A Note on Simple Nonzero Finite Generalized Singular Values
|
|
- Georgia Riley
- 6 years ago
- Views:
Transcription
1 A Note on Simple Nonzero Finite Generalized Singular Values Wei Ma Zheng-Jian Bai December Abstract In this paper we study the sensitivity and second order perturbation expansions of simple nonzero finite generalized singular values of a complex matrix pair which is analytically dependent on several parameters. Our results generalize the perturbation analysis given by Sun J. Comput. Math pp for simple nonzero singular values. Keywords Generalized singular value sensitivity analysis AMS subject classification. 65F15 15A18 1 Introduction The generalized singular value decomposition GSVD of two matrices of the same number of columns was proposed by Van Loan 25 and further studied by Paige and Saunders 13. The GSVD is very useful in many applications including constrained least squares problems 8 p. 58 and 25 weighted least squares problems 4 25 information retrieval 1 linear discriminant analysis 14 computing the Kronecker structure of matrix pencil A λb 12 discriminant analysis 11 ionospheric tomography 3 etc. Numerical methods and sensitivity analysis of the GSVD can be found for instance in Recently Chen and Li 7 presented the sensitivity of multiple nonzero finite generalized singular values of a real matrix pair which is analytically dependent on several parameters. In this paper we focus on the sensitivity issue and second order perturbation expansions of simple nonzero finite generalized singular values of a complex matrix pair analytically dependent on several parameters. We provide the explicit expressions for the first order partial derivatives of simple nonzero finite generalized singular values of the complex matrix pair and associated generalized singular matrix set. We also give the second order partial derivatives of simple nonzero finite generalized singular values of the complex matrix pair which is very useful for computing the second order Taylor expansions for simple nonzero finite generalized singular School of Mathematical Sciences Xiamen University Xiamen 3615 People s Republic of China mawei83424@yahoo.com.cn. School of Mathematical Sciences Xiamen University Xiamen 3615 People s Republic of China zjbai@xmu.edu.cn. The research of this author was partially supported by the National Natural Science Foundation of China grant the Natural Science Foundation of Fujian Province of China for Distinguished Young Scholars No. 21J62 and NCET. 1
2 values. Our results may be used to check the effectiveness and stability of GSVD-based methods for practical applications such as the method of particular solutions for solving planar eigenvalue problems 2 ionospheric tomography techniques 3 and discriminant analysis 11 etc. Our results generalize the perturbation analysis in 21 for simple non-zero singular values. Throughout this paper the following notation will be used. Let C m n and R m n stand for the set of all m n complex matrices and the set of all m n real matrices respectively. The symbols C n and R n denote the set of all complex n-vectors and the set of all real n-vectors respectively. Denote by A T and A the transpose and the conjugate transpose of a matrix A respectively. Let I n be the identity matrix of order n. The paper is organized as follows. In Section 2 we introduce some preliminary results on the GSVD. In Section 3 we discuss the sensitivity analysis and second order perturbation expansions of simple nonzero finite generalized singular values. The partial derivatives of a generalized singular vector set corresponding to the simple nonzero finite generalized singular values are also established. In Section 4 we define the sensitivity of simple nonzero finite generalized singular values and give some examples for computing the sensitivity and second order expansions of simple nonzero finite generalized singular values. Finally some conclusions and future work are presented in Section 5. 2 Preliminaries On the GSVD of a complex matrix pair we have the following result Lemma 2.1 Let A C m n and B C l n be such that ranka B n. Then there exist two unitary matrices U C m m V C l l and a nonsingular matrix Q C n n such that U Λ AQ and V l+r n r BQ Σ m r s n r s where s t denotes the s t null matrix and with Λ diagα 1... α r+s and Σ diagβ r+1... β n 1 α 1 α r > α r+1 α r+s > α r+s+1 α n β 1 β r < β r+1 β r+s < β r+s+1 β n 1 α 2 j + β 2 j 1 for 1 j n. ere {α j β j } n j1 are called the generalized singular values of the complex matrix-pair {A B}. For simplicity denote by σ{a B} the set of generalized singular values of {A B}. Suppose that α β is a simple nonzero finite generalized singular value of {A B} and σ 1 α/β. Then By Lemma 2.1 we can easily know that there exist two unitary matrices U u 1 U 2 C m m V v 1 V 2 C l l and a nonsingular matrix Q Q 2 C n n with u 1 C m v 1 C l and C n such that U σ1 AQ and V 1 BQ 1 Λ 2 Σ 2 2
3 where σ 1 1 / σ{λ 2 Σ 2 }. Therefore we get by 1 A σ 1 u 1 B v 1 u 1 u 1 v 1 v The vector set {u 1 v 1 } satisfying 2 is called a generalized singular vector set of {A B} corresponding to the generalized singular value σ Computing partial derivatives In this section we study the sensitivity issue of simple nonzero finite generalized singular values and the associated generalized singular vector set. Let p p 1... p N T Ap C m n and Bp C l n. In what follows without loss of generality we assume that p R N. By using the Implicit Function Theorem we establish the following results. Theorem 3.1 Let p R N Ap C m n Bp C l n and rankap Bp n. Suppose that ReAp ReBp ImAp and ImAp are all real analytic matrix-valued functions of p in some neighborhood B of the origin. If σ 1 1 σ 1 > is a simple nonzero finite generalized singular value of {A B} and there exist two unit vectors u 1 C m v 1 C l and a nonzero vector C n such that {u 1 v 1 } is an associated generalized singular vector set of {A B} then 1 there exists a simple generalized singular value σ 1 p 1 of {Ap Bp} such that σ 1 p is a real analytic function of p in some neighborhood B B of the origin σ 1 p > and σ 1 σ 1. 2 there exist u 1 p C m v 1 p C l and p C n with u 1 p u 1 p v 1 p v 1 p 1 such that Reu 1 p Imu 1 p Rev 1 p Imv 1 p Re p and Im p are all real analytic functions of p in B and {u 1 p v 1 p p} is a generalized singular vector set of {Ap Bp} corresponding to the generalized singular value σ 1 p 1 i.e. Ap p σ 1 pu 1 p and Bp p v 1 p where u 1 u 1 v 1 v 1 and. Proof: By the hypothesis there exist two unitary matrices U u 1 U 2 C m m V v 1 V 2 C l l and a nonsingular matrix Q Q 2 C n n such that U σ1 AQ and V 1 BQ 3 Λ 2 Σ 2 where σ 1 1 / σλ 2 Σ 2 and σ 1 >. Let Ãp Q Ap ã11 p ã ApQ 21 p ã 21 p à 22 p b11 Bp Q Bp p b21 p BpQ b21 p B22 p 4 5 3
