Contents 1 Introduction 1 Preliminaries Singly structured matrices Doubly structured matrices 9.1 Matrices that are H-selfadjoint and G-selfadjoint...

Size: px
Start display at page:

Download "Contents 1 Introduction 1 Preliminaries Singly structured matrices Doubly structured matrices 9.1 Matrices that are H-selfadjoint and G-selfadjoint..."

Transcription

1 Technische Universitat Chemnitz Sonderforschungsbereich 9 Numerische Simulation auf massiv parallelen Rechnern Christian Mehl, Volker Mehrmann, Hongguo Xu Canonical forms for doubly structured matrices and pencils Preprint SFB9/- Preprint-Reihe des Chemnitzer SFB 9 SFB9/- June

2 Contents 1 Introduction 1 Preliminaries Singly structured matrices Doubly structured matrices 9.1 Matrices that are H-selfadjoint and G-selfadjoint Matrices that are H-selfadjoint and G-skew-adjoint Singly and doubly structured pencils Conclusions 5 Appendix Authors: Christian Mehl Fakultat fur Mathematik TU Chemnitz 91 Chemnitz Germany mehl@mathematik.tu-chemnitz.de Volker Mehrmann Fakultat fur Mathematik TU Chemnitz 91 Chemnitz Germany mehrmann@mathematik.tu-chemnitz.de Hongguo Xu Department of Mathematics Case Western Reserve University 19 Euclid Avenue Cleveland, Ohio, 1, USA hxx@po.cwru.edu

3 Canonical forms for doubly structured matrices and pencils Christian Mehl Volker Mehrmann y Hongguo Xu z June, Abstract In this paper we derive canonical forms under structure preserving equivalence transformations for matrices and matrix pencils that have a multiple structure, which is either an H-selfadjoint or H-skew-adjoint structure, where the matrix H is a complex nonsingular Hermitian or skew-hermitian matrix. Matrices and pencils of such multiple structures arise for example in quantum chemistry in Hartree-Fock models or random phase approximation. Keywords. Indenite inner product, selfadjoint matrix, skew-adjoint matrix, matrix pencil, canonical form. AMS subject classication. 15A1, 15A, 15A5. 1 Introduction Canonical forms for matrices and matrix pencils have been studied for more than hundred years since work of Jordan, Kronecker and Weierstra, see [5]. In recent years, motivated Fakultat fur Mathematik, Technische Universitat Chemnitz, D-91 Chemnitz, Germany mehl@mathematik.tu-chemnitz.de. Large part of this author's work was performed while he was visiting the College of William and Mary, Department of Mathematics, P.O.Box 895, Williamsburg, VA , USA, and while he was supported by Deutsche Forschungsgemeinschaft, Me 19/1-1, Berechnung von Normalformen fur strukturierte Matrizenbuschel. y Fakultat fur Mathematik, Technische Universitat Chemnitz, D-91 Chemnitz, Germany mehrmann@mathematik.tu-chemnitz.de. Supported by Deutsche Forschungsgemeinschaft within SFB9, Project: Algebraische Zerlegungsmethoden z Department of Mathematics, Case Western Reserve University, Cleveland, OH 1-58, USA hxx@po.cwru.edu 1

4 from applications in control theory as well as quantum physics and quantum chemistry, there is a revived interest in such canonical forms for matrices and pencils that have algebraic structures, like Lie groups or Lie algebras. While the possible invariants have been characterized already some time ago [], the emphasis in the new results lies in structure preserving equivalence transformations, see e.g., [1, 1, 1, 15, 1]. In this paper we derive canonical forms under structure preserving equivalence transformations for matrices and matrix pencils with multiple structure. Denition 1 Let H C nn be a nonsingular Hermitian or skew-hermitian matrix, and let X C nn. 1. X is called H-selfadjoint if X H = HX.. X is called H-skew-adjoint if X H = HX. Canonical forms for pairs (A H), where H is Hermitian or skew-hermitian nonsingular and A is H-selfadjoint or H-skew-adjoint are well-known in literature (see, e.g., [, 11]). These forms are obtained under transformations of the form (A H)! (P 1 AP P HP ) where P is nonsingular. Here, we are interested in canonical forms for matrix triples (A H G), where G and H are Hermitian or skew-hermitian nonsingular and A is doubly structured with respect to G and H, i.e., A is H-selfadjoint or H-skew-adjoint and at the same time G-selfadjoint or G-skew-adjoint. We are also interested in the pencil case, i.e., we will also consider pencils %A B, where both A and B are doubly structured with respect to H and G. The main motivation for our interest in these types of matrices and pencils arises from quantum chemistry. Response function models lead to the problem of solving the generalized eigenvalue problem with a matrix pencil of the form C Z E F E A := (1) Z C F E where E F C Z C nn E = E F = F C = C Z = Z, see [8, 1]. Furthermore, there are important special cases in which the pencil has even further structure. For example, the simplest response function model is the time-dependent Hartree-Fock model, also called the random phase approximation (RPA). In this case, C is the identity and Z is the zero matrix, see [8, 1]. Thus, the generalized eigenvalue problem (1) reduces to the problem of nding the eigenvalues of the matrix E F L = () F E

5 where E F are as in (1). For stable Hartree-Fock ground state wave functions, it is furthermore known that E F and E + F are positive denite, see [8]. In other applications, however, like in multicongurational RPA [8], it is not even guaranteed that the matrix E in (1) is nonsingular. It is easy to see that the matrices E, A in (1) and L in () are doubly structured. With In In G = J = I n I n In H = I n we have that E is I-selfadjoint and H-skew-adjoint, A is I-selfadjoint and H-selfadjoint, while L is G-selfadjoint and J-skew-adjoint. When designing structure preserving numerical methods for large scale structures eigenvalue problems sometimes diculties in the convergence of the methods were observed in [, ] that have to do with the invariants of these pencils under structure preserving equivalence transformations, see also [1]. It is another motivation for our work to derive canonical forms that allow a better understanding of those properties of the pencils that lead to these diculties. We will derive the canonical form for matrix triples (A H G) under structure preserving transformations of the form (A H G)! (P 1 AP P HP P GP ) where P is nonsingular. This preserves the (skew-)hermitian structure of H and G and also the structure of A with respect to H and G. Based on the classical results, see Section, we clearly have canonical forms for (A H), (A G) or the pencil %H G, and hence the invariants of the pairs (A H) and (A G), as well as the invariants of the pencil %H G under congruence are invariants of the triple (A H G). It is our goal to obtain a canonical form that displays simultaneously the Jordan structure of A and the invariants of the canonical forms of (A H) and (A G). In general this is a very dicult problem, such a form may not even exist. Consider the following example. Example Consider matrices A = G = and H = Then A is G-selfadjoint and H-selfadjoint. But it is impossible to simultaneously decompose A, H, and G further into smaller block diagonal forms. This follows from the obvious fact that the pencil %G H can not be further decomposed. On the other hand, A has the Jordan canonical form : 5 :

6 Hence, both (A G) and (A H) are decomposable into smaller blocks (see Theorems 9 and 11 below). Due to this diculty, we restrict ourselves to an important special case. In most applications, the matrices H and G, that induce the structure, are contained in the set I S = I n m Im Im m n N : () I m I n m I m If this is the case then the pencil %H G is nondefective. Denition Let %A B C nn be a matrix pencil. We say that %A B is nondefective, if there exists nonsingular matrices P Q C nn, such that both P AQ and P BQ are diagonal. We will show that if G H are Hermitian nonsingular, such that the pencil %H G is nondefective, then a canonical form for the triple (A H G) exists, which is also unique except for the permutation of blocks. In particular, this canonical form includes the Jordan structure of A, and also the canonical forms of the pairs (A H) and (A G) and the pencil %H G can easily be read o. The paper is organized as follows. After providing some preliminary results in Section, we review canonical forms for matrices that are structured with respect to only one Hermitian matrix in Section. In Section we then discuss doubly structured matrices and in Section 5 we discuss canonical forms for structured pencils of the form A B, where both A and B are singly or doubly structured matrices. Preliminaries Throughout the paper, we use the following notation: By (A) we denote the spectrum of the matrix A. J p () denotes pp upper triangular Jordan block with eigenvalue. By sign(t) we mean the sign of a real number t Rnfg. A = A 1 : : : A m stands for the block diagonal matrix A with diagonal blocks A 1,..., A m. Furthermore, we introduce the following p p matrices. Z p := Dp := ( 1) ( 1) p+1 5

7 and F p := ( 1) ( 1) p+1 Note that F p R pp is symmetric if p is odd and skew-symmetric if p is even, whereas Z p and D p are symmetric for all p. We list some properties of these matrices and the matrix J p () which can be easily veried, and will be used in the following. 5 Lemma Let p N. 1. Z p = I p D p = I p F p = ( 1) p+1 I p.. F p Z p = D p = ( 1) p+1 Z p F p D p F p = Z p = ( 1) p+1 F p D p :. D p Z p = F p = ( 1) p+1 Z p D p F p Z p F p = Z p.. Z 1 p J p ()Z p = J p (). 5. D 1 p J p ()D p = J p ().. F 1 p J p ()F p = J p (). Another important result that will frequently be used throughout the paper is the following well-known result, [5]. Lemma 5 Let A B X be square matrices, such that the spectra of A and B are disjoint. If AX = XB, then X =. Finally, we review the canonical forms for regular Hermitian pencils, i.e., pencils %H G, where both H and G are Hermitian and det(%h G). This result goes back to results from Weierstra (see [19]) and Kronecker (see [1]). Theorem Let %H G be a regular Hermitian pencil. Then there exists a nonsingular matrix P C nn such that P (%H G)P = (%H 1 G 1 ) : : : (%H l G l ) () where the blocks %H j G j have one and only one of the following forms: 1. Blocks associated with paired nonreal eigenvalues,, where Im() > : %H j G j = % Ir I r 5 J r () J r () : (5)

8 . Blocks associated with real eigenvalues and sign " f1 1g: %H j G j = %"Z r "Z r J r () = %" ". Blocks associated with the eigenvalue 1 and sign " f1 1g: %H j G j = %"Z r J r () "Z r = %" " : () 5 : () Moreover, the decomposition () is unique up to a block permutation that exchanges blocks H i G i. Proof. For a full proof, see [18], Lemmas 1.{. There, the result is shown without the additional condition Im() > for the blocks associated with nonreal eigenvalues, but applying a permutation, we may always place the block that is associated with the eigenvalue in the (1 )-block position of the form in (5). If %H G is nondefective then we immediately have the following corollary. Corollary Let %H G be a nondefective Hermitian pencil, where both H and G are nonsingular. Then there exists a nonsingular matrix P C nn such that P (%H G)P = (%H 1 G 1 ) : : : (%H l G l ) where the spectra of %H j G j and %H l G l are disjoint for j = l, and where each block %H j G j has either only one pair of complex conjugate eigenvalues or only one single real eigenvalue. Moreover, the block %H j G j has one and only one of the following forms. 1. Blocks with nonreal eigenvalues,, where Im > and r N: : %H j G j = % Ir I r. Blocks with real eigenvalue, where r p N: Ip %H j G j = I r p Ir I r Ip : I r p Moreover the decomposition is unique up to a block permutation.

