERSITY OF QUEENSLAND Second Online Version, December 998 Comments to the author at krm@mathsuqeduau All contents copyright c 99 Keith

More information

GROUP THEORY PRIMER. New terms: so(2n), so(2n+1), symplectic algebra sp(2n)

GROUP THEORY PRIMER. New terms: so(2n), so(2n+1), symplectic algebra sp(2n) GROUP THEORY PRIMER New terms: so(2n), so(2n+1), symplectic algebra sp(2n) 1. Some examples of semi-simple Lie algebras In the previous chapter, we developed the idea of understanding semi-simple Lie algebras

More information

Linear Algebra March 16, 2019

Linear Algebra March 16, 2019 Linear Algebra March 16, 2019 2 Contents 0.1 Notation................................ 4 1 Systems of linear equations, and matrices 5 1.1 Systems of linear equations..................... 5 1.2 Augmented

More information

Fall Inverse of a matrix. Institute: UC San Diego. Authors: Alexander Knop

Fall Inverse of a matrix. Institute: UC San Diego. Authors: Alexander Knop Fall 2017 Inverse of a matrix Authors: Alexander Knop Institute: UC San Diego Row-Column Rule If the product AB is defined, then the entry in row i and column j of AB is the sum of the products of corresponding

More information

ELA MAPS PRESERVING GENERAL MEANS OF POSITIVE OPERATORS

ELA MAPS PRESERVING GENERAL MEANS OF POSITIVE OPERATORS MAPS PRESERVING GENERAL MEANS OF POSITIVE OPERATORS LAJOS MOLNÁR Abstract. Under some mild conditions, the general form of bijective transformations of the set of all positive linear operators on a Hilbert

More information

Decomposition of Complete Tripartite Graphs Into 5-Cycles

Decomposition of Complete Tripartite Graphs Into 5-Cycles Decomposition of Complete Tripartite Graphs Into 5-Cycles E.S. MAHMOODIAN MARYAM MIRZAKHANI Department of Mathematical Sciences Sharif University of Technology P.O. Box 11365 9415 Tehran, I.R. Iran emahmood@irearn.bitnet

More information

Math Linear Algebra II. 1. Inner Products and Norms

Math Linear Algebra II. 1. Inner Products and Norms Math 342 - Linear Algebra II Notes 1. Inner Products and Norms One knows from a basic introduction to vectors in R n Math 254 at OSU) that the length of a vector x = x 1 x 2... x n ) T R n, denoted x,

More information

Sequences and Series

Sequences and Series Sequences and Series SUBJECTIVE PROBLEMS: Q. 1. The harmonic mean of two numbers is 4. Their arithmetic mean A and the geometric mean G satisfy the relation. 2A + G 2 = 27. Find the two numbers. (IIT JEE

More information

Matrices BUSINESS MATHEMATICS

Matrices BUSINESS MATHEMATICS Matrices BUSINESS MATHEMATICS 1 CONTENTS Matrices Special matrices Operations with matrices Matrix multipication More operations with matrices Matrix transposition Symmetric matrices Old exam question

More information

Getting Started with Communications Engineering. Rows first, columns second. Remember that. R then C. 1

Getting Started with Communications Engineering. Rows first, columns second. Remember that. R then C. 1 1 Rows first, columns second. Remember that. R then C. 1 A matrix is a set of real or complex numbers arranged in a rectangular array. They can be any size and shape (provided they are rectangular). A

More information

Spectral Theorem for Self-adjoint Linear Operators

Spectral Theorem for Self-adjoint Linear Operators Notes for the undergraduate lecture by David Adams. (These are the notes I would write if I was teaching a course on this topic. I have included more material than I will cover in the 45 minute lecture;

More information

Math 350 Fall 2011 Notes about inner product spaces. In this notes we state and prove some important properties of inner product spaces.

Math 350 Fall 2011 Notes about inner product spaces. In this notes we state and prove some important properties of inner product spaces. Math 350 Fall 2011 Notes about inner product spaces In this notes we state and prove some important properties of inner product spaces. First, recall the dot product on R n : if x, y R n, say x = (x 1,...,

More information

Exercise Set Suppose that A, B, C, D, and E are matrices with the following sizes: A B C D E

Exercise Set Suppose that A, B, C, D, and E are matrices with the following sizes: A B C D E Determine the size of a given matrix. Identify the row vectors and column vectors of a given matrix. Perform the arithmetic operations of matrix addition, subtraction, scalar multiplication, and multiplication.

More information

Determinant Worksheet Math 113

Determinant Worksheet Math 113 Determinant Worksheet Math 3 Evaluate: ) 2) 3) 4) 5) 6) 7) 8) 9) 0) ) 2) Answers ) -6 2) 9 3) - 4) 2,520 5) 0 6) 9 7) - 8) 42 9) -32 0) 64 ) 0 2) - X d2d0sl23 JK4uatfar RSFoIf0tswzaGrbeb 6LLL5CXq H 0AHl5lA

More information

ABSOLUTELY FLAT IDEMPOTENTS

ABSOLUTELY FLAT IDEMPOTENTS ABSOLUTELY FLAT IDEMPOTENTS JONATHAN M GROVES, YONATAN HAREL, CHRISTOPHER J HILLAR, CHARLES R JOHNSON, AND PATRICK X RAULT Abstract A real n-by-n idempotent matrix A with all entries having the same absolute

More information

Equivalent Conditions on Conjugate Unitary Matrices

Equivalent Conditions on Conjugate Unitary Matrices International Journal of Mathematics Trends and Technology (IJMTT Volume 42 Number 1- February 2017 Equivalent Conditions on Conjugate Unitary Matrices A. Govindarasu #, S.Sassicala #1 # Department of