4 where ã 11 p b 11 p R ã 11 σ 2 1 and b We now define the following vectorvalued functions fz w p ã 21 p + Ã22pz + wã 11 p + wã 21 p z 6 gz w p b 21 p + B 22 pz + w b 11 p + w b 21 p z 7 where f f 1... f n 1 T g g 1... g n 1 T z ζ 1... ζ n 1 T C n 1 w ω 1... ω n 1 T C n 1 p R N. Let f j ϕ j + iψ j ζ j ξ j + iη j g j ϕ j + i ψ j ω j ξ j + i η j j 1... n 1 and x ξ 1... ξ n 1 T R n 1 y η 1... η n 1 T R n 1 x ξ 1... ξ n 1 T R n 1 ỹ η 1... η n 1 T R n 1. It is clear that ϕ j x y x ỹ p ψ j x y x ỹ p ϕ j x y x ỹ p and ψ j x y x ỹ p are all real analytic functions of real variables x y x ỹ R n 1 and p B and ϕ j ψ j ϕ j ψj 8 for j 1... n 1. Notice that f 1... f n 1 g 1... g n 1 are all complex analytic functions of the complex variables ζ 1... ζ n 1 ω 1... ω n 1 for any p B. Thus we have 5 p.39 Theorem 8 det ϕ 1... ϕ n 1 ψ 1... ψ n 1 ϕ 1... ϕ n 1 ψ 1... ψ n 1 ξ 1... ξ n 1 η 1... η n 1 ξ 1... ξ n 1 η 1... η n 1 det f 1... f n 1 g 1... g n 1 2 ζ 1... ζ n 1 ω 1... ω n 1. This together with f1... f n 1 g 1... g n 1 I1 det det Ã22 I 1 B 22 ζ 1... ζ n 1 ω 1... ω n 1 zw ã 11 I n 1 b11 T I n 1 Λ T det 2 Λ 2 Σ T 2 Σ 2 σ1 2I detλ T 2 Λ n 1 I 2 σ1σ 2 T 2 Σ 2 n 1 implies that det ϕ 1... ϕ n 1 ψ 1... ψ n 1 ϕ 1... ϕ n 1 ψ 1... ψ n 1 ξ 1... ξ n 1 η 1... η n 1 ξ 1... ξ n 1 η 1... η n 1 4
5 where denotes the Kronecker product see for instance 9. Therefore by using the Implicit Function Theorem 18 Theorem 1.2 we know that the system of equations ϕ j x y x ỹ p ψ j x y x ỹ p ϕ j x y x ỹ p ψj x y x ỹ p for j 1... n 1 i.e. { fz w p has a unique real analytic solution gz w p 9 x xp y yp x xp ỹ ỹp i.e. z zp w wp in some neighbourhood B B of the origin where x y x ỹ i.e. z w 1 and deti n 1 wpzp p B. 11 Next we construct a simple generalized singular value of {Ap Bp} and an associated generalized singular vector set. From 11 it follows that the matrix 1 wp zp I n 1 is nonsingular for any p B. Thus we obtain by 4 and 5 for any p B 1 wp zp I n 1 Ãp 1 wp zp I n 1 a1 p A 2 p 12 and 1 wp where and zp I n 1 Bp 1 wp zp I n 1 a 1 p + Q 2 zp Ap Ap + Q 2 zp b1 p B 2 p ã 11 p + zp ã 21 p + ã 21 p zp + zp à 22 pzp b 1 p + Q 2 zp Bp Bp + Q 2 zp b 11 p + zp b21 p + b 21 p zp + zp B22 pzp with a 1 σ 2 1 and b 1 1. We observe that for sufficiently small B a 1 p > b 1 p > p B. ence we can define a positive valued function σ 1 : B R by σ 1 p a 1 p 1 2 b1 p 1 2 p B. 16 5
6 In addition let 1 p Q zp b 1 p 1 2 p B 17 u 1 p Ap p/σ 1 p v 1 p Bp p p B. 18 By 4 18 it is easy to know that the functions u 1 p v 1 p and p are such that Reu 1 p Imu 1 p Rev 1 p Imv 1 p Re p and Im p are all real analytic in B o with and Ap p σ 1 pu 1 p Bp p v 1 p u 1 p u 1 p v 1 p v 1 p 1 19 u 1 u 1 v 1 v 1. 2 By using the perturbation theorem for generalized singular values see for instance 16 it is easy to see that if the neighborhood B is small enough the generalized singular value σ 1 p 1 of {Ap Bp} such that σ 1 p 1 is simple and σ 1 p is a real analytic function of p in B and σ 1 σ 1. Theorem 3.2 Under the same assumptions as in Theorem 3.1 the following formulas for the simple generalized singular value σ 1 p 1 and the generalized singular vector set {u 1 p v 1 p p} defined in hold: σ1 p Re u 1 Ap σ 1 Re v 1 Bp 21 q1 p Ap Ap u1 p Q 2 Φ 1 Q 2 Bp Q 2 Φ 3 Q 2 Bp Re v1 u 1 + Q 2 Φ 2 U2 v 1 Q 2 Φ 4 V2 Bp 22 { Ap 1 q1 p + A σ 1 1 Ap Ap {U 2 Λ 2 Φ 1 Q 2 u 1 + U 2 Λ 2 Φ 2 U2 σ 1 1 Bp {U 2 Λ 2 Φ 3 Q 2 v 1 + U 2 Λ 2 Φ 4 V2 σ 1 { Ap + 1 σ 1 Re u 1 Ap σ1 p u 1 Bp } } } } u
7 v1 p q1 p Bp B + Ap Ap V 2 Σ 2 Φ 1 Q 2 V 2 Σ 2 Φ 3 Q 2 Bp + Bp u 1 + V 2 Σ 2 Φ 2 U2 Re v 1 V 2 Σ 2 Φ 4 V2 Bp v 1 Bp v σ 1 p Re u 2 Ap 1 σ 1 Re v 2 Bp Ap Ap Re q 1 σ 1 Bp Bp σ 1 Re q 1 u1 +Re Dk C u1 v1 D j + σ 1 Re σ 1 Re 1 σ 1 Re Re Re v1 u 1 u 1 +3σ 1 Re u 1 Sj C 1 D k + Sk C u1 1D j Ap Ap Ap v 1 Bp Re Re Re v 1 v 1 Re u 1 Ap Bp Bp v 1 Bp S j C 2 S k v1 25 for j k 1... N where Φ σ 2 1Σ T 2 Σ 2 Λ T 2 Λ 2 1 D j Φ 1 σ 1 Φ Φ 2 ΦΛ T 2 Φ 3 σ1φ 2 Φ 4 σ1φσ 2 T 2 Bp S j Ap Ap Bp 7
8 Q2 Φ 1 Q 2 Q 2 Φ 2 U2 C U 2 Φ 2 Q 1 2 σ 1 U 2 Λ 2 Φ 2 U2 C 2 Q2 Φ C 1 1 Q 2 Q 2 Φ 2 U2 V 2 Σ 2 Φ 1 Q 2 V 2 Σ 2 Φ 2 U2 Q2 Φ 3 Q 2 Q 2 Φ 4 V2 V 2 Σ 2 Φ 3 Q 2 V 2 Σ 2 Φ 4 V2 Proof: 1 By Theorem 3.1 see 19 and 2 we have. It follows from 26 and 19 that σ 1 p σ 1 p u 1 p Ap p p Ap u 1 p 26 1 v 1 p Bp p p Bp v 1 p. 27 u1 p σ 1 p u 1 p + u 1 p Ap p + u 1 p q1 p Ap and σ 1 p q1 p Ap Ap u 1 p + p u 1 p + σ 1 pu 1 p u1 p. 29 By27 and 19 we get v1 p v 1 p + v 1 p Bp p + v 1 p q1 p Bp 28 3 and q1 p Bp Bp v 1 p + p v 1 p + v 1 p v1 p. 31 Using and u 1 p u 1 p v 1 p v 1 p 1 we have σ 1 p 1 u 1 p Ap Ap p + p u 1 p σ 1p v 1 p Bp Bp p + p v 1 p + 1 u1 p Ap σ 1 pv 1 p Bp p 2 q1 p Ap u 1 p σ 1 pbp v 1 p Substituting p into 32 and using u 1 A σ 1v 1 B 1 n we obtain By Theorem 3.1 see and 17 we get Ap Ap p σ 2 1pBp Bp p 8
9 which yields σ 2 1 B B A A p Ap Ap A + A σ1 p 2σ 1 B B Bp Bp σ1 2 B + B. 33 This together with 3 1 and 17 gives rise to σ1 2Σ 2 Σ 2 Λ 2 Λ zp 2 σ1 Ap σ 1 Q { Bp σ1 2 Q u 1 + v 1 + Λ 2 1 Σ 2 Ap U V Bp σ1 p 2σ 1 } 1 and thus zp σ 2 1Σ T 2 Σ 2 Λ T 2 Λ 2 1 σ 1 Q 2 σ 2 1σ 2 1Σ T 2 Σ 2 Λ T 2 Λ 2 1 Q 2 Ap Bp u 1 + Λ 2 U2 v 1 + Σ 2 V2 Ap Bp. 34 Moreover we have by 15 and 3 b1 p 2Re v 1 Bp 35 and using 17 we get q1 p Combining and 36 yields From 19 we obtain u1 p zp Q Ap 1 q1 p + A σ 1 9 b1 p. 36 σ1 p u 1.