9 Proof. Since the size of every Jordan blocks equals one, the result follows directly from Theorem after proper permutations such that equal eigenvalues are combined in a single block. Remark 8 Following from Theorem and Corollary it is obvious that if C nr is an eigenvalue of %H G then so is and both eigenvalues have the same Jordan structures. Singly structured matrices In this section, we review the well-known canonical forms for H-selfadjoint matrices and H-skew-adjoint matrices, where H always denotes a complex nonsingular Hermitian n n matrix. Theorem 9 Let A C nn be H-selfadjoint. Then there exists a nonsingular matrix P C nn, such that P 1 AP = A 1 : : : A k and P HP = H 1 : : : H k (8) where A j and H j are of the same size and the pair (A j H j ) has one and only one of the following forms: 1. Blocks associated with real eigenvalues: where R, p N, and " f1 1g. A j = J p () and H j = "Z p (9). Blocks associated with a pair of nonreal eigenvalues: Jp () Zp A j = and H J p () j = Z p (1) where C nr with Im() > and p N. Moreover, the form (P 1 AP P HP ) of (A H) is uniquely determined up to the permutation of blocks. Proof. See, e.g., []. Even though (8) is unique only up to a permutation of blocks, we call it the a canonical form of the pair (A H).

10 Remark 1 In some instances it will turn out be useful to use a slightly dierent form for the blocks of type (1) in (8). Multiplying the matrices from both sides by I p Z p, one nds that (1) takes the form A j = Jp ( ) J p ( ) and H j = Ip I p : (11) Using the same transformation, we can also get back from the form (11) to the form (1). This transformation will frequently be used in the following and its application will be called the Z-trick. Apart from the eigenvalues of an H-selfadjoint matrix A, the parameters " that are associated with blocks to real eigenvalues are invariants of the pair (A H). The collection of these parameters is sometimes referred to as the sign characteristic (see, e.g., [] and [11]). To highlight that these parameters are related to the matrix H (we will soon have to deal with two structures), we will use the term H-structure indices in the following. Theorem 11 Let S C nn be H-skew-adjoint. Then there exists a nonsingular matrix P C nn, such that P 1 SP = S 1 : : : S k and P HP = H 1 : : : H k (1) where S j and H j are of the same size and each pair (S j H j ) has one and only one of the following forms: 1. Blocks associated with purely imaginary eigenvalues: where R, p N, and " f1 1g. S j = ij p () and H j = "Z p (1). Blocks associated with a pair of non purely imaginary eigenvalues: ijp () Zp S j = and H = (1) ij p () Z p where C nr with Im() > and p N. Moreover, the form (P 1 SP P HP ) of (S H) is uniquely determined up to a permutation of blocks. Proof. This follows directly from Theorem 9 considering the H-selfadjoint matrix is. Again, we will call the parameter " in (1) the H-structure index of the block S j in (1). Moreover, the form (1) will be called the canonical form of the pair (S H). Remark 1 From Theorems 9 and 11, it is easy to nd the following symmetries in the spectra of H-selfadjoint and H-skew-adjoint matrices. If = R is an eigenvalue of the H- selfadjoint matrix A then so is and both eigenvalues have the same Jordan structure. If = ir is an eigenvalue of the H-skew-adjoint matrix A then so is and both eigenvalues have the same Jordan structure. 8

11 Doubly structured matrices In this section we give canonical forms for matrices that are doubly structured with respect to Hermitian or skew-hermitian nonsingular matrices G and H. First, we note that by Theorem 9, Jordan blocks associated with real eigenvalues in the selfadjoint case (or purely imaginary eigenvalues in the skew-adjoint case) have structure indices with respect to G and/or H. We will call these indices the G- and H-structure indices of A, respectively. Moreover, we may always assume that G and H are Hermitian. Otherwise, we may consider ig or ih, respectively, having in mind the following remark. Remark 1 Let H C nn be nonsingular and Hermitian or skew-hermitian and let A C nn. Then the following conditions hold. 1. A is H-selfadjoint if and only if A is ih-selfadjoint.. A is H-skew-adjoint if and only if A is ih-skew-adjoint.. A is H-selfadjoint if and only if ia is H-skew-adjoint. Remark 1 implies in particular that we may assume that the structure on A induced by one of the matrices G and H, say H, is the structure of a selfadjoint matrix. In other words, we may assume that A is H-selfadjoint. Otherwise, we may consider ia. Hence, it remains to discuss the following cases. Matrices that are H-selfadjoint and G-selfadjoint (Section.1) Matrices that are H-selfadjoint and G-skew-adjoint (Section.) Finally, we always assume that the pencil %H G is nondefective. Remark 1 Instead of requiring that %H G is nondefective, we may as well consider the generalization of this case, that for the matrices A G H at least one of the three pencils %H G, %H HA and %G GA is nondefective. For example, if A is nonsingular and both H- and G-selfadjoint then we can consider the matrix triple (H 1 G H HA) for which H, HA are Hermitian and H 1 G is H and HA selfadjoint, since (H 1 G) = GH 1. Thus, if %H HA is nondefective then we can get the canonical form of this new triple. But once we have this, we can easily get the canonical form of the original triple (A H G). So our results will cover more general cases. 9

12 .1 Matrices that are H-selfadjoint and G-selfadjoint In this section we will derive a canonical form for matrices that are selfadjoint with respect to nonsingular Hermitian matrices H and G, such that the pencil %H G is nondefective. For the proof of our main result, the following lemma will be needed. Lemma 15 Let G H C nn be Hermitian and nonsingular. Let A C nn be H- selfadjoint and G-selfadjoint. Then there exists a nonsingular matrix P C nn such that P 1 AP = A 1 : : : A k P HP = H 1 : : : H k P GP = G 1 : : : G k where A j, H j and G j have corresponding sizes. Moreover, each pencil %H j G j has as spectrum either f j j g for some j C nr or f j g for some j R and the spectra of two subpencils %H j G j and %H l G l, j = l, are disjoint. Proof. By Theorem, there exists a nonsingular matrix Q C nn such that Q GQ = G1 G ~ Q HQ = H1 H ~ and Q 1 AQ = A11 A 1 A 1 A where the pencil %H 1 G 1 has as spectrum either f 1 1 g for some 1 C nr or f 1 g for some 1 R and such that the spectra of the pencils %H 1 G 1 and % H ~ G ~ are disjoint. Since A is H-selfadjoint and G-selfadjoint, we obtain that A H 11 1 A ~ 1 H H1 A 11 H 1 A 1 and A 1 H 1 A ~ H A G 11 1 A ~ 1 G A G 1 1 A ~ G Since with G also ~ G is nonsingular, this implies = = ~H A 1 ~ H A G1 A 11 G 1 A 1 ~G A 1 G ~ : A A 1 ~ H ~ G 1 = H 1 A 1 ~ G 1 = H 1 G 1 1 G 1A 1 ~ G 1 = H 1 G 1 1 A 1 : Since the pencils %H 1 G 1 and % ~ H ~ G have disjoint spectra, we obtain that A 1 = and therefore A 1 = H 1 1 A 1 ~ H =. The rest of the proof now follows by induction. Theorem 1 Let G H C nn be Hermitian and nonsingular such that the pencil %H G is nondefective. Let A C nn be H-selfadjoint and G-selfadjoint. Then there exists a nonsingular matrix P C nn such that P 1 AP = A 1 : : : A k P GP = G 1 : : : G k (15) P HP = H 1 : : : H k 1

13 where the blocks A j, G j, H j have corresponding sizes and are of one and only one of the following forms. Type (1): A j = J p () H j = "Z p and G j = "Z p where R, p N, " f1 1g, and Rnfg. The H-structure index of A j is " and the G-structure index of A j is sign("). Type (): Jp () A j = J p () H j = Zp Z p and G j = Zp Z p where R, p N, and C, Im() >. The H-structure indices of A j are 1 1 and the G-structure indices of A j are 1 1. Type (): Jp () A j = J p () H j = Zp Z p and G j = Zp Z p where C nr, p N, and C nfg, where Im(). Moreover, the canonical form (15) is unique up to permutation of blocks. Proof. By Lemma 15 we may assume that the pencil %H G has the eigenvalues either for some C nr or for some R. Case 1: R. Since the pencil %H G is nondefective, by Corollary there exists a nonsingular matrix P C nn such that P Im Im (%H G)P = % I n m I n m i.e., in particular that G is a scalar multiple of H. Applying Theorem 9, we nd that there exists a nonsingular matrix Q C nn such that (Q 1 AQ Q HQ) is in canonical form (8). Since G = H, we obtain that A, H, and G can be reduced simultaneously to block diagonal form with diagonal blocks of types 1 and. Case : C nr. In this case, we obtain from Corollary that there exists a nonsingular matrix P C nn such that P (%H G)P = % Im Im I m I m 11

14 where m = n and Im() >. Let A = A11 A 1 A 1 A be partitioned conformably. Then we obtain from A H = HA and A G = GA that A 1 = A 1 and A 1 = A 1 : Since =, this implies that A 1 =. In an analogous way we show that A 1 =, and moreover, we have A = A 11 by symmetry. Let Q 1 be such that Q 1 A 1 11Q 1 is in Jordan canonical form and set Q1 Q = P Q : 1 Then we obtain ~A = Q 1 AQ = Q Im HQ = I m Q 1 1 A 11Q 1 Q 1 A 11 Q 1 and Q GQ = Jp () J p () H ~ Ip = I p Im I m After a proper block permutation, we obtain that A, H, and G can be reduced simultaneously to block diagonal form with diagonal blocks of the forms : and ~ G = Ip I p respectively, where p N and C. The result then follows by applying the Z-trick, see Remark 1. Uniqueness: Suppose that A1 A = H = A ~A = H1 G1 G = H G ~A1 ~H1 A ~ H ~ ~G1 = H ~ G ~ = G ~ are in canonical form, where H, G, H, ~ G ~ are Hermitian nonsingular, A is H-selfadjoint and G-selfadjoint and A ~ is H-selfadjoint ~ and G-selfadjoint ~ and all matrices have corresponding block structures. If P 1 AP = A, ~ (A1 ) = ( A1 ~ ) and (A ) = ( A ~ ), such that the spectra of A 1 and A are disjoint, then it follows immediately that P has a corresponding block diagonal structure. Analogously, assuming that the spectra of %H 1 G 1 and % H ~ G ~ (and of %H G and % H1 ~ G1 ~, respectively) are disjoint and that P HP = H ~ and P GP = G, ~ where P is nonsingular, we obtain again that P has a corresponding block diagonal structure. Indeed, partitioning P = P11 P 1 P 1 P and P = 1 Q11 Q 1 Q 1 Q and