More information

1 Matrices and matrix algebra

1 Matrices and matrix algebra 1 Matrices and matrix algebra 1.1 Examples of matrices A matrix is a rectangular array of numbers and/or variables. For instance 4 2 0 3 1 A = 5 1.2 0.7 x 3 π 3 4 6 27 is a matrix with 3 rows and 5 columns

More information

Linear Algebra and Matrix Inversion

Linear Algebra and Matrix Inversion Jim Lambers MAT 46/56 Spring Semester 29- Lecture 2 Notes These notes correspond to Section 63 in the text Linear Algebra and Matrix Inversion Vector Spaces and Linear Transformations Matrices are much

More information

ELEMENTARY LINEAR ALGEBRA

ELEMENTARY LINEAR ALGEBRA ELEMENTARY LINEAR ALGEBRA K. R. MATTHEWS DEPARTMENT OF MATHEMATICS UNIVERSITY OF QUEENSLAND Corrected Version, 7th April 013 Comments to the author at keithmatt@gmail.com Chapter 1 LINEAR EQUATIONS 1.1

More information

MATH 213 Linear Algebra and ODEs Spring 2015 Study Sheet for Midterm Exam. Topics

MATH 213 Linear Algebra and ODEs Spring 2015 Study Sheet for Midterm Exam. Topics MATH 213 Linear Algebra and ODEs Spring 2015 Study Sheet for Midterm Exam This study sheet will not be allowed during the test Books and notes will not be allowed during the test Calculators and cell phones

More information

GENERALIZED NORMS INEQUALITIES FOR ABSOLUTE VALUE OPERATORS

GENERALIZED NORMS INEQUALITIES FOR ABSOLUTE VALUE OPERATORS International Journal of Analysis Alications ISSN 9-8639 Volume 5, Number (04), -9 htt://www.etamaths.com GENERALIZED NORMS INEQUALITIES FOR ABSOLUTE VALUE OPERATORS ILYAS ALI, HU YANG, ABDUL SHAKOOR Abstract.

More information

A note on an unusual type of polar decomposition

A note on an unusual type of polar decomposition A note on an unusual type of polar decomposition H. Faßbender a, Kh. D. Ikramov b,,1 a Institut Computational Mathematics, TU Braunschweig, Pockelsstr. 14, D-38023 Braunschweig, Germany. b Faculty of Computational

More information

Problem set 2. Math 212b February 8, 2001 due Feb. 27

Problem set 2. Math 212b February 8, 2001 due Feb. 27 Problem set 2 Math 212b February 8, 2001 due Feb. 27 Contents 1 The L 2 Euler operator 1 2 Symplectic vector spaces. 2 2.1 Special kinds of subspaces....................... 3 2.2 Normal forms..............................

More information

(1) A frac = b : a, b A, b 0. We can define addition and multiplication of fractions as we normally would. a b + c d

(1) A frac = b : a, b A, b 0. We can define addition and multiplication of fractions as we normally would. a b + c d The Algebraic Method 0.1. Integral Domains. Emmy Noether and others quickly realized that the classical algebraic number theory of Dedekind could be abstracted completely. In particular, rings of integers

More information

New insights into best linear unbiased estimation and the optimality of least-squares

New insights into best linear unbiased estimation and the optimality of least-squares Journal of Multivariate Analysis 97 (2006) 575 585 www.elsevier.com/locate/jmva New insights into best linear unbiased estimation and the optimality of least-squares Mario Faliva, Maria Grazia Zoia Istituto

More information

UNDERSTANDING THE DIAGONALIZATION PROBLEM. Roy Skjelnes. 1.- Linear Maps 1.1. Linear maps. A map T : R n R m is a linear map if

UNDERSTANDING THE DIAGONALIZATION PROBLEM. Roy Skjelnes. 1.- Linear Maps 1.1. Linear maps. A map T : R n R m is a linear map if UNDERSTANDING THE DIAGONALIZATION PROBLEM Roy Skjelnes Abstract These notes are additional material to the course B107, given fall 200 The style may appear a bit coarse and consequently the student is

More information

b jσ(j), Keywords: Decomposable numerical range, principal character AMS Subject Classification: 15A60

b jσ(j), Keywords: Decomposable numerical range, principal character AMS Subject Classification: 15A60 On the Hu-Hurley-Tam Conjecture Concerning The Generalized Numerical Range Che-Man Cheng Faculty of Science and Technology, University of Macau, Macau. E-mail: fstcmc@umac.mo and Chi-Kwong Li Department

More information

22.3. Repeated Eigenvalues and Symmetric Matrices. Introduction. Prerequisites. Learning Outcomes

22.3. Repeated Eigenvalues and Symmetric Matrices. Introduction. Prerequisites. Learning Outcomes Repeated Eigenvalues and Symmetric Matrices. Introduction In this Section we further develop the theory of eigenvalues and eigenvectors in two distinct directions. Firstly we look at matrices where one

More information

Matrices. Chapter Definitions and Notations

Matrices. Chapter Definitions and Notations Chapter 3 Matrices 3. Definitions and Notations Matrices are yet another mathematical object. Learning about matrices means learning what they are, how they are represented, the types of operations which

More information

Semidefinite and Second Order Cone Programming Seminar Fall 2001 Lecture 5

Semidefinite and Second Order Cone Programming Seminar Fall 2001 Lecture 5 Semidefinite and Second Order Cone Programming Seminar Fall 2001 Lecture 5 Instructor: Farid Alizadeh Scribe: Anton Riabov 10/08/2001 1 Overview We continue studying the maximum eigenvalue SDP, and generalize

More information