10 This together with and the relation AQ 2 U 2 Λ 2 yields By using 19 again we obtain v1 p q1 p Bp B + 37 which together with 22 and the relation BQ 2 V 2 Σ 2 gives rise to By 32 we have Re 2 σ 1 p u1 p +Re σ 1 Re σ 1 Re u 1 Ap v1 p v 1 Ap Bp u1 p +Re σ1 p v1 p σ 1 Re q1 p Bp A Re + Re q1 p q1 p u 2 Ap 1 σ1 p Bp Re v1 2 Bp σ 1 Re v1 q1 p B q1 p B + Re u 1 v 1 σ 1 Re Combining it with and 24 yields 25. Ap v 1 Bp q1 p q1 p We remark that as in we derive our sensitivity results based on the Implicit Function Theorem and the definition of the generalized singular value decomposition. Also if Ap R m n Bp R l n the first order partial derivatives related to the simple generalized singular value σ 1 p 1 and the generalized singular vector set {u 1 p v 1 p p} of {Ap Bp} in Theorem 3.2 are same as in 7 Theorem 2.1 under the assumption that the generalized singular value σ 1 1 of {A B} is simple. On the other hand suppose that m n. Let Bp I n for all p R N. By 3 there exist two unitary matrices U u 1 U 2 C m m V v 1 V 2 C n n and a nonsingular matrix Q V C n n such that U T σ1 AV and V BQ V 1 V 38 Λ 2 Σ 2 1.
11 σ 2... Λ 2 σ n Rm 1 n 1 Σ 2 I n 1 where σ 2... σ n and < σ 1 σ j for j 2... n. That is σ 1 is a simple nonzero singular value of A v 1 C n and u 1 C m are associated unit right and unit left singular vectors respectively. By using the unitarity of U we have Ap Ap v 1 UU { Ap u 1 Also it is easy to check that v 1 } v 1 u 1 + U 2 U2 Ap v I m 1 + Λ 2 σ 2 1I n 1 Λ T 2 Λ 2 1 Λ T 2 σ 2 1σ 2 1I m 1 Λ 2 Λ T Therefore by using Theorems 3.1 and and 4 it is easy to derive the same results as in 21 on the analyticity of simple nonzero singular values of a matrix analytically dependent on several parameters. Corollary 3.3 Let p R N and Ap C m n m n. Suppose that ReAp and ImAp are real analytic matrix-valued functions of p in some neighborhood B of the origin. If σ 1 > is a simple nonzero singular value of A and there exist two unit vectors u 1 C m and v 1 C n such that u 1 and v 1 are associated left and right singular vectors respectively i.e. there exist two unitary matrices U u 1 U 2 C m m and V v 1 V 2 C n n such that the first equality of 38 holds then 1 there exists a simple singular value σ 1 p > of Ap such that σ 1 p is a real analytic function of p in some neighborhood B B of the origin and σ 1 σ 1. 2 there exist unit vectors u 1 p C m and v 1 p C n such that Reu 1 p Imu 1 p Rev 1 p and Imv 1 p are all real analytic functions of p in B and u 1 p and v 1 p are the left and right singular vectors of Ap corresponding to the simple singular value σ 1 p i.e. Apv 1 p σ 1 pu 1 p and Ap u 1 p σ 1 pv 1 p where u 1 u 1 and v 1 v 1. Moreover the simple nonzero singular value σ 1 p is given by σ 1 p 1 ã 11 p + zp ã 21 p + ã 21 p zp + zp 2 Ã 22 pzp 1 + zp zp
12 for all p B where ã 11 p ã z1 p à 22 p are given by V Ap ã11 p ã ApV : 21 p ã 21 p à 22 p p B and zp R n 1 is the real analytic solution of fz z p in B where fz w p is defined in 6 z and deti n 1 + zpzp for all p B. In addition the associated unit singular vectors v 1 p and u 1 p are given by v 1 p V 1 zp 1 + zp zp 1 2 u 1 p Apv 1 p/σ 1 p p B. 42 Finally the following formulas for the simple nonzero singular value σ 1 p and the associated singular vectors v 1 p and u 1 p defined by hold: σ1 p Ap Re u 1 v 1 43 v1 p Ap Ap V 2 Φ 1 V2 Φ T 2 U2 u1 44 v 1 u1 p U 2 Φ 2 V 2 + i Im u 1 σ 1 Ap Ap Φ 3 U2 Ap u1 v 1 u 1 45 v 1 2 σ 1 p Re +Re u 1 2 Ap u1 v 1 v 1 V2 Φ 1 V2 V 2 Φ T 2 U 2 U 2 Φ 2 V2 U 2 Φ 3 U2 + 1 Ap Im u 1 σ 1 Ap Ap Ap v 1 Im u 1 Ap Ap u1 v 1 v 1 46 where j k 1... N u 1 v 1 U 2 and V 2 are defined by 38 and Φ 1 σ 1 σ 2 1I n 1 Λ T 2 Λ 2 1 Φ 2 Λ 2 σ 2 1I n 1 Λ T 2 Λ 2 1 Φ 3 σ 1 σ 2 1I m 1 Λ 2 Λ T 2 1 in which Λ 2 is defined by
13 4 Applications In this section we give some examples to show that our results are useful for computing the sensitivity and the second order perturbation expansions of simple nonzero finite generalized singular values of a complex matrix pair analytically dependent on several parameters. Based on Theorems 3.1 and 3.2 we first define the sensitivity of simple nonzero finite generalized singular values as follows 7. Definition 4.1 Let p p 1... p N T R N Ap C m n and Bp C l n. Suppose that Ap Bp has full row rank and ReAp ReBp ImAp and ImAp are real analytic matrix-valued functions of p in some neighborhood B of the origin. If {A B} has the GSVD 3 then the quantity σ1 p s pj σ 1 47 is called the sensitivity of the simple nonzero finite generalized singular value σ 1 1 with respect to the parameter p j the quantity s pi1 p i2...p im σ 1 m s 2 p ik σ 1 48 is called the sensitivity of the simple nonzero finite generalized singular value σ 1 1 with respect to the parameter p i1 p i2... p im the quantity s p σ 1 N s 2 p j σ 1 49 is called the sensitivity of the simple nonzero finite generalized singular value σ 1 1. j1 k1 Example 4.2 Let Ap 3 + 2p 1 + 4p 2 + ip 1 p 1 + ip 2 p 2 2 p 2 p p 1 5 and Bp where p p 1 p 2 T R 2 and i p 1 + ip 2 p 1 p p 1 p p 2 51 We observe that A B
14 where σ is a simple nonzero finite generalized singular value of {A B} and Using 5 and 51 we get Ap p 1 Bp p 1 By and 49 u 1 v 1 1 T. 2 + i Ap p 2 Bp p 2 s p1 3 2 s p2 3 1 s p i i 1 1 Next we present an example to show how the second order perturbation expansions of simple nonzero finite generalized singular values work.. Example 4.3 Let Ap p 1 +ip and Bp 5 3 p 1 +ip where p p 1 p 2 T R 2. Obviously the matrix pair {A B} is given by 6 2 A and B 2 6 which has the following GSVD: where Thus one has U AQ U V V 1 BQ 2 Q σ 1 2 Λ 2 Σ 2 2 Φ 1 Φ Φ Φ u T U T v T V T T Q T 14.