15 conformably with H, we obtain that This implies that G 11 P 1 = Q 1 ~ G and H 11 P 1 = Q 1 ~ H : H 1 11 G 11P 1 = P 1 ~ H 1 ~ G and from that, we obtain P 1 =, since the spectra of H 1 11 G 11 and ~ H 1 ~ G are disjoint. Analogously, we show P 1 =. Hence, it is sucient to prove the uniqueness for the case that A has only one pair of eigenvalues and that %G H has only a pair of eigenvalues. But then, the uniqueness is clear, since we obtain from Theorem 9 the uniqueness of the canonical form for the pair (A H). Note that the structure G is then uniquely dened by the invariant with Im(). In both cases it is easy to verify that the H and G-structure indices of each block are as claimed in the theorem.. Matrices that are H-selfadjoint and G-skew-adjoint In this section we present a canonical form for a matrix A that is H-selfadjoint and G-skewadjoint, where H and G are Hermitian nonsingular matrices, such that the pencil %H G is nondefective. By Remark 1, the eigenvalues of A satisfy more symmetry properties. If C is an eigenvalue of A then, because A is G-skew-adjoint, so is having the same Jordan structure as. On the other hand, A is H-selfadjoint and thus, with and also and are eigenvalues of A having the same Jordan structures as. Thus, the eigenvalues of A occur in quadruples f g, where all these eigenvalues have the same Jordan structures. If is real or purely imaginary, this set is equal to f g, and if =, this set is just fg. The following lemmas will be needed for constructing the canonical form. Lemma 1 Let G H C nn be Hermitian and nonsingular. Furthermore, let A C nn be H-selfadjoint and G-skew-adjoint. Then there exists a nonsingular matrix P C nn such that P 1 AP = A 1 : : : A k P HP = H 1 : : : H k P GP = G 1 : : : G k where A j, H j and G j have corresponding sizes. Moreover, each matrix A j has the spectrum f j j j j g and the spectra of two matrices A j and A l, where j = l, are disjoint. 1

16 Proof. By using the eigenvalue properties of A mentioned above, one can nd a matrix Q C nn such that Q GQ = G11 G 1 G Q HQ = 1 G H11 H 1 H and Q 1 A1 AQ = 1 H A ~ where A 1 has the spectrum f g for some 1 C, such that the spectra of A 1 and ~ A are disjoint. Then we obtain from A H = HA and A G = GA that A 1 H 1 = H 1 ~ A and A 1 G 1 = G 1 ~ A By construction, the spectra of A 1 and ~ A are disjoint. This implies H 1 = and G 1 =. The proof then follows by induction. Lemma 18 Let G H C nn be Hermitian and nonsingular. Furthermore, let A C nn be H-selfadjoint and G-skew-adjoint. Then there exists a nonsingular matrix P C nn such that P 1 AP = A 1 : : : A k P HP = H 1 : : : H k P GP = G 1 : : : G k where A j, H j and G j have corresponding sizes. The spectrum of each pencil %H j G j is contained in f j j j j g for some j C and the spectrum of %H l G l is disjoint from the set f j j j j g if j = l. Proof. The proof proceeds analogously to the proof of Lemma 15 using the equations A H = HA and A G = GA. Note that in contrast to the eigenvalue of A, the eigenvalues of the pencil %H G need not occur in quadruples f j j j j g. If j is an eigenvalue of %H G then from Theorem, we only know that also j is an eigenvalue, but j and j need not be. However, to get corresponding block diagonal forms of A, G, H, we have to group j and j together with j and j if they are also eigenvalues of %H G. In view of Lemma 18, it is sucient to consider pencils %H G whose spectrum is contained in f g. Therefore, a discussion of properties of such pencils will be helpful. Lemma 19 Let G, H C nn be nonsingular and Hermitian such that the pencil %H G is nondefective. (i) If the spectrum of %H G is contained in f g, where Rnfg, then H 1 GH 1 G = I n : 1

17 (ii) If the spectrum of %H G is contained in f g, where C nr, then there exists a matrix P such that for ~ H = P HP, ~ G = P GP and ~ A = P 1 AP, Moreover, ~H 1 G ~ H ~ 1 ~G I = m : (1) I m A1 ~A = A 1 Proof. (i) We consider the problem in two cases. Case (1): Im() =. Since the pencil %H G is nondefective and has only the eigenvalues R, by Corollary there exists a nonsingular matrix P C nn and numbers p q r s N such that I p I p H = P I q I r 5 P and G = P I q I r 5 P: I s I s This implies H 1 GH 1 G = P 1 ( I n )P = I n. Case (): Re() =. Since the pencil %H G is nondefective and has only the eigenvalues ir, by Corollary there exists a nonsingular matrix P C nn such that Im P and G = P P H = P I m : I m I m where m = n N. This implies H 1 GH 1 G = P 1 ( I n )P = I n. (ii) By Corollary there exists a nonsingular matrix P such that where m = n % ~ H ~ G = %P HP P GP = % Im I m N and = I p ( I m p ), p m. We then obtain that ~H 1 ~ G = (1) and hence we have (1). Note that ~ A is ~ H-selfadjoint and ~ G-skew-adjoint. This implies that ~A( ~ H 1 ~G) = ~ H 1 ~A ~ G = ( ~ H 1 ~G) ~ A: Since in this case =, from the block form (1) we get ~ A = A1 A. Since ~ A is ~H-selfadjoint, we obtain that A = A 1. 15

18 Theorem Let G H C nn be Hermitian nonsingular such that the pencil %H G is nondefective. Furthermore, let A C nn be H-selfadjoint and G-skew-adjoint. Then there exists a nonsingular matrix P C nn such that P 1 AP = A 1 : : : A k P GP = G 1 : : : G k (18) P HP = H 1 : : : H k where, for each j, the blocks A j, G j, H j have corresponding sizes and are of one and only one of the following forms. Type (1a): H j = A j = Z p Z p Z p Z p J p () J p () J p () J p () 5 and G j = 5 (19) Z p Z p Z p Z p where C with Re()Im() >, p N and Rnfg, Re() Im(). Type (1b): Jp () A j = J p ( ) Zm H j = " ( G jj ) Z j = m Z m Z m 5 () (1) where >, p N and Rnfg, Re() Im(). The H j -structure index of is " and the H j -structure index of is "( jj ). Type (1c): Jp () A j = i J p ( ) H j = Zm Z m Zm G j = "jj ( jj () ) Z m where >, p N and Rnfg, Re() Im(). The G j -structure index of is " and the G j -structure index of is "( jj ). Type (1d1): A j = J p () H j = "Z p and G j = ~"F p () where Rnfg, Re() Im(), and p N is odd if R and even if ir. Moreover, the eigenvalue = has the H j -structure index " and the G j -structure index sign(~") if R and sign( i~") if ir. 1

19 Type (1d): Jp () A j = J p () H j = Zp Z p and G j = F p F p () where Rnfg, Re() Im(), and p N is even if R and odd if ir. Moreover, the eigenvalue = has the H j -structure indices +1 1 and the G j -structure indices Type (a): H j = A j = Z p Z p Z p Z p J p () J p () J p () J p () 5 and G j = 5 (5) Z p Z p Z p Z p where C with Re()Im() >, p N, and C with Re()Im() >. Type (b): H j = A j = Z p Z p Z p Z p J p () J p () J p () J p () 5 and G j = 5 () 5 () Z p Z p Z p Z p 5 (8) where >, p N, and C with Re()Im() >. The H j -structure indices of are +1 1 and the H j -structure indices of are Type (c): H j = A j = i Z p Z p Z p Z p J p () J p () J p () J p () 5 and G j = 1 5 (9) Z p Z p Z p Z p 5 ()

20 where >, p N, and C with Re()Im() >. The G j -structure indices of are +1 1 and the G j -structure indices of are Type (d): Jp () A j = J p () H j = Zp Z p G j = " F p ( 1) p+1 F p (1) where p N, " f+1 1g, and C with Re()Im() >. Moreover, the eigenvalue = has the H j -structure indices +1 1 and the G j -structure indices In the blocks of type (1a){(1d) the subpencil %H j G j has only real or purely imaginary eigenvalues and in the blocks (a){(d) the subpencil %H j G j has only eigenvalues that are neither real nor purely imaginary. Moreover, the canonical form (18) is unique up to permutation of blocks. Proof. In view of Lemma 18, we may assume that the spectrum of the pencil %H G is contained in f g for some C nfg, Re() Im(), and it is sucient to distinguish the following two cases. Case (1): Re()Im() =. In view of Lemma 1, we may distinguish the following four subcases. Subcase (1a): The spectrum of A is f g, where Re()Im() >. Since A is H-selfadjoint and G-skew-adjoint, it follows from Remark 1 that,, and have the same Jordan structures. Applying Theorem 9, the Z-trick, and a block permutation, we may assume that A and H have the following forms. A = J () J () J () J () 5 H = I m I m I m I m 5 () where m = n N and J () is an (m m) matrix in Jordan canonical form only having the eigenvalue. Then, the equation A G = GA and the fact that,,, and are pairwise distinct, imply that G necessarily has the form G = G G G G 5 () where G G C mm. By Lemma 19, we obtain that H 1 GH 1 G = I n. This implies in particular that G G = I m : () 18

21 Note that the equation A G = GA also implies that J () G = G J (), i.e., G commutes with J (). Hence, setting Q := we obtain that Q 1 AQ = A, Q HQ = H and Q GQ = 1 G 1 I m 1 G 1 I m I m 1 G G 1 G G I m 5 5 : (5) Then it follows from () and (5) that the triple (A H G) can be reduced to blocks of the form given by (19) and (), by applying a proper block permutation and the Z-trick. Subcase (1b): The spectrum of A is f g, where >. Theorem 11 implies that and have the same Jordan structures. Moreover, applying Theorem 9, we may assume that A, H, and G have the following forms. where A = A1 H = A 1 H1 and G = H A 1 = J p1 () : : : J pk () H 1 = " 1 Z p1 : : : " k Z pk H = ~" 1 Z p1 : : : ~" k Z pk G1 G G G and G j C mm for m = n. Observing that A G = GA, we obtain that G 1 = G =, since =, and A 1 G = G A 1. Moreover, H 1 GH 1 G = I n implies that H 1 G 1 H 1 G = I m = H 1 G H 1G 1 : () Setting Im Q = 1 H 1 G then from (), A G 1 = G A 1, Zp 1 J p () Z p = J p () (Lemma ), and the block forms of H and A 1, we obtain that Q 1 A1 A1 AQ = G H = A 1 H 1 G A 1 H1 H1 Q HQ = Q GQ = = 1 j j G H 1 H H 1 G 1 G H 1 G 1 G H 1 G 19 ( jj ) H 1 H1 = H 1 : and

22 Thus, it follows from a proper block permutation that we may assume that Jp () A = H = J p () Hence, setting "Zp "( and G = jj ) Z p ~Q = " 1 I m I m we nd that ~ Q 1 A ~ Q, ~ Q H ~ Q, and ~ Q G ~ Q have the desired forms. "Z p "Z p Subcase (1c): The spectrum of A is f g, where ir, Im() >. The matrix ia is G-selfadjoint, H-skew-adjoint and has only a pair of real eigenvalues. Noting that the spectrum of %G H is contained in f 1 1 g, we can reduce the problem to Case (1b), i.e., it is sucient consider the case that ia, G, and H have the form (1). ia = Jp () J p ( ) where ~" f+1 1g. Setting G = Q = we obtain that Q 1 ( ia)q = ia, Q Zm HQ = Z m ~"Zm ~"( jj H = ) Z m 1 I m 1 I m and Q GQ = ~"jj Subcase (1d): The spectrum of A is fg. 1 Z m 1 Z m Zm ~"( jj : ) Z m It follows from Lemma in the appendix that the triple (A H G) can be reduced to blocks of the forms () or (). Case (): Re()Im() =. By Corollary we may assume that the pencil %H G is already in the form %H G = % Im I m where m = n N and = diag(i p I m p ), 1 p m and, furthermore, we have that (1). Then Lemma 19 implies that A has the form A1 A = A : 1 Note, that by Lemma 19 a similarity transformation on A with a corresponding block diagonal matrix and simultaneous congruence transformations on H, G will not change :