15 Ap p 1 Bp p 1 2 Ap p Ap p 1 p 2 2 Ap p i 1 Using 21 and 25 a simple calculation yields σ1 p p 1.5 Ap p 2 Bp p 2 2 Bp p Bp p 1 p 2 2 Bp p 2 2 σ1 p p 2 i 3i 3 3i 3. and 2 σ 1 p p Therefore σ 1 p has the expansion 2 σ 1 p p 1 p 2 2 σ 1 p p σ 1 p 2.5p p p O p 3 in a neighborhood of the origin where denotes the Euclidean vector norm. These examples show that our results are effective for evaluating the sensitivity and the second order Taylor expansions of simple nonzero finite generalized singular values. 5 Future work In this paper we give the first order and second order partial derivatives of simple nonzero finite generalized singular values of a complex matrix pair analytically dependent on several parameters. These results may be used to investigate the effectiveness of the GSVD-based methods for practical applications. An interesting problem is to discuss the sensitivity analysis and the second order perturbation expansions of zero generalized singular values infinite generalized singular values and multiple generalized singular values analytically dependent on several parameters. This needs further study. Acknowledgments We would like to thank the referees for their useful suggestions. 15
16 References 1 Z. J. Bai and J. W. Demmel Computing the generalized singular value decomposition SIAM J. Sci. Comput pp T. Betcke The generalized singular value decomposition and the method of particular solutions SIAM J. Sci. Comput pp K. Bhuyan S. B. Singh and P. K. Bhuyan Application of generalized singular value decomposition to ionospheric tomography Annales Geophysicae pp Å. Björck. Numerical Methods for Least Squares Problems SIAM Philadelphia S. Bochner and W. T. Martin Several Complex Variables Princeton X. S. Chen and W. Li A note on backward error analysis of the generalized singular value decomposition SIAM J. Matrix Anal pp X. S. Chen and W. Li Sensitivity analysis for the generalized singular value decomposition Numer. Linear Algebra Appl pp G.. Golub and C. F. Van Loan Matrix Computations 3rd edition Johns opkins University Press Baltimore and London R. A. orn and C. R. Johnson Topics in Matrix Analysis Cambridge University Press: Cambridge MA P. owland M. Jeon and. Park Structure preserving dimension reduction for clustered text data based on the generalized singular value decomposition SIAM J. Matrix Anal. Appl pp P. owland and. Park Generalizing discriminant analysis using the generalized singular value decomposition IEEE Transactions on PAMI pp B. Kågström The generalized singular value decomposition and the general A λb- problem BIT pp C. C. Paige and M. A. Saunders Towards a generalized singular value decomposition SIAM J. Numer. Anal pp C.. Park and. Park A relationship between linear discriminant analysis and the generalized minimum squared error solution SIAM J. Matrix Anal. Appl pp G. W. Stewart and J. G. Sun Matrix Perturbation Theory Academic Press Boston J.-G. Sun Perturbation analysis for the generalized eigenvalue and the generalized singular value problem Lecture Notes in Mathematics pp J. G. Sun Perturbation analysis for the generalized singular value problem SIAM J. Numer. Anal pp
17 18 J. G. Sun Eigenvalues and eigenvectors of a matirx dependent on several parameters J. Comput. Math pp J. G. Sun Sensitivity analysis of multiple eigenvalues I J. Comput. Math pp J. G. Sun Sensitivity analysis of multiple eigenvalues II J. Comput. Math pp J. G. Sun A note on simple non-zero singular values J. Comput. Math pp J. G. Sun Sensitivity analysis of zero singular values and multiple singular values J. Comput. Math pp J. G. Sun Multiple eigenvalue sensitivity analysis Linear Algebra Appl. 137/ pp J. G. Sun Condition number and backward error for the generalized singular value decomposition SIAM J. Matrix Anal. Appl pp C. F. Van Loan Generalizing the singular value decomposition SIAM J. Numer. Anal pp Q. Xie and. Dai On the sensitivity of multiple eigenvalues of nonsymmetric matrix pencil Linear Algebra Appl pp
arxiv: v1 [math.na] 1 Sep 2018
On the perturbation of an L -orthogonal projection Xuefeng Xu arxiv:18090000v1 [mathna] 1 Sep 018 September 5 018 Abstract The L -orthogonal projection is an important mathematical tool in scientific computing
More informationA Multi-Step Hybrid Method for Multi-Input Partial Quadratic Eigenvalue Assignment with Time Delay
A Multi-Step Hybrid Method for Multi-Input Partial Quadratic Eigenvalue Assignment with Time Delay Zheng-Jian Bai Mei-Xiang Chen Jin-Ku Yang April 14, 2012 Abstract A hybrid method was given by Ram, Mottershead,
More informationMultiplicative Perturbation Bounds of the Group Inverse and Oblique Projection
Filomat 30: 06, 37 375 DOI 0.98/FIL67M Published by Faculty of Sciences Mathematics, University of Niš, Serbia Available at: http://www.pmf.ni.ac.rs/filomat Multiplicative Perturbation Bounds of the Group
More informationThe semi-convergence of GSI method for singular saddle point problems
Bull. Math. Soc. Sci. Math. Roumanie Tome 57(05 No., 04, 93 00 The semi-convergence of GSI method for singular saddle point problems by Shu-Xin Miao Abstract Recently, Miao Wang considered the GSI method
More informationCharacterization of half-radial matrices
Characterization of half-radial matrices Iveta Hnětynková, Petr Tichý Faculty of Mathematics and Physics, Charles University, Sokolovská 83, Prague 8, Czech Republic Abstract Numerical radius r(a) is the
More informationTHE PERTURBATION BOUND FOR THE SPECTRAL RADIUS OF A NON-NEGATIVE TENSOR
THE PERTURBATION BOUND FOR THE SPECTRAL RADIUS OF A NON-NEGATIVE TENSOR WEN LI AND MICHAEL K. NG Abstract. In this paper, we study the perturbation bound for the spectral radius of an m th - order n-dimensional
More informationA Regularized Directional Derivative-Based Newton Method for Inverse Singular Value Problems
A Regularized Directional Derivative-Based Newton Method for Inverse Singular Value Problems Wei Ma Zheng-Jian Bai September 18, 2012 Abstract In this paper, we give a regularized directional derivative-based
More informationELA THE OPTIMAL PERTURBATION BOUNDS FOR THE WEIGHTED MOORE-PENROSE INVERSE. 1. Introduction. Let C m n be the set of complex m n matrices and C m n
Electronic Journal of Linear Algebra ISSN 08-380 Volume 22, pp. 52-538, May 20 THE OPTIMAL PERTURBATION BOUNDS FOR THE WEIGHTED MOORE-PENROSE INVERSE WEI-WEI XU, LI-XIA CAI, AND WEN LI Abstract. In this
More informationPERTURBATION ANALYSIS OF THE EIGENVECTOR MATRIX AND SINGULAR VECTOR MATRICES. Xiao Shan Chen, Wen Li and Wei Wei Xu 1. INTRODUCTION A = UΛU H,
TAIWAESE JOURAL O MATHEMATICS Vol. 6, o., pp. 79-94, ebruary 0 This paper is available online at http://tjm.math.ntu.edu.tw PERTURBATIO AALYSIS O THE EIGEVECTOR MATRIX AD SIGULAR VECTOR MATRICES Xiao Shan
More informationWe first repeat some well known facts about condition numbers for normwise and componentwise perturbations. Consider the matrix
BIT 39(1), pp. 143 151, 1999 ILL-CONDITIONEDNESS NEEDS NOT BE COMPONENTWISE NEAR TO ILL-POSEDNESS FOR LEAST SQUARES PROBLEMS SIEGFRIED M. RUMP Abstract. The condition number of a problem measures the sensitivity
More informationThe reflexive re-nonnegative definite solution to a quaternion matrix equation
Electronic Journal of Linear Algebra Volume 17 Volume 17 28 Article 8 28 The reflexive re-nonnegative definite solution to a quaternion matrix equation Qing-Wen Wang wqw858@yahoo.com.cn Fei Zhang Follow
More informationThe Eigenvalue Shift Technique and Its Eigenstructure Analysis of a Matrix
The Eigenvalue Shift Technique and Its Eigenstructure Analysis of a Matrix Chun-Yueh Chiang Center for General Education, National Formosa University, Huwei 632, Taiwan. Matthew M. Lin 2, Department of
More informationThe Solvability Conditions for the Inverse Eigenvalue Problem of Hermitian and Generalized Skew-Hamiltonian Matrices and Its Approximation
The Solvability Conditions for the Inverse Eigenvalue Problem of Hermitian and Generalized Skew-Hamiltonian Matrices and Its Approximation Zheng-jian Bai Abstract In this paper, we first consider the inverse
More informationIyad T. Abu-Jeib and Thomas S. Shores
NEW ZEALAND JOURNAL OF MATHEMATICS Volume 3 (003, 9 ON PROPERTIES OF MATRIX I ( OF SINC METHODS Iyad T. Abu-Jeib and Thomas S. Shores (Received February 00 Abstract. In this paper, we study the determinant
More informationThe Skew-Symmetric Ortho-Symmetric Solutions of the Matrix Equations A XA = D
International Journal of Algebra, Vol. 5, 2011, no. 30, 1489-1504 The Skew-Symmetric Ortho-Symmetric Solutions of the Matrix Equations A XA = D D. Krishnaswamy Department of Mathematics Annamalai University
More informationOn the Solution of Constrained and Weighted Linear Least Squares Problems
International Mathematical Forum, 1, 2006, no. 22, 1067-1076 On the Solution of Constrained and Weighted Linear Least Squares Problems Mohammedi R. Abdel-Aziz 1 Department of Mathematics and Computer Science
More information2 Computing complex square roots of a real matrix
On computing complex square roots of real matrices Zhongyun Liu a,, Yulin Zhang b, Jorge Santos c and Rui Ralha b a School of Math., Changsha University of Science & Technology, Hunan, 410076, China b
More informationELA THE MINIMUM-NORM LEAST-SQUARES SOLUTION OF A LINEAR SYSTEM AND SYMMETRIC RANK-ONE UPDATES
Volume 22, pp. 480-489, May 20 THE MINIMUM-NORM LEAST-SQUARES SOLUTION OF A LINEAR SYSTEM AND SYMMETRIC RANK-ONE UPDATES XUZHOU CHEN AND JUN JI Abstract. In this paper, we study the Moore-Penrose inverse
More informationETNA Kent State University
Electronic Transactions on Numerical Analysis. Volume 1, pp. 1-11, 8. Copyright 8,. ISSN 168-961. MAJORIZATION BOUNDS FOR RITZ VALUES OF HERMITIAN MATRICES CHRISTOPHER C. PAIGE AND IVO PANAYOTOV Abstract.
More informationA Note on Eigenvalues of Perturbed Hermitian Matrices
A Note on Eigenvalues of Perturbed Hermitian Matrices Chi-Kwong Li Ren-Cang Li July 2004 Let ( H1 E A = E H 2 Abstract and à = ( H1 H 2 be Hermitian matrices with eigenvalues λ 1 λ k and λ 1 λ k, respectively.
More informationBOUNDS OF MODULUS OF EIGENVALUES BASED ON STEIN EQUATION
K Y BERNETIKA VOLUM E 46 ( 2010), NUMBER 4, P AGES 655 664 BOUNDS OF MODULUS OF EIGENVALUES BASED ON STEIN EQUATION Guang-Da Hu and Qiao Zhu This paper is concerned with bounds of eigenvalues of a complex
More informationLinear and Multilinear Algebra. Linear maps preserving rank of tensor products of matrices
Linear maps preserving rank of tensor products of matrices Journal: Manuscript ID: GLMA-0-0 Manuscript Type: Original Article Date Submitted by the Author: -Aug-0 Complete List of Authors: Zheng, Baodong;
More informationKey words. conjugate gradients, normwise backward error, incremental norm estimation.