23 the block structure of A and the identity (1), but it does change the block forms in H and G. Hence we can apply similarity transformations on A 1 and at the same time keep the relation (1). Again, we will consider the following four subcases. Subcase (a): The spectrum of A is f g, where Re()Im() >. Again, the eigenvalues, and have the same Jordan structures. Moreover, there exists a nonsingular matrix Q1 Q = Q C nn 1 such that Q 1 AQ = A 11 A A 11 A 5 and Q HQ = H where A 11 C kk has the eigenvalues and and A C ( n k)( n k) has the eigenvalues and. Partitioning Q GQ conformably, i.e., Q GQ = G 1 G G G G 1 G G G we obtain from the equation A G = GA and the fact that A 11 and A have no common eigenvalues that G = G =. Thus, after a proper block permutation, we may consider two smaller subproblems. The rst one is A11 ~A = A 11 and (1) implies that ~ H = Ik I k 5 ~H 1 G ~ H ~ 1 ~G I = k : I k and ~ G = G1 G 1 Hence, after applying a similarity transformation on A 11, we may assume that ~ A, ~ G, and ~H are in the forms () and (), where (G 1 ) = I. The remainder of the proof then proceeds analogously to Subcase (1a). The second subproblem with respect to A can be transformed in the same way. Subcase (b): The spectrum of A is f g, where >. We obtain from Theorem 11 that and have the same Jordan structures. Hence, both A 1 and A 1 must have the eigenvalues and with the same Jordan structures. Thus, there exists a nonsingular matrix Q1 Q = Q 1 1

24 such that Q 1 AQ = J () J () J () J () 5 and Q HQ = H where k = n and J () is an k k matrix in Jordan canonical form associated with only one eigenvalue. Partitioning Q GQ conformably, i.e., Q GQ = G 1 G G G G 1 G G G we obtain from A G = GA and the fact that J () and J () have no common eigenvalues that G 1 = G = and J () G = G J (), J () G = G J (). Moreover, we still have (1), which implies that G G = I. Thus we may assume that A, G, and H are in the forms () and (), where G G = I. The remainder of the proof then proceeds analogously to Subcase (1a). 5 Subcase (c): The spectrum of A is f g, where ir. The proof proceeds analogously to the proof of Subcase (1c). Subcase (d): The spectrum of A is fg. This case follows from Lemma in the appendix and by applying the Z-trick. Uniqueness: Analogous to the proof of Theorem 1, it is sucient to prove uniqueness for the case that the spectrum of A is f g for some C and that the spectrum of %H G is contained in f g for some C. Again, the canonical form for the pair (A H) is unique. In any case except for the case that = and = R, the matrix G is then uniquely determined by the invariants with Re() Im() (and signs " or ~" in some cases that are uniquely determined by the canonical form for the pair (A G)). Only in the case = and = R, we have an additional invariant " that is not an invariant of the canonical form for the pair (A G). In this case, the uniqueness follows from Lemma in the appendix. In all cases (1a){(d) it is easy to verify that the H and G-structure indices of each block are as claimed in the theorem. 5 Singly and doubly structured pencils In this section, we discuss canonical forms for matrix pencils %A B, where both A and B are matrices that are singly or doubly structured with respect to some indenite inner

25 product. It turns out that the case of structured pencils can be reduced to the matrix case. This is done in the following theorem. Theorem 1 Let the matrices G H C nn be nonsingular and Hermitian or skew- Hermitian, i.e., G = G G and H = H H where G H f1 1g. Furthermore, let %A B C nn be a regular pencil such that A H = " A HA A G = A GA B H = " B HB B G = B GB () where " A " B A B f1 1g. Then there exists nonsingular matrices P Q C nn that P 1 In1 M (%A B)Q = % N I n H11 Q HP = Q GP = H G11 G such where M H 11 G 11 C n 1 n 1 and N H G C n n. Moreover, M and N are in Jordan canonical form, N is nilpotent and the following conditions are satised. H 11 = H " A H 11 G 11 = G A G 11 M H 11 = " A " B H 11 M M G 11 = A B G 11 M H = H " B H G = G B G N H = " A " B H N N G = A B G N: Proof. Let P Q C nn be such that the pencil P 1 In1 (%A B)Q = N M I n (8) is in Kronecker canonical form (see []), where M, N are in Jordan canonical form and N is nilpotent. Then () and (8) imply in particular that Q H(%" A A " B B) = Q (%A B M )H = % P H: From this and (8) we obtain that Q In1 HP N Q HP M I n In1 N I n = Q In1 HAQ = " A N P HQ M = Q HBQ = " B P HQ: I n

26 Setting Q H11 H HP = 1 H 1 H and H11 H 1 This implies, in particular, that and noting that P HQ = H (Q HP ), we nd that H 1 N H N = H " A H 11 H 1 N H 1 N H H11 M H 1 M = H 11 M H 1 H 1 M H H " B H 1 H H 1 = H " B M H 1 = " A " B M H 1 N = (" A " B ) k (M ) k H 1 N k for every k N: Since N is nilpotent, it follows that H 1 = and thus, also H 1 = H " A N H 1 =. Moreover, H 11 = H " A H 11 and H = H " B H, and H N = H " A N H = " A " B N H, H 11 M = H " B M H 11 = " A " B M H 11. Analogously we show that Q GP has the structure claimed in the theorem. This concludes the proof. We note that M is a doubly structured matrix with structures induced by H 11 and G 11 and that N is a nilpotent doubly structured matrix with structured induced by H and G, where H 11, G 11, H, and G are all Hermitian or skew-hermitian. Therefore, Theorem 1 gives a general description about how to obtain the canonical forms for the pencil case from the canonical forms in the matrix case that are given in the previous sections. We only have to further reduce M and N by applying the results from Section. Note that Theorem 1 does not require the pencil %H G to be nondefective. However, canonical forms for the matrix case are known for this case only. Theorem 1 also describes the case of singly structured pencils. In this case one may choose H = G, " A = A, and " B = B. Thus, Theorem 1 gives a general description how to obtain canonical forms for singly and doubly structured pencils from the canonical forms in the matrix case. For obvious reasons, we do not give a list of the canonical forms for all possible cases, but only one example to illustrate the eect of Theorem 1. Theorem Let H C nn be Hermitian and nonsingular and let %A B C nn be a regular pencil such that A and B are H-selfadjoint. Then there exists nonsingular matrices P Q C nn such that P 1 (%A B)Q = % Q HP = A 1... A k H 1... H k 5 B 1... : B k where the blocks A j, B j, and H j have corresponding sizes and are of one and only one of the following forms: 5 5

27 1. Blocks associated with real eigenvalues: where p N, R, and " f1 1g. A j = I p B j = J p () and H j = "Z p. Blocks associated with a pair of nonreal eigenvalues: Jp () A j = I p B j = and H J p () j = Z p where p N and C nr.. Blocks associated with the eigenvalue 1: where p N and " f1 1g. A j = J p () B j = I p and H j = "Z p Moreover, this form is uniquely determined up to permutation of blocks. Proof. This follows directly from Theorem 1 and Theorem 9. Note that with the assumptions and notation of Theorem the pencil H(%A B) = %HA HB is a Hermitian pencil. It turns out that Theorem is a generalization of Theorem. Indeed, the pencil Q HP P 1 (%A B)Q is a Hermitian pencil in canonical form. Conclusions We have derived canonical forms for matrices and matrix pencils that are doubly structured in the sense that they are H-selfadjoint (or H-skew-adjoint) and at the same time G-selfadjoint (or G-skew-adjoint), where we have assumed that G H are nonsingular Hermitian (or skew Hermitian) and G H is a nondefective pencil. The general case that G or H are singular, or that the pencil G H is defective is still an open problem. Also the associated real canonical forms, which appear to be much more dicult, are open. In view of the applications in eigenvalue computations, it is also important to restrict the transformation matrices to be unitary (or orthogonal in the real case). This case will be covered in a forthcoming paper, that will also address numerical methods, in particular for the classes of pencils arising in quantum chemistry that we have discussed in the introduction. 5

28 References [1] G. Ammar, C. Mehl, and V. Mehrmann. Schur-like forms for matrix Lie groups, Lie algebras and Jordan algebras. Linear Algebra Appl., 8:11{9, [] D.Z. Djokovic, J. Patera, P. Winternitz and H. Zassenhaus, Normal forms of elements of classical real and complex Lie and Jordan algebras, J. Math. Phys., :1-1, 198. [] U. Flaschka. Eine Variante des Lanczos-Algorithmus fur groe, dunn besetzte symmetrische Matrizen mit Blockstruktur. Dissertation, Universitat Bielefeld, Bielefeld, FRG, 199. [] U. Flaschka, W.-W. Lin, and J.-L. Wu. A kqz algorithm for solving linear-response eigenvalue equations. Linear Algebra Appl., 15:9{1, 199. [5] F. Gantmacher. Theory of Matrices, volume 1. Chelsea, New York, [] F. Gantmacher. Theory of Matrices, volume. Chelsea, New York, [] I. Gohberg, P. Lancaster, and L. Rodman. Matrices and Indenite Scalar Products. Birkhauser Verlag, Basel, Boston, Stuttgart, 198. [8] A. Hansen, B. Voigt, and S. Rettrup. Large-scale RPA calculations of chiroptical properties of organic molecules: Program RPAC. International Journal of Quantum Chemistry, XXIII.:595{11, 198. [9] R. Horn and C. Johnson. Topics in matrix analysis. Camebridge University Press, Camebridge, [1] L. Kronecker. Algebraische Reduction von Scharen bilinearer Formen, 189. In Collected Works III (second part), pages 11{155, Chelsea, New York, 198. [11] P. Lancaster and L. Rodman. Algebraic Riccati Equations. Clarendon Press, Oxford, [1] P. Lancaster and M. Tismenetsky. The Theory of Matrices. Academic Press, Orlando, nd edition, [1] W.-W. Lin, V. Mehrmann, and H. Xu. Canonical forms for Hamiltonian and symplectic matrices and pencils. Linear Algebra Appl., 1{:9{5, [1] C. Mehl. Compatible Lie and Jordan algebras and applications to structured matrices and pencils. Dissertation, Fakultat fur Mathematik, TU Chemnitz, 91 Chemnitz (FRG), 1998.