Proceedings of ALGORITMY 2016 pp. 323 332 ON ERROR ESTIMATION IN THE CONJUGATE GRADIENT METHOD: NORMWISE BACKWARD ERROR PETR TICHÝ Abstract. Using an idea of Duff and Vömel [BIT, 42 (2002), pp. 300 322
More informationYimin Wei a,b,,1, Xiezhang Li c,2, Fanbin Bu d, Fuzhen Zhang e. Abstract
Linear Algebra and its Applications 49 (006) 765 77 wwwelseviercom/locate/laa Relative perturbation bounds for the eigenvalues of diagonalizable and singular matrices Application of perturbation theory
More informationThe Jordan forms of AB and BA
Electronic Journal of Linear Algebra Volume 18 Volume 18 (29) Article 25 29 The Jordan forms of AB and BA Ross A. Lippert ross.lippert@gmail.com Gilbert Strang Follow this and additional works at: http://repository.uwyo.edu/ela
More informationModule 6.6: nag nsym gen eig Nonsymmetric Generalized Eigenvalue Problems. Contents
Eigenvalue and Least-squares Problems Module Contents Module 6.6: nag nsym gen eig Nonsymmetric Generalized Eigenvalue Problems nag nsym gen eig provides procedures for solving nonsymmetric generalized
More informationTHE INVERSE PROBLEM OF CENTROSYMMETRIC MATRICES WITH A SUBMATRIX CONSTRAINT 1) 1. Introduction
Journal of Computational Mathematics, Vol22, No4, 2004, 535 544 THE INVERSE PROBLEM OF CENTROSYMMETRIC MATRICES WITH A SUBMATRIX CONSTRAINT 1 Zhen-yun Peng Department of Mathematics, Hunan University of
More informationThe Hermitian R-symmetric Solutions of the Matrix Equation AXA = B
International Journal of Algebra, Vol. 6, 0, no. 9, 903-9 The Hermitian R-symmetric Solutions of the Matrix Equation AXA = B Qingfeng Xiao Department of Basic Dongguan olytechnic Dongguan 53808, China
More informationOn the eigenvalues of specially low-rank perturbed matrices
On the eigenvalues of specially low-rank perturbed matrices Yunkai Zhou April 12, 2011 Abstract We study the eigenvalues of a matrix A perturbed by a few special low-rank matrices. The perturbation is
More informationSEMI-CONVERGENCE ANALYSIS OF THE INEXACT UZAWA METHOD FOR SINGULAR SADDLE POINT PROBLEMS
REVISTA DE LA UNIÓN MATEMÁTICA ARGENTINA Vol. 53, No. 1, 2012, 61 70 SEMI-CONVERGENCE ANALYSIS OF THE INEXACT UZAWA METHOD FOR SINGULAR SADDLE POINT PROBLEMS JIAN-LEI LI AND TING-ZHU HUANG Abstract. Recently,
More informationTRUNCATED TOEPLITZ OPERATORS ON FINITE DIMENSIONAL SPACES
TRUNCATED TOEPLITZ OPERATORS ON FINITE DIMENSIONAL SPACES JOSEPH A. CIMA, WILLIAM T. ROSS, AND WARREN R. WOGEN Abstract. In this paper, we study the matrix representations of compressions of Toeplitz operators
More informationThe reflexive and anti-reflexive solutions of the matrix equation A H XB =C
Journal of Computational and Applied Mathematics 200 (2007) 749 760 www.elsevier.com/locate/cam The reflexive and anti-reflexive solutions of the matrix equation A H XB =C Xiang-yang Peng a,b,, Xi-yan
More informationLecture 1: Review of linear algebra
Lecture 1: Review of linear algebra Linear functions and linearization Inverse matrix, least-squares and least-norm solutions Subspaces, basis, and dimension Change of basis and similarity transformations
More informationTotal Least Squares Approach in Regression Methods
WDS'08 Proceedings of Contributed Papers, Part I, 88 93, 2008. ISBN 978-80-7378-065-4 MATFYZPRESS Total Least Squares Approach in Regression Methods M. Pešta Charles University, Faculty of Mathematics
More informationTHE RELATION BETWEEN THE QR AND LR ALGORITHMS
SIAM J. MATRIX ANAL. APPL. c 1998 Society for Industrial and Applied Mathematics Vol. 19, No. 2, pp. 551 555, April 1998 017 THE RELATION BETWEEN THE QR AND LR ALGORITHMS HONGGUO XU Abstract. For an Hermitian
More informationNumerical Methods for Solving Large Scale Eigenvalue Problems
Peter Arbenz Computer Science Department, ETH Zürich E-mail: arbenz@inf.ethz.ch arge scale eigenvalue problems, Lecture 2, February 28, 2018 1/46 Numerical Methods for Solving Large Scale Eigenvalue Problems
More informationThe Singular Value Decomposition
The Singular Value Decomposition Philippe B. Laval KSU Fall 2015 Philippe B. Laval (KSU) SVD Fall 2015 1 / 13 Review of Key Concepts We review some key definitions and results about matrices that will
More informationAMS526: Numerical Analysis I (Numerical Linear Algebra)
AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 1: Course Overview & Matrix-Vector Multiplication Xiangmin Jiao SUNY Stony Brook Xiangmin Jiao Numerical Analysis I 1 / 20 Outline 1 Course
More informationSolutions to the generalized Sylvester matrix equations by a singular value decomposition
Journal of Control Theory Applications 2007 5 (4) 397 403 DOI 101007/s11768-006-6113-0 Solutions to the generalized Sylvester matrix equations by a singular value decomposition Bin ZHOU Guangren DUAN (Center
More informationOUTLINE 1. Introduction 1.1 Notation 1.2 Special matrices 2. Gaussian Elimination 2.1 Vector and matrix norms 2.2 Finite precision arithmetic 2.3 Fact
Computational Linear Algebra Course: (MATH: 6800, CSCI: 6800) Semester: Fall 1998 Instructors: { Joseph E. Flaherty, aherje@cs.rpi.edu { Franklin T. Luk, luk@cs.rpi.edu { Wesley Turner, turnerw@cs.rpi.edu
More informationPositive definite preserving linear transformations on symmetric matrix spaces
Positive definite preserving linear transformations on symmetric matrix spaces arxiv:1008.1347v1 [math.ra] 7 Aug 2010 Huynh Dinh Tuan-Tran Thi Nha Trang-Doan The Hieu Hue Geometry Group College of Education,
More informationMaths for Signals and Systems Linear Algebra in Engineering
Maths for Signals and Systems Linear Algebra in Engineering Lectures 13 15, Tuesday 8 th and Friday 11 th November 016 DR TANIA STATHAKI READER (ASSOCIATE PROFFESOR) IN SIGNAL PROCESSING IMPERIAL COLLEGE
More informationOn condition numbers for the canonical generalized polar decompostion of real matrices
Electronic Journal of Linear Algebra Volume 26 Volume 26 (2013) Article 57 2013 On condition numbers for the canonical generalized polar decompostion of real matrices Ze-Jia Xie xiezejia2012@gmail.com
More information2. Linear algebra. matrices and vectors. linear equations. range and nullspace of matrices. function of vectors, gradient and Hessian
FE661 - Statistical Methods for Financial Engineering 2. Linear algebra Jitkomut Songsiri matrices and vectors linear equations range and nullspace of matrices function of vectors, gradient and Hessian
More informationLinear Algebra Methods for Data Mining
Linear Algebra Methods for Data Mining Saara Hyvönen, Saara.Hyvonen@cs.helsinki.fi Spring 2007 1. Basic Linear Algebra Linear Algebra Methods for Data Mining, Spring 2007, University of Helsinki Example
More informationAbed Elhashash, Uriel G. Rothblum, and Daniel B. Szyld. Report August 2009
Paths of matrices with the strong Perron-Frobenius property converging to a given matrix with the Perron-Frobenius property Abed Elhashash, Uriel G. Rothblum, and Daniel B. Szyld Report 09-08-14 August
More informationETNA Kent State University
Electronic Transactions on Numerical Analysis Volume 2 pp 138-153 September 1994 Copyright 1994 ISSN 1068-9613 ETNA etna@mcskentedu ON THE PERIODIC QUOTIENT SINGULAR VALUE DECOMPOSITION JJ HENCH Abstract
More informationContour integral solutions of Sylvester-type matrix equations
Contour integral solutions of Sylvester-type matrix equations Harald K. Wimmer Mathematisches Institut, Universität Würzburg, 97074 Würzburg, Germany Abstract The linear matrix equations AXB CXD = E, AX
More informationLinear Algebra and its Applications
Linear Algebra and its Applications 433 (2010) 476 482 Contents lists available at ScienceDirect Linear Algebra and its Applications journal homepage: www.elsevier.com/locate/laa Nonsingularity of the
More informationAN INVERSE EIGENVALUE PROBLEM AND AN ASSOCIATED APPROXIMATION PROBLEM FOR GENERALIZED K-CENTROHERMITIAN MATRICES
AN INVERSE EIGENVALUE PROBLEM AND AN ASSOCIATED APPROXIMATION PROBLEM FOR GENERALIZED K-CENTROHERMITIAN MATRICES ZHONGYUN LIU AND HEIKE FAßBENDER Abstract: A partially described inverse eigenvalue problem
More informationSTRUCTURE PRESERVING DIMENSION REDUCTION FOR CLUSTERED TEXT DATA BASED ON THE GENERALIZED SINGULAR VALUE DECOMPOSITION
SIAM J. MARIX ANAL. APPL. Vol. 25, No. 1, pp. 165 179 c 2003 Society for Industrial and Applied Mathematics SRUCURE PRESERVING DIMENSION REDUCION FOR CLUSERED EX DAA BASED ON HE GENERALIZED SINGULAR VALUE
More informationMultiplicative Perturbation Analysis for QR Factorizations
Multiplicative Perturbation Analysis for QR Factorizations Xiao-Wen Chang Ren-Cang Li Technical Report 011-01 http://www.uta.edu/math/preprint/ Multiplicative Perturbation Analysis for QR Factorizations
More informationMath 315: Linear Algebra Solutions to Assignment 7
Math 5: Linear Algebra s to Assignment 7 # Find the eigenvalues of the following matrices. (a.) 4 0 0 0 (b.) 0 0 9 5 4. (a.) The characteristic polynomial det(λi A) = (λ )(λ )(λ ), so the eigenvalues are
More informationWavelets and Linear Algebra
Wavelets and Linear Algebra 4(1) (2017) 43-51 Wavelets and Linear Algebra Vali-e-Asr University of Rafsanan http://walavruacir Some results on the block numerical range Mostafa Zangiabadi a,, Hamid Reza
More informationAnn. Funct. Anal. 5 (2014), no. 2, A nnals of F unctional A nalysis ISSN: (electronic) URL:
Ann Funct Anal 5 (2014), no 2, 127 137 A nnals of F unctional A nalysis ISSN: 2008-8752 (electronic) URL:wwwemisde/journals/AFA/ THE ROOTS AND LINKS IN A CLASS OF M-MATRICES XIAO-DONG ZHANG This paper
More informationA Backward Stable Hyperbolic QR Factorization Method for Solving Indefinite Least Squares Problem
A Backward Stable Hyperbolic QR Factorization Method for Solving Indefinite Least Suares Problem Hongguo Xu Dedicated to Professor Erxiong Jiang on the occasion of his 7th birthday. Abstract We present
More informationMS&E 318 (CME 338) Large-Scale Numerical Optimization
Stanford University, Management Science & Engineering (and ICME MS&E 38 (CME 338 Large-Scale Numerical Optimization Course description Instructor: Michael Saunders Spring 28 Notes : Review The course teaches
More informationOn rank one perturbations of Hamiltonian system with periodic coefficients
On rank one perturbations of Hamiltonian system with periodic coefficients MOUHAMADOU DOSSO Université FHB de Cocody-Abidjan UFR Maths-Info., BP 58 Abidjan, CÔTE D IVOIRE mouhamadou.dosso@univ-fhb.edu.ci
More informationPOSITIVE SEMIDEFINITE INTERVALS FOR MATRIX PENCILS
POSITIVE SEMIDEFINITE INTERVALS FOR MATRIX PENCILS RICHARD J. CARON, HUIMING SONG, AND TIM TRAYNOR Abstract. Let A and E be real symmetric matrices. In this paper we are concerned with the determination
More informationMajorization for Changes in Ritz Values and Canonical Angles Between Subspaces (Part I and Part II)
1 Majorization for Changes in Ritz Values and Canonical Angles Between Subspaces (Part I and Part II) Merico Argentati (speaker), Andrew Knyazev, Ilya Lashuk and Abram Jujunashvili Department of Mathematics
More informationS.F. Xu (Department of Mathematics, Peking University, Beijing)
Journal of Computational Mathematics, Vol.14, No.1, 1996, 23 31. A SMALLEST SINGULAR VALUE METHOD FOR SOLVING INVERSE EIGENVALUE PROBLEMS 1) S.F. Xu (Department of Mathematics, Peking University, Beijing)
More informationRESCALING THE GSVD WITH APPLICATION TO ILL-POSED PROBLEMS
RESCALING THE GSVD WITH APPLICATION TO ILL-POSED PROBLEMS L. DYKES, S. NOSCHESE, AND L. REICHEL Abstract. The generalized singular value decomposition (GSVD) of a pair of matrices expresses each matrix
More informationACI-matrices all of whose completions have the same rank
ACI-matrices all of whose completions have the same rank Zejun Huang, Xingzhi Zhan Department of Mathematics East China Normal University Shanghai 200241, China Abstract We characterize the ACI-matrices
More informationChap 3. Linear Algebra
Chap 3. Linear Algebra Outlines 1. Introduction 2. Basis, Representation, and Orthonormalization 3. Linear Algebraic Equations 4. Similarity Transformation 5. Diagonal Form and Jordan Form 6. Functions
More informationELA ON THE GROUP INVERSE OF LINEAR COMBINATIONS OF TWO GROUP INVERTIBLE MATRICES
ON THE GROUP INVERSE OF LINEAR COMBINATIONS OF TWO GROUP INVERTIBLE MATRICES XIAOJI LIU, LINGLING WU, AND JULIO BENíTEZ Abstract. In this paper, some formulas are found for the group inverse of ap +bq,
More information1 Singular Value Decomposition and Principal Component
Singular Value Decomposition and Principal Component Analysis In these lectures we discuss the SVD and the PCA, two of the most widely used tools in machine learning. Principal Component Analysis (PCA)
More informationMINIMAL NORMAL AND COMMUTING COMPLETIONS
INTERNATIONAL JOURNAL OF INFORMATION AND SYSTEMS SCIENCES Volume 4, Number 1, Pages 5 59 c 8 Institute for Scientific Computing and Information MINIMAL NORMAL AND COMMUTING COMPLETIONS DAVID P KIMSEY AND
More informationMULTIPLICATIVE PERTURBATION ANALYSIS FOR QR FACTORIZATIONS. Xiao-Wen Chang. Ren-Cang Li. (Communicated by Wenyu Sun)
NUMERICAL ALGEBRA, doi:10.3934/naco.011.1.301 CONTROL AND OPTIMIZATION Volume 1, Number, June 011 pp. 301 316 MULTIPLICATIVE PERTURBATION ANALYSIS FOR QR FACTORIZATIONS Xiao-Wen Chang School of Computer
More informationWeaker assumptions for convergence of extended block Kaczmarz and Jacobi projection algorithms
DOI: 10.1515/auom-2017-0004 An. Şt. Univ. Ovidius Constanţa Vol. 25(1),2017, 49 60 Weaker assumptions for convergence of extended block Kaczmarz and Jacobi projection algorithms Doina Carp, Ioana Pomparău,
More informationOn the Modification of an Eigenvalue Problem that Preserves an Eigenspace
Purdue University Purdue e-pubs Department of Computer Science Technical Reports Department of Computer Science 2009 On the Modification of an Eigenvalue Problem that Preserves an Eigenspace Maxim Maumov
More informationPreliminary/Qualifying Exam in Numerical Analysis (Math 502a) Spring 2012
Instructions Preliminary/Qualifying Exam in Numerical Analysis (Math 502a) Spring 2012 The exam consists of four problems, each having multiple parts. You should attempt to solve all four problems. 1.