29 [15] C. Mehl. Condensed forms for skew-hamiltonian/hamiltonian pencils. SIAM J. Matrix Anal. Appl., 1:5{, [1] V.Mehrmann, and H. Xu. Structured Jordan canonical forms for structured matrices that are Hermitian, skew Hermitian or unitary with respect to indenite inner products. Electron. J. Linear Algebra, 5:{1, [1] J. Olson, H. Jensen, and P. Jrgensen. Solution of large matrix equations which occur in response theory. J. Comput. Phys., :5{8, [18] R. Thompson. The characteristic polynomial of a principal subpencil of a Hermitian matrix pencil. Linear Algebra Appl., 1:15{1, 19. [19] K. Weierstra. Zur Theorie der bilinearen und quadratischen Formen. Monatsb. Akad. d. Wiss. Berlin, pages 1{8, 18. Appendix In the appendix we derive some technical Lemmas. Recall the Kronecker product (see, e.g., [9, 1]). Denition Let A = [a jk ] C mn and B C pq. Then A B := a 11 B : : : a 1n B.. a m1 B : : : a mn B 5 C mpnq : This product has the following basic properties (see, e.g., [9, 1]). Proposition Let A C C p 1 p, B D C q 1 q, E C p p, and F C q q. Then the following identities hold. 1. A (B + D) = A B + A D, (A + C) B = A B + C B.. (A B)(E F ) = (AE) (BF ):. A B is invertible if and only if A and B are invertible. In this case we have that (A B) 1 = A 1 B 1 :. (A B) T = A T B T, (A B) = A B : 5. A B = if and only if A = or B =.

30 We will frequently need the permutation matrix mn = [e 1 e n+1 : : : e (m 1)n+1 e e n+ : : : e (m 1)n+ e n e n : : : e mn ]: If A, B are m n and p q, respectively, then mp(a B) nq = B A: In the following we derive the canonical forms for doubly structured matrices that are nilpotent. This case is the most complicated case, since we have least symmetry in the spectrum. Therefore, we have to use a very technical reduction procedure. For the sake of briefness of notation, let J p denote the nilpotent Jordan block J p () of size p. O pq is the p q zero matrix. Lemma 5 Let Z p, D p, and F p be dened as in Section and let k l p q N, (p q). Then Z p Jp l = (Jp) l Z p D p Jp ld p = ( 1) l J l F p J k p Z p J k p Jq l O p qq D p Op qq F q Jq l O p qq = Op qq Z q J k+l q p = ( 1) p q Op qq F q J k+l = ( 1) p q Op qq D q F q F pjp l = ( 1) l (Jp) l F p : (9) : () q : (1) : () Denition Let A = (a jk ) nn C nn. Then the l-th lower anti-diagonal of A or, in short, the l-th anti-diagonal of A is dened by the elements a jk, where j + k = n l. Here, we allow l =. The -th anti-diagonal is also called the main anti-diagonal. If B = B ~ and C = ~C where ~ B and ~ C are square matrices, then the l-th anti-diagonal of ~ B and ~ C is called the l-th anti-diagonal of B and C, respectively. Analogously, we dene the l-th block anti-diagonal for square and non-square block matrices. Lemma Let G H C nn be Hermitian nonsingular such that the pencil %H G is nondefective and such that its spectrum is contained in f g, where Rnfg and Re() Im(). Furthermore, let A C nn be nilpotent, H-selfadjoint and G-skewadjoint. Then there exists a nonsingular matrix P C nn such that P 1 AP = A 1 : : : A k P GP = G 1 : : : G k () P HP = H 1 : : : H k 8

31 where the blocks A j, G j, H j have corresponding sizes and, for each j, are of one and only one of the following forms. Type (1d1): A j = J p () H j = "Z p and G j = ~"F p () where p N is odd if R and even if ir. Type (1d): Jp () A j = J p () H j = where p N is even if R and odd if ir. Zp Z p G j = F p F p (5) Proof. Applying Theorem 9, we may assume that (A H) is in canonical form, i.e., collecting blocks of same size and representing them by means of the Kronecker product, we may assume that A = I m1 J p H = m1 Z p I mk J pk mk Z pk where p 1 > : : : > p k are the sizes of Jordan blocks and mj are signature matrices for j = 1 : : : k. Setting I m1 F p1 F = 5 I mk F pk we obtain from A G = GA and (9) that A and F G commute. Thus, the structure of G is implicitly given by the well-known form for matrices that commute with matrices in Jordan canonical form (see [5]). For the sake of better clarity, we will not work directly on A, H, and G, but rst apply a permutation. Setting = m1 p 1 : : : mk p k and updating A, H, G by 1 A, H, G, we may consider the following situation. A = J p1 I m1 J pk I mk where H jj := Z pj mj. Partitioning G = 5 and H = G 11 : : : G 1k.. G 1k : : : G kk 9 H 11 H kk 5 : () 5 ()

32 conformably and using the well-known structures of matrices that commute with matrices in Jordan canonical form [5], we obtain that G qq = = px q 1 l= (F pq J l p q ) G (l) qq : : : : : : G () qq. G () qq G (1) qq. G () qq G (1) qq.. ( 1) pq+1 G () qq : : : : : : : : : ( 1) pq+1 G (pq 1) qq 5 (8) for q = 1 : : : k, where G qq (l) mq mq C and G qr = px r 1 l=1 Opq p rp r F pr J l p r G (l) qr (9) for 1 q < r k, with G (l) qr C mq mr. We will stepwise reduce the matrix G, while keeping the forms of A and H. Step (1): We rst show that G () jj is nonsingular for j = 1 : : : k. Since the pencil %H G is nondefective and has only the eigenvalues, where Rnfg, we obtain from Lemma 19 that GH 1 G = H: Comparing the j-th diagonal blocks on both sides, this implies in particular that H jj = G 1j H 11G 1j + : : : + G jj H jj G jj + : : : + G jk H kk G jk : (5) Because of the structure of the blocks G qr, it follows that all the block anti-diagonals of G qr H qqg qr and G qr H rr G qr are zero for q < r, and hence, comparing the main block anti-diagonals on both sides of (5), we obtain that Since F pj Z pj F pj Z pj mj = (F pj G () jj )(Z pj mj )(F pj G () jj ) = Z pj, this implies that = (F pj Z pj F pj ) (G () jj m j G () jj ) G () jj m j G () jj = 1 m j (51) and thus, G () jj is nonsingular.

33 Step (): Elimination of G 1 : : : G 1k Assume that we already have G (l 1) 1j = for all j = : : : k and G (l) 1j = for j = : : : r 1, where l and r. We then show how to eliminate G 1r, (l) while keeping the forms of A and H. Let X = r I r X r1 have a block form analogous to G, where zero blocks of the matrix are indicated by blanks and, moreover, X 1r = J l p r O p1 p rp r X 1r 1 ( 1)p 1 p r+1 (G () 11) 1 G (l) 1r X r1 = O prp 1 p r J l p r 1 ( 1)l+1 (G () rr) (G (l) 1r) : Substep (a) X is chosen such that it commutes with A. I 5 ^= Substep (b) In the updated matrix ~ G := X GX we have ~ G (l) 1r =. : : : Indeed, it is easy to see that ~ G is again a matrix of the form (), (8), and (9). The (1 r)-block of ~ G satises ~ G1r = G11X1r + X r1 G 1r X 1r + G 1r + X r1 G rr: (5) From the structure of G and X, we nd immediately that the rst l 1 block anti-diagonals of all the summands of the right hand side of (5) are zero. Furthermore, the l-th block anti-diagonal of G1r ~ has the form (F p1 G () 11) + Op1 p rp r F pr J l p r + Oprp 1 p r (Jp l r ) J l p r O p1 p rp r G (l) 1r 1 ( 1)l+1 G (l) = 1 ( 1)p 1 p r+1 Jp F l r p1 + 1 ( 1)l+1 Oprp 1 p r (J l p r ) O p1 p rp r 1 ( 1)p 1 p r+1 (G () 11) 1 G (l) 1r 1r(G () rr) 1 G (l) 1r + F pr G (l) 1r = 1 (F pr G () rr) Op1 p rp r F pr Jp l r G (l) 1r 1 5 C A

34 using (9) and (1). Substep (c) In the updated matrix G ~ := X GX we still have G ~ (l 1) 1j = for all j = : : : k and G ~ (l) 1j = for j = : : : r 1. Indeed, the elements of the rst block row of ~ G have the form G 1q + X r1 G qr for 1 < q < r and G 1q + X r1 G rq for r < q: From the block structure of G 1q, G rq, G qr, and X r1, we obtain that the rst p q p r + l block anti-diagonals in X r1 G qr and the rst p r p q + l 1 block anti-diagonals in X r1 G rq are zero. Substep (d) We show that the matrix ~ H := X HX is block diagonal. The only changes outside the block diagonal can have happened to the (1 r)-block ~ H 1r and the (r 1)-block ~ Hr1 = ~ H 1r. The (1 r)-block has the form ~H 1r = (Z p1 m1 )X 1r + X 1r(Z pr mr ) (5) = 1 Op1 p rp r Z pr J l p r (5) ( 1) p 1 p r+1 m1 (G () 11) 1 G (l) 1r + ( 1) l+1 G (l) 1r(G () rr) 1 mr (55) using (9) and (). On the other hand, we have GH 1 G = H. Noting that H 1 = H and comparing the (1 r) blocks of both sides, we obtain that = G 11 H 11 G 1r + r 1 X q= G 1q H qq G qr! + G 1r H rr G rr + kx q=r+1 G 1q H qq G rq! : (5) Clearly, the rst l 1 anti-diagonals of all the summands in (5) are zero. We now consider the l-th block anti-diagonal. We note that G 11 H 11 G 1r and G 1r H rr G rr are the only summands that have a nonzero l-th block anti-diagonal. For the terms G 1q H qq G qr, 1 < q < r this follows from the fact that the l-th block anti-diagonal of G 1q is already zero. For G 1q H qq G rq, q > r this can be seen as follows. If we write the j-th block antidiagonal of G 1q H qq G rq in the form S j T j, then we obtain p q p 1 p q S j = p q F pq Jp j Z pq h p r p q p q i 1 p q C F p q A q = p r p q p q p 1 p r p r p q p q F pq Jp j q Z pq F p q 5 :

Schur-Like Forms for Matrix Lie Groups, Lie Algebras and Jordan Algebras

Schur-Like Forms for Matrix Lie Groups, Lie Algebras and Jordan Algebras Schur-Like Forms for Matrix Lie Groups, Lie Algebras and Jordan Algebras Gregory Ammar Department of Mathematical Sciences Northern Illinois University DeKalb, IL 65 USA Christian Mehl Fakultät für Mathematik

More information

On doubly structured matrices and pencils that arise in linear response theory

On doubly structured matrices and pencils that arise in linear response theory On doubly structured matrices and pencils that arise in linear response theory Christian Mehl Volker Mehrmann Hongguo Xu Abstract We discuss matrix pencils with a double symmetry structure that arise in

More information

Contents 1 Introduction and Preliminaries 1 Embedding of Extended Matrix Pencils 3 Hamiltonian Triangular Forms 1 4 Skew-Hamiltonian/Hamiltonian Matri

Contents 1 Introduction and Preliminaries 1 Embedding of Extended Matrix Pencils 3 Hamiltonian Triangular Forms 1 4 Skew-Hamiltonian/Hamiltonian Matri Technische Universitat Chemnitz Sonderforschungsbereich 393 Numerische Simulation auf massiv parallelen Rechnern Peter Benner Ralph Byers Volker Mehrmann Hongguo Xu Numerical Computation of Deating Subspaces

More information

Definite versus Indefinite Linear Algebra. Christian Mehl Institut für Mathematik TU Berlin Germany. 10th SIAM Conference on Applied Linear Algebra

Definite versus Indefinite Linear Algebra. Christian Mehl Institut für Mathematik TU Berlin Germany. 10th SIAM Conference on Applied Linear Algebra Definite versus Indefinite Linear Algebra Christian Mehl Institut für Mathematik TU Berlin Germany 10th SIAM Conference on Applied Linear Algebra Monterey Bay Seaside, October 26-29, 2009 Indefinite Linear

More information

Canonical forms of structured matrices and pencils

Canonical forms of structured matrices and pencils Canonical forms of structured matrices and pencils Christian Mehl and Hongguo Xu Abstract This chapter provides a survey on the development of canonical forms for matrices and matrix pencils with symmetry

More information

Finite dimensional indefinite inner product spaces and applications in Numerical Analysis

Finite dimensional indefinite inner product spaces and applications in Numerical Analysis Finite dimensional indefinite inner product spaces and applications in Numerical Analysis Christian Mehl Technische Universität Berlin, Institut für Mathematik, MA 4-5, 10623 Berlin, Germany, Email: mehl@math.tu-berlin.de

More information

Structured Krylov Subspace Methods for Eigenproblems with Spectral Symmetries

Structured Krylov Subspace Methods for Eigenproblems with Spectral Symmetries Structured Krylov Subspace Methods for Eigenproblems with Spectral Symmetries Fakultät für Mathematik TU Chemnitz, Germany Peter Benner benner@mathematik.tu-chemnitz.de joint work with Heike Faßbender

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

Perturbation theory for eigenvalues of Hermitian pencils. Christian Mehl Institut für Mathematik TU Berlin, Germany. 9th Elgersburg Workshop

Perturbation theory for eigenvalues of Hermitian pencils. Christian Mehl Institut für Mathematik TU Berlin, Germany. 9th Elgersburg Workshop Perturbation theory for eigenvalues of Hermitian pencils Christian Mehl Institut für Mathematik TU Berlin, Germany 9th Elgersburg Workshop Elgersburg, March 3, 2014 joint work with Shreemayee Bora, Michael

More information

QUASI-UNIFORMLY POSITIVE OPERATORS IN KREIN SPACE. Denitizable operators in Krein spaces have spectral properties similar to those

QUASI-UNIFORMLY POSITIVE OPERATORS IN KREIN SPACE. Denitizable operators in Krein spaces have spectral properties similar to those QUASI-UNIFORMLY POSITIVE OPERATORS IN KREIN SPACE BRANKO CURGUS and BRANKO NAJMAN Denitizable operators in Krein spaces have spectral properties similar to those of selfadjoint operators in Hilbert spaces.

More information

Extending Results from Orthogonal Matrices to the Class of P -orthogonal Matrices

Extending Results from Orthogonal Matrices to the Class of P -orthogonal Matrices Extending Results from Orthogonal Matrices to the Class of P -orthogonal Matrices João R. Cardoso Instituto Superior de Engenharia de Coimbra Quinta da Nora 3040-228 Coimbra Portugal jocar@isec.pt F. Silva

More information

Singular-value-like decomposition for complex matrix triples

Singular-value-like decomposition for complex matrix triples Singular-value-like decomposition for complex matrix triples Christian Mehl Volker Mehrmann Hongguo Xu December 17 27 Dedicated to William B Gragg on the occasion of his 7th birthday Abstract The classical

More information

ARTICLE IN PRESS. Linear Algebra and its Applications xx (2002) xxx xxx

ARTICLE IN PRESS. Linear Algebra and its Applications xx (2002) xxx xxx Linear Algebra and its Applications xx (2002) xxx xxx www.elsevier.com/locate/laa On doubly structured matrices and pencils that arise in linear response theory Christian Mehl a Volker Mehrmann a 1 Hongguo

More information

PROOF OF TWO MATRIX THEOREMS VIA TRIANGULAR FACTORIZATIONS ROY MATHIAS

PROOF 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 information

Linear Algebra: Matrix Eigenvalue Problems

Linear Algebra: Matrix Eigenvalue Problems CHAPTER8 Linear Algebra: Matrix Eigenvalue Problems Chapter 8 p1 A matrix eigenvalue problem considers the vector equation (1) Ax = λx. 8.0 Linear Algebra: Matrix Eigenvalue Problems Here A is a given

More information

An SVD-Like Matrix Decomposition and Its Applications

An SVD-Like Matrix Decomposition and Its Applications An SVD-Like Matrix Decomposition and Its Applications Hongguo Xu Abstract [ A matrix S C m m is symplectic if SJS = J, where J = I m I m. Symplectic matrices play an important role in the analysis and

More information

On the Definition of Two Natural Classes of Scalar Product

On the Definition of Two Natural Classes of Scalar Product On the Definition of Two Natural Classes of Scalar Product D. Steven Mackey, Niloufer Mackey and Françoise Tisseur Abstract. We identify two natural classes of scalar product, termed unitary and orthosymmetric,

More information

STABILITY OF INVARIANT SUBSPACES OF COMMUTING MATRICES We obtain some further results for pairs of commuting matrices. We show that a pair of commutin

STABILITY OF INVARIANT SUBSPACES OF COMMUTING MATRICES We obtain some further results for pairs of commuting matrices. We show that a pair of commutin On the stability of invariant subspaces of commuting matrices Tomaz Kosir and Bor Plestenjak September 18, 001 Abstract We study the stability of (joint) invariant subspaces of a nite set of commuting

More information

Generic rank-k perturbations of structured matrices

Generic rank-k perturbations of structured matrices Generic rank-k perturbations of structured matrices Leonhard Batzke, Christian Mehl, André C. M. Ran and Leiba Rodman Abstract. This paper deals with the effect of generic but structured low rank perturbations

More information

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators. MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators. Adjoint operator and adjoint matrix Given a linear operator L on an inner product space V, the adjoint of L is a transformation

More information

Eigenvalue perturbation theory of structured real matrices and their sign characteristics under generic structured rank-one perturbations

Eigenvalue perturbation theory of structured real matrices and their sign characteristics under generic structured rank-one perturbations Eigenvalue perturbation theory of structured real matrices and their sign characteristics under generic structured rank-one perturbations Christian Mehl Volker Mehrmann André C. M. Ran Leiba Rodman April

More information

ON THE MATRIX EQUATION XA AX = X P

ON THE MATRIX EQUATION XA AX = X P ON THE MATRIX EQUATION XA AX = X P DIETRICH BURDE Abstract We study the matrix equation XA AX = X p in M n (K) for 1 < p < n It is shown that every matrix solution X is nilpotent and that the generalized

More information

THE RELATION BETWEEN THE QR AND LR ALGORITHMS

THE 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 information

Matrices. Chapter What is a Matrix? We review the basic matrix operations. An array of numbers a a 1n A = a m1...

Matrices. Chapter What is a Matrix? We review the basic matrix operations. An array of numbers a a 1n A = a m1... Chapter Matrices We review the basic matrix operations What is a Matrix? An array of numbers a a n A = a m a mn with m rows and n columns is a m n matrix Element a ij in located in position (i, j The elements

More information

FORTRAN 77 Subroutines for the Solution of Skew-Hamiltonian/Hamiltonian Eigenproblems Part I: Algorithms and Applications 1

FORTRAN 77 Subroutines for the Solution of Skew-Hamiltonian/Hamiltonian Eigenproblems Part I: Algorithms and Applications 1 SLICOT Working Note 2013-1 FORTRAN 77 Subroutines for the Solution of Skew-Hamiltonian/Hamiltonian Eigenproblems Part I: Algorithms and Applications 1 Peter Benner 2 Vasile Sima 3 Matthias Voigt 4 August

More information

Multiple eigenvalues

Multiple 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 information

1 Vectors. Notes for Bindel, Spring 2017 Numerical Analysis (CS 4220)

1 Vectors. Notes for Bindel, Spring 2017 Numerical Analysis (CS 4220) Notes for 2017-01-30 Most of mathematics is best learned by doing. Linear algebra is no exception. You have had a previous class in which you learned the basics of linear algebra, and you will have plenty

More information

The Kalman-Yakubovich-Popov Lemma for Differential-Algebraic Equations with Applications

The Kalman-Yakubovich-Popov Lemma for Differential-Algebraic Equations with Applications MAX PLANCK INSTITUTE Elgersburg Workshop Elgersburg February 11-14, 2013 The Kalman-Yakubovich-Popov Lemma for Differential-Algebraic Equations with Applications Timo Reis 1 Matthias Voigt 2 1 Department

More information

Theorem Let X be a symmetric solution of DR(X) = 0. Let X b be a symmetric approximation to Xand set V := X? X. b If R b = R + B T XB b and I + V B R

Theorem Let X be a symmetric solution of DR(X) = 0. Let X b be a symmetric approximation to Xand set V := X? X. b If R b = R + B T XB b and I + V B R Initializing Newton's method for discrete-time algebraic Riccati equations using the buttery SZ algorithm Heike Fabender Zentrum fur Technomathematik Fachbereich - Mathematik und Informatik Universitat

More information

CANONICAL FORMS, HIGHER RANK NUMERICAL RANGES, TOTALLY ISOTROPIC SUBSPACES, AND MATRIX EQUATIONS

CANONICAL FORMS, HIGHER RANK NUMERICAL RANGES, TOTALLY ISOTROPIC SUBSPACES, AND MATRIX EQUATIONS CANONICAL FORMS, HIGHER RANK NUMERICAL RANGES, TOTALLY ISOTROPIC SUBSPACES, AND MATRIX EQUATIONS CHI-KWONG LI AND NUNG-SING SZE Abstract. Results on matrix canonical forms are used to give a complete description

More information

On families of anticommuting matrices

On families of anticommuting matrices On families of anticommuting matrices Pavel Hrubeš December 18, 214 Abstract Let e 1,..., e k be complex n n matrices such that e ie j = e je i whenever i j. We conjecture that rk(e 2 1) + rk(e 2 2) +

More information

Trimmed linearizations for structured matrix polynomials

Trimmed linearizations for structured matrix polynomials Trimmed linearizations for structured matrix polynomials Ralph Byers Volker Mehrmann Hongguo Xu January 5 28 Dedicated to Richard S Varga on the occasion of his 8th birthday Abstract We discuss the eigenvalue

More information

Nonlinear palindromic eigenvalue problems and their numerical solution

Nonlinear palindromic eigenvalue problems and their numerical solution Nonlinear palindromic eigenvalue problems and their numerical solution TU Berlin DFG Research Center Institut für Mathematik MATHEON IN MEMORIAM RALPH BYERS Polynomial eigenvalue problems k P(λ) x = (

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

Orthogonal similarity of a real matrix and its transpose

Orthogonal 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 information

Structured decompositions for matrix triples: SVD-like concepts for structured matrices

Structured decompositions for matrix triples: SVD-like concepts for structured matrices Structured decompositions for matrix triples: SVD-like concepts for structured matrices Christian Mehl Volker Mehrmann Hongguo Xu July 1 8 In memory of Ralph Byers (1955-7) Abstract Canonical forms for

More information

Complementary bases in symplectic matrices and a proof that their determinant is one

Complementary bases in symplectic matrices and a proof that their determinant is one Linear Algebra and its Applications 419 (2006) 772 778 www.elsevier.com/locate/laa Complementary bases in symplectic matrices and a proof that their determinant is one Froilán M. Dopico a,, Charles R.