More informationON 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 informationTwo-stage Methods for Linear Discriminant Analysis: Equivalent Results at a Lower Cost
Two-stage Methods for Linear Discriminant Analysis: Equivalent Results at a Lower Cost Peg Howland and Haesun Park Abstract Linear discriminant analysis (LDA) has been used for decades to extract features
More informationSensitivity analysis of the differential matrix Riccati equation based on the associated linear differential system
Advances in Computational Mathematics 7 (1997) 295 31 295 Sensitivity analysis of the differential matrix Riccati equation based on the associated linear differential system Mihail Konstantinov a and Vera
More informationThe skew-symmetric orthogonal solutions of the matrix equation AX = B
Linear Algebra and its Applications 402 (2005) 303 318 www.elsevier.com/locate/laa The skew-symmetric orthogonal solutions of the matrix equation AX = B Chunjun Meng, Xiyan Hu, Lei Zhang College of Mathematics
More informationOn the loss of orthogonality in the Gram-Schmidt orthogonalization process
CERFACS Technical Report No. TR/PA/03/25 Luc Giraud Julien Langou Miroslav Rozložník On the loss of orthogonality in the Gram-Schmidt orthogonalization process Abstract. In this paper we study numerical
More informationPrincipal Angles Between Subspaces and Their Tangents
MITSUBISI ELECTRIC RESEARC LABORATORIES http://wwwmerlcom Principal Angles Between Subspaces and Their Tangents Knyazev, AV; Zhu, P TR2012-058 September 2012 Abstract Principal angles between subspaces
More informationUniversity of Colorado at Denver Mathematics Department Applied Linear Algebra Preliminary Exam With Solutions 16 January 2009, 10:00 am 2:00 pm
University of Colorado at Denver Mathematics Department Applied Linear Algebra Preliminary Exam With Solutions 16 January 2009, 10:00 am 2:00 pm Name: The proctor will let you read the following conditions
More informationLinear Algebra and its Applications
Linear Algebra and its Applications 431 (2009) 471 487 Contents lists available at ScienceDirect Linear Algebra and its Applications journal homepage: www.elsevier.com/locate/laa A Jacobi Davidson type
More informationAnalytical formulas for calculating the extremal ranks and inertias of A + BXB when X is a fixed-rank Hermitian matrix
Analytical formulas for calculating the extremal ranks and inertias of A + BXB when X is a fixed-rank Hermitian matrix Yongge Tian CEMA, Central University of Finance and Economics, Beijing 100081, China
More informationON 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 informationc 2002 Society for Industrial and Applied Mathematics
SIAM J. MATRIX ANAL. APPL. Vol. 24, No. 1, pp. 150 164 c 2002 Society for Industrial and Applied Mathematics VARIANTS OF THE GREVILLE FORMULA WITH APPLICATIONS TO EXACT RECURSIVE LEAST SQUARES JIE ZHOU,
More informationScientific Computing
Scientific Computing Direct solution methods Martin van Gijzen Delft University of Technology October 3, 2018 1 Program October 3 Matrix norms LU decomposition Basic algorithm Cost Stability Pivoting Pivoting
More informationFoundations 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 informationTwo 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 informationforms Christopher Engström November 14, 2014 MAA704: Matrix factorization and canonical forms Matrix properties Matrix factorization Canonical forms
Christopher Engström November 14, 2014 Hermitian LU QR echelon Contents of todays lecture Some interesting / useful / important of matrices Hermitian LU QR echelon Rewriting a as a product of several matrices.
More informationOrthogonal similarity of a real matrix and its transpose
Available online at www.sciencedirect.com Linear Algebra and its Applications 428 (2008) 382 392 www.elsevier.com/locate/laa Orthogonal similarity of a real matrix and its transpose J. Vermeer Delft University
More informationTensor Complementarity Problem and Semi-positive Tensors
DOI 10.1007/s10957-015-0800-2 Tensor Complementarity Problem and Semi-positive Tensors Yisheng Song 1 Liqun Qi 2 Received: 14 February 2015 / Accepted: 17 August 2015 Springer Science+Business Media New
More informationLecture 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 informationMultiple eigenvalues
Multiple eigenvalues arxiv:0711.3948v1 [math.na] 6 Nov 007 Joseph B. Keller Departments of Mathematics and Mechanical Engineering Stanford University Stanford, CA 94305-15 June 4, 007 Abstract The dimensions
More informationLinear Algebra using Dirac Notation: Pt. 2
Linear Algebra using Dirac Notation: Pt. 2 PHYS 476Q - Southern Illinois University February 6, 2018 PHYS 476Q - Southern Illinois University Linear Algebra using Dirac Notation: Pt. 2 February 6, 2018
More informationThe Lanczos and conjugate gradient algorithms
The Lanczos and conjugate gradient algorithms Gérard MEURANT October, 2008 1 The Lanczos algorithm 2 The Lanczos algorithm in finite precision 3 The nonsymmetric Lanczos algorithm 4 The Golub Kahan bidiagonalization
More informationDiagonal and Monomial Solutions of the Matrix Equation AXB = C
Iranian Journal of Mathematical Sciences and Informatics Vol. 9, No. 1 (2014), pp 31-42 Diagonal and Monomial Solutions of the Matrix Equation AXB = C Massoud Aman Department of Mathematics, Faculty of
More informationPROOF OF TWO MATRIX THEOREMS VIA TRIANGULAR FACTORIZATIONS ROY MATHIAS
PROOF OF TWO MATRIX THEOREMS VIA TRIANGULAR FACTORIZATIONS ROY MATHIAS Abstract. We present elementary proofs of the Cauchy-Binet Theorem on determinants and of the fact that the eigenvalues of a matrix
More informationA Jacobi Davidson type method for the generalized singular value problem
A Jacobi Davidson type method for the generalized singular value problem M. E. Hochstenbach a, a Department of Mathematics and Computing Science, Eindhoven University of Technology, P.O. Box 513, 5600
More informationChapter 1. Matrix Algebra
ST4233, Linear Models, Semester 1 2008-2009 Chapter 1. Matrix Algebra 1 Matrix and vector notation Definition 1.1 A matrix is a rectangular or square array of numbers of variables. We use uppercase boldface
More informationVERSAL DEFORMATIONS OF BILINEAR SYSTEMS UNDER OUTPUT-INJECTION EQUIVALENCE
PHYSCON 2013 San Luis Potosí México August 26 August 29 2013 VERSAL DEFORMATIONS OF BILINEAR SYSTEMS UNDER OUTPUT-INJECTION EQUIVALENCE M Isabel García-Planas Departamento de Matemàtica Aplicada I Universitat
More informationNote on the Jordan form of an irreducible eventually nonnegative matrix
Electronic Journal of Linear Algebra Volume 30 Volume 30 (2015) Article 19 2015 Note on the Jordan form of an irreducible eventually nonnegative matrix Leslie Hogben Iowa State University, hogben@aimath.org
More informationWHEN MODIFIED GRAM-SCHMIDT GENERATES A WELL-CONDITIONED SET OF VECTORS
IMA Journal of Numerical Analysis (2002) 22, 1-8 WHEN MODIFIED GRAM-SCHMIDT GENERATES A WELL-CONDITIONED SET OF VECTORS L. Giraud and J. Langou Cerfacs, 42 Avenue Gaspard Coriolis, 31057 Toulouse Cedex
More informationAn angle metric through the notion of Grassmann representative
Electronic Journal of Linear Algebra Volume 18 Volume 18 (009 Article 10 009 An angle metric through the notion of Grassmann representative Grigoris I. Kalogeropoulos gkaloger@math.uoa.gr Athanasios D.
More information