More information

1 Linear Algebra Problems

1 Linear Algebra Problems Linear Algebra Problems. Let A be the conjugate transpose of the complex matrix A; i.e., A = A t : A is said to be Hermitian if A = A; real symmetric if A is real and A t = A; skew-hermitian if A = A and

More information

QUASI-DIAGONALIZABLE AND CONGRUENCE-NORMAL MATRICES. 1. Introduction. A matrix A C n n is normal if AA = A A. A is said to be conjugate-normal if

QUASI-DIAGONALIZABLE AND CONGRUENCE-NORMAL MATRICES. 1. Introduction. A matrix A C n n is normal if AA = A A. A is said to be conjugate-normal if QUASI-DIAGONALIZABLE AND CONGRUENCE-NORMAL MATRICES H. FAßBENDER AND KH. D. IKRAMOV Abstract. A matrix A C n n is unitarily quasi-diagonalizable if A can be brought by a unitary similarity transformation

More information

STAT 309: MATHEMATICAL COMPUTATIONS I FALL 2018 LECTURE 13

STAT 309: MATHEMATICAL COMPUTATIONS I FALL 2018 LECTURE 13 STAT 309: MATHEMATICAL COMPUTATIONS I FALL 208 LECTURE 3 need for pivoting we saw that under proper circumstances, we can write A LU where 0 0 0 u u 2 u n l 2 0 0 0 u 22 u 2n L l 3 l 32, U 0 0 0 l n l

More information

STRUCTURE PRESERVING DEFLATION OF INFINITE EIGENVALUES IN STRUCTURED PENCILS

STRUCTURE PRESERVING DEFLATION OF INFINITE EIGENVALUES IN STRUCTURED PENCILS Electronic Transactions on Numerical Analysis Volume 44 pp 1 24 215 Copyright c 215 ISSN 168 9613 ETNA STRUCTURE PRESERVING DEFLATION OF INFINITE EIGENVALUES IN STRUCTURED PENCILS VOLKER MEHRMANN AND HONGGUO

More information

ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA

ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA ALGEBRA QUALIFYING EXAM PROBLEMS LINEAR ALGEBRA Kent State University Department of Mathematical Sciences Compiled and Maintained by Donald L. White Version: August 29, 2017 CONTENTS LINEAR ALGEBRA AND

More information

RANK AND PERIMETER PRESERVER OF RANK-1 MATRICES OVER MAX ALGEBRA

RANK AND PERIMETER PRESERVER OF RANK-1 MATRICES OVER MAX ALGEBRA Discussiones Mathematicae General Algebra and Applications 23 (2003 ) 125 137 RANK AND PERIMETER PRESERVER OF RANK-1 MATRICES OVER MAX ALGEBRA Seok-Zun Song and Kyung-Tae Kang Department of Mathematics,

More information

ISOLATED SEMIDEFINITE SOLUTIONS OF THE CONTINUOUS-TIME ALGEBRAIC RICCATI EQUATION

ISOLATED SEMIDEFINITE SOLUTIONS OF THE CONTINUOUS-TIME ALGEBRAIC RICCATI EQUATION ISOLATED SEMIDEFINITE SOLUTIONS OF THE CONTINUOUS-TIME ALGEBRAIC RICCATI EQUATION Harald K. Wimmer 1 The set of all negative-semidefinite solutions of the CARE A X + XA + XBB X C C = 0 is homeomorphic

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

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 1 x 2. x n 8 (4) 3 4 2

MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS. + + x 1 x 2. x n 8 (4) 3 4 2 MATRIX ALGEBRA AND SYSTEMS OF EQUATIONS SYSTEMS OF EQUATIONS AND MATRICES Representation of a linear system The general system of m equations in n unknowns can be written a x + a 2 x 2 + + a n x n b a

More information

A note on the product of two skew-hamiltonian matrices

A note on the product of two skew-hamiltonian matrices A note on the product of two skew-hamiltonian matrices H. Faßbender and Kh. D. Ikramov October 13, 2007 Abstract: We show that the product C of two skew-hamiltonian matrices obeys the Stenzel conditions.

More information

Jordan Canonical Form of A Partitioned Complex Matrix and Its Application to Real Quaternion Matrices

Jordan Canonical Form of A Partitioned Complex Matrix and Its Application to Real Quaternion Matrices COMMUNICATIONS IN ALGEBRA, 29(6, 2363-2375(200 Jordan Canonical Form of A Partitioned Complex Matrix and Its Application to Real Quaternion Matrices Fuzhen Zhang Department of Math Science and Technology

More information

MAPPING AND PRESERVER PROPERTIES OF THE PRINCIPAL PIVOT TRANSFORM

MAPPING AND PRESERVER PROPERTIES OF THE PRINCIPAL PIVOT TRANSFORM MAPPING AND PRESERVER PROPERTIES OF THE PRINCIPAL PIVOT TRANSFORM OLGA SLYUSAREVA AND MICHAEL TSATSOMEROS Abstract. The principal pivot transform (PPT) is a transformation of a matrix A tantamount to exchanging

More information

Linear and Multilinear Algebra. Linear maps preserving rank of tensor products of matrices

Linear 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 information

Dampening Controllers via a Riccati Equation. Approach. Pod vodarenskou vez Praha 8. Czech Republic. Fakultat fur Mathematik

Dampening Controllers via a Riccati Equation. Approach. Pod vodarenskou vez Praha 8. Czech Republic. Fakultat fur Mathematik Dampening Controllers via a Riccati Equation Approach J.J. Hench y C. He z V. Kucera y V. Mehrmann z y Institute of Information Theory and Automation Academy of Sciences of the Czech Republic Pod vodarenskou

More information

Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors Chapter 1 Eigenvalues and Eigenvectors Among problems in numerical linear algebra, the determination of the eigenvalues and eigenvectors of matrices is second in importance only to the solution of linear

More information

Linearizing Symmetric Matrix Polynomials via Fiedler pencils with Repetition

Linearizing Symmetric Matrix Polynomials via Fiedler pencils with Repetition Linearizing Symmetric Matrix Polynomials via Fiedler pencils with Repetition Kyle Curlett Maribel Bueno Cachadina, Advisor March, 2012 Department of Mathematics Abstract Strong linearizations of a matrix

More information

Index. for generalized eigenvalue problem, butterfly form, 211

Index. for generalized eigenvalue problem, butterfly form, 211 Index ad hoc shifts, 165 aggressive early deflation, 205 207 algebraic multiplicity, 35 algebraic Riccati equation, 100 Arnoldi process, 372 block, 418 Hamiltonian skew symmetric, 420 implicitly restarted,

More information

Characterization of half-radial matrices

Characterization 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 information

Math 408 Advanced Linear Algebra

Math 408 Advanced Linear Algebra Math 408 Advanced Linear Algebra Chi-Kwong Li Chapter 4 Hermitian and symmetric matrices Basic properties Theorem Let A M n. The following are equivalent. Remark (a) A is Hermitian, i.e., A = A. (b) x

More information

Central Groupoids, Central Digraphs, and Zero-One Matrices A Satisfying A 2 = J

Central Groupoids, Central Digraphs, and Zero-One Matrices A Satisfying A 2 = J Central Groupoids, Central Digraphs, and Zero-One Matrices A Satisfying A 2 = J Frank Curtis, John Drew, Chi-Kwong Li, and Daniel Pragel September 25, 2003 Abstract We study central groupoids, central

More information

Low Rank Perturbations of Quaternion Matrices

Low Rank Perturbations of Quaternion Matrices Electronic Journal of Linear Algebra Volume 32 Volume 32 (2017) Article 38 2017 Low Rank Perturbations of Quaternion Matrices Christian Mehl TU Berlin, mehl@mathtu-berlinde Andre CM Ran Vrije Universiteit

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

Numerical Linear Algebra Homework Assignment - Week 2

Numerical Linear Algebra Homework Assignment - Week 2 Numerical Linear Algebra Homework Assignment - Week 2 Đoàn Trần Nguyên Tùng Student ID: 1411352 8th October 2016 Exercise 2.1: Show that if a matrix A is both triangular and unitary, then it is diagonal.

More information

4.1 Eigenvalues, Eigenvectors, and The Characteristic Polynomial

4.1 Eigenvalues, Eigenvectors, and The Characteristic Polynomial Linear Algebra (part 4): Eigenvalues, Diagonalization, and the Jordan Form (by Evan Dummit, 27, v ) Contents 4 Eigenvalues, Diagonalization, and the Jordan Canonical Form 4 Eigenvalues, Eigenvectors, and

More information

ACI-matrices all of whose completions have the same rank

ACI-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 information

Eigenvalue perturbation theory of classes of structured matrices under generic structured rank one perturbations

Eigenvalue perturbation theory of classes of structured matrices under generic structured rank one perturbations Eigenvalue perturbation theory of classes of structured matrices under generic structured rank one perturbations, VU Amsterdam and North West University, South Africa joint work with: Leonard Batzke, Christian

More information

Algebra C Numerical Linear Algebra Sample Exam Problems

Algebra C Numerical Linear Algebra Sample Exam Problems Algebra C Numerical Linear Algebra Sample Exam Problems Notation. Denote by V a finite-dimensional Hilbert space with inner product (, ) and corresponding norm. The abbreviation SPD is used for symmetric

More information

Positive definite preserving linear transformations on symmetric matrix spaces

Positive 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 information

Refined Inertia of Matrix Patterns

Refined Inertia of Matrix Patterns Electronic Journal of Linear Algebra Volume 32 Volume 32 (2017) Article 24 2017 Refined Inertia of Matrix Patterns Kevin N. Vander Meulen Redeemer University College, kvanderm@redeemer.ca Jonathan Earl

More information

Section 3.2. Multiplication of Matrices and Multiplication of Vectors and Matrices

Section 3.2. Multiplication of Matrices and Multiplication of Vectors and Matrices 3.2. Multiplication of Matrices and Multiplication of Vectors and Matrices 1 Section 3.2. Multiplication of Matrices and Multiplication of Vectors and Matrices Note. In this section, we define the product

More information

Linear Algebra (part 1) : Matrices and Systems of Linear Equations (by Evan Dummit, 2016, v. 2.02)

Linear Algebra (part 1) : Matrices and Systems of Linear Equations (by Evan Dummit, 2016, v. 2.02) Linear Algebra (part ) : Matrices and Systems of Linear Equations (by Evan Dummit, 206, v 202) Contents 2 Matrices and Systems of Linear Equations 2 Systems of Linear Equations 2 Elimination, Matrix Formulation

More information

Matrix Mathematics. Theory, Facts, and Formulas with Application to Linear Systems Theory. Dennis S. Bernstein

Matrix Mathematics. Theory, Facts, and Formulas with Application to Linear Systems Theory. Dennis S. Bernstein Matrix Mathematics Theory, Facts, and Formulas with Application to Linear Systems Theory Dennis S. Bernstein PRINCETON UNIVERSITY PRESS PRINCETON AND OXFORD Contents Special Symbols xv Conventions, Notation,

More information

MATH 425-Spring 2010 HOMEWORK ASSIGNMENTS

MATH 425-Spring 2010 HOMEWORK ASSIGNMENTS MATH 425-Spring 2010 HOMEWORK ASSIGNMENTS Instructor: Shmuel Friedland Department of Mathematics, Statistics and Computer Science email: friedlan@uic.edu Last update April 18, 2010 1 HOMEWORK ASSIGNMENT

More information

Linear Algebra and its Applications

Linear Algebra and its Applications Linear Algebra and its Applications 435 (011) 1845 1856 Contents lists available at ScienceDirect Linear Algebra and its Applications journal homepage: wwwelseviercom/locate/laa Hurwitz rational functions

More information

Operators with numerical range in a closed halfplane

Operators with numerical range in a closed halfplane Operators with numerical range in a closed halfplane Wai-Shun Cheung 1 Department of Mathematics, University of Hong Kong, Hong Kong, P. R. China. wshun@graduate.hku.hk Chi-Kwong Li 2 Department of Mathematics,

More information

A Numerical Algorithm for Block-Diagonal Decomposition of Matrix -Algebras, Part II: General Algorithm

A Numerical Algorithm for Block-Diagonal Decomposition of Matrix -Algebras, Part II: General Algorithm A Numerical Algorithm for Block-Diagonal Decomposition of Matrix -Algebras, Part II: General Algorithm Takanori Maehara and Kazuo Murota May 2008 / May 2009 Abstract An algorithm is proposed for finding

More information

ON ORTHOGONAL REDUCTION TO HESSENBERG FORM WITH SMALL BANDWIDTH

ON ORTHOGONAL REDUCTION TO HESSENBERG FORM WITH SMALL BANDWIDTH ON ORTHOGONAL REDUCTION TO HESSENBERG FORM WITH SMALL BANDWIDTH V. FABER, J. LIESEN, AND P. TICHÝ Abstract. Numerous algorithms in numerical linear algebra are based on the reduction of a given matrix

More information

Math 489AB Exercises for Chapter 2 Fall Section 2.3

Math 489AB Exercises for Chapter 2 Fall Section 2.3 Math 489AB Exercises for Chapter 2 Fall 2008 Section 2.3 2.3.3. Let A M n (R). Then the eigenvalues of A are the roots of the characteristic polynomial p A (t). Since A is real, p A (t) is a polynomial

More information

AMS526: Numerical Analysis I (Numerical Linear Algebra)

AMS526: Numerical Analysis I (Numerical Linear Algebra) AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 16: Eigenvalue Problems; Similarity Transformations Xiangmin Jiao Stony Brook University Xiangmin Jiao Numerical Analysis I 1 / 18 Eigenvalue

More information

The Eigenvalue Shift Technique and Its Eigenstructure Analysis of a Matrix

The 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 information

Improved Newton s method with exact line searches to solve quadratic matrix equation

Improved Newton s method with exact line searches to solve quadratic matrix equation Journal of Computational and Applied Mathematics 222 (2008) 645 654 wwwelseviercom/locate/cam Improved Newton s method with exact line searches to solve quadratic matrix equation Jian-hui Long, Xi-yan

More information

Finding eigenvalues for matrices acting on subspaces

Finding eigenvalues for matrices acting on subspaces Finding eigenvalues for matrices acting on subspaces Jakeniah Christiansen Department of Mathematics and Statistics Calvin College Grand Rapids, MI 49546 Faculty advisor: Prof Todd Kapitula Department

More information

Math 108b: Notes on the Spectral Theorem

Math 108b: Notes on the Spectral Theorem Math 108b: Notes on the Spectral Theorem From section 6.3, we know that every linear operator T on a finite dimensional inner product space V has an adjoint. (T is defined as the unique linear operator

More information

Linear algebra properties of dissipative Hamiltonian descriptor systems

Linear algebra properties of dissipative Hamiltonian descriptor systems Linear algebra properties of dissipative Hamiltonian descriptor systems C. Mehl V. Mehrmann M. Wojtylak January 6, 28 Abstract A wide class of matrix pencils connected with dissipative Hamiltonian descriptor

More information

Projectors for matrix pencils

Projectors for matrix pencils Projectors for matrix pencils Roswitha März Abstract In this paper, basic properties of projector sequences for matrix pairs which can be used for analyzing differential algebraic systems are collected.

More information

Boolean Inner-Product Spaces and Boolean Matrices

Boolean Inner-Product Spaces and Boolean Matrices Boolean Inner-Product Spaces and Boolean Matrices Stan Gudder Department of Mathematics, University of Denver, Denver CO 80208 Frédéric Latrémolière Department of Mathematics, University of Denver, Denver

More information

only nite eigenvalues. This is an extension of earlier results from [2]. Then we concentrate on the Riccati equation appearing in H 2 and linear quadr

only nite eigenvalues. This is an extension of earlier results from [2]. Then we concentrate on the Riccati equation appearing in H 2 and linear quadr The discrete algebraic Riccati equation and linear matrix inequality nton. Stoorvogel y Department of Mathematics and Computing Science Eindhoven Univ. of Technology P.O. ox 53, 56 M Eindhoven The Netherlands

More information

A PRIMER ON SESQUILINEAR FORMS

A PRIMER ON SESQUILINEAR FORMS A PRIMER ON SESQUILINEAR FORMS BRIAN OSSERMAN This is an alternative presentation of most of the material from 8., 8.2, 8.3, 8.4, 8.5 and 8.8 of Artin s book. Any terminology (such as sesquilinear form

More information

e jk :=< e k, e j >, e[t ] e = E 1 ( e [T ] e ) h E.

e jk :=< e k, e j >, e[t ] e = E 1 ( e [T ] e ) h E. onjb.tex 14.02.23 Orthonormal Jordan bases in finite dimensional Hilbert spaces 1. Introduction and terminology The aim of this paper is twofold: first, we give a necessary and sufficient condition for

More information

c 2006 Society for Industrial and Applied Mathematics

c 2006 Society for Industrial and Applied Mathematics SIAM J. MATRIX ANAL. APPL. Vol. 28, No. 4, pp. 029 05 c 2006 Society for Industrial and Applied Mathematics STRUCTURED POLYNOMIAL EIGENVALUE PROBLEMS: GOOD VIBRATIONS FROM GOOD LINEARIZATIONS D. STEVEN

More information

Eventually reducible matrix, eventually nonnegative matrix, eventually r-cyclic

Eventually reducible matrix, eventually nonnegative matrix, eventually r-cyclic December 15, 2012 EVENUAL PROPERIES OF MARICES LESLIE HOGBEN AND ULRICA WILSON Abstract. An eventual property of a matrix M C n n is a property that holds for all powers M k, k k 0, for some positive integer

More information

Algebra Exam Syllabus

Algebra Exam Syllabus Algebra Exam Syllabus The Algebra comprehensive exam covers four broad areas of algebra: (1) Groups; (2) Rings; (3) Modules; and (4) Linear Algebra. These topics are all covered in the first semester graduate

More information

arxiv: v3 [math.ra] 10 Jun 2016

arxiv: v3 [math.ra] 10 Jun 2016 To appear in Linear and Multilinear Algebra Vol. 00, No. 00, Month 0XX, 1 10 The critical exponent for generalized doubly nonnegative matrices arxiv:1407.7059v3 [math.ra] 10 Jun 016 Xuchen Han a, Charles

More information

On the Smooth Feshbach-Schur Map

On the Smooth Feshbach-Schur Map On the Smooth Feshbach-Schur Map M. Griesemer 1 and D. Hasler 2 1. Fachbereich Mathematik, Universität Stuttgart, D-70569 Stuttgart, Deutschland 2. Department of Mathematics, University of Virginia, Charlottesville,

More information

This can be accomplished by left matrix multiplication as follows: I

This can be accomplished by left matrix multiplication as follows: I 1 Numerical Linear Algebra 11 The LU Factorization Recall from linear algebra that Gaussian elimination is a method for solving linear systems of the form Ax = b, where A R m n and bran(a) In this method

More information

Structured eigenvalue/eigenvector backward errors of matrix pencils arising in optimal control

Structured eigenvalue/eigenvector backward errors of matrix pencils arising in optimal control Electronic Journal of Linear Algebra Volume 34 Volume 34 08) Article 39 08 Structured eigenvalue/eigenvector backward errors of matrix pencils arising in optimal control Christian Mehl Technische Universitaet

More information

THREE LECTURES ON QUASIDETERMINANTS

THREE LECTURES ON QUASIDETERMINANTS THREE LECTURES ON QUASIDETERMINANTS Robert Lee Wilson Department of Mathematics Rutgers University The determinant of a matrix with entries in a commutative ring is a main organizing tool in commutative

More information

arxiv: v1 [math.rt] 7 Oct 2014

arxiv: v1 [math.rt] 7 Oct 2014 A direct approach to the rational normal form arxiv:1410.1683v1 [math.rt] 7 Oct 2014 Klaus Bongartz 8. Oktober 2014 In courses on linear algebra the rational normal form of a matrix is usually derived

More information

Linear Operators Preserving the Numerical Range (Radius) on Triangular Matrices

Linear Operators Preserving the Numerical Range (Radius) on Triangular Matrices Linear Operators Preserving the Numerical Range (Radius) on Triangular Matrices Chi-Kwong Li Department of Mathematics, College of William & Mary, P.O. Box 8795, Williamsburg, VA 23187-8795, USA. E-mail:

More information

Synopsis of Numerical Linear Algebra

Synopsis of Numerical Linear Algebra Synopsis of Numerical Linear Algebra Eric de Sturler Department of Mathematics, Virginia Tech sturler@vt.edu http://www.math.vt.edu/people/sturler Iterative Methods for Linear Systems: Basics to Research

More information

Solvability of Linear Matrix Equations in a Symmetric Matrix Variable

Solvability of Linear Matrix Equations in a Symmetric Matrix Variable Solvability of Linear Matrix Equations in a Symmetric Matrix Variable Maurcio C. de Oliveira J. William Helton Abstract We study the solvability of generalized linear matrix equations of the Lyapunov type

More information

Bounds on fast decodability of space-time block codes, skew-hermitian matrices, and Azumaya algebras

Bounds on fast decodability of space-time block codes, skew-hermitian matrices, and Azumaya algebras Bounds on fast decodability of space-time block codes, skew-hermitian matrices, and Azumaya algebras Grégory Berhuy, Nadya Markin, and B. A. Sethuraman 1 Abstract We study fast lattice decodability of

More information

Maximizing the numerical radii of matrices by permuting their entries

Maximizing the numerical radii of matrices by permuting their entries Maximizing the numerical radii of matrices by permuting their entries Wai-Shun Cheung and Chi-Kwong Li Dedicated to Professor Pei Yuan Wu. Abstract Let A be an n n complex matrix such that every row and

More information