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Research Article

Decay estimates of Green's matrices for discrete-time linear periodic systems

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Received 03 Jun 2022, Accepted 03 Jul 2023, Published online: 16 Sep 2023

Abstract

We study periodic Lyapunov matrix equations for a general discrete-time linear periodic system BpxpApxp1=fp, where the matrix coefficients Bp and Ap can be singular. The block coefficients of the inverse operator of the system are referred to as the Green matrices. We derive new decay estimates of the Green matrices in terms of the spectral norms of special solutions to the periodic Lyapunov matrix equations. The study is based on the periodic Schur decomposition of matrices.

1. Introduction

Linear periodic systems are frequently used mathematical models of periodic phenomena. A variety of such models is presented in the IFAC proceedings volume [Citation1]. The book [Citation2] provides fundamentals of modelling and analysis of both continuous and discrete-time linear periodic systems with emphasis on control theory. Modern algorithmic and computational issues related to linear periodic systems are discussed in papers [Citation3–5].

We are concerned with the general discrete-time linear periodic systems here, namely with the doubly infinite linear systems of the form (1) BpxpApxp1=fp,pZ,(1) where Z is the set of integer numbers. The N×N complex matrices Bp and Ap are periodic in p with period P1 so that Bp+Pk=Bp and Ap+Pk=Ap for all integers k. The matrices Ap and Bp are allowed to be singular and fp is a given complex N-vector. The doubly infinite vector sequences xp and fp are assumed to be elements of l2, i.e. p=xpxp< and p=fpfp<. By V we denote the conjugate transpose of a matrix or vector V.

Let us first consider the time-invariant case where Bp=I is the identity matrix and Ap=A is constant for all pZ, and the matrix A is discrete-time stable, that is, all its eigenvalues lie in the open unit disk {zC:|z|<1} of the complex plane C. Then, the sensitivity of the stability of A to small perturbations can be characterized by the value minϕRσmin(AeiϕI), where σmin denotes the minimum singular value of a matrix, i=1 and R is the set of real numbers. The sensitivity can be alternatively characterized by the parameter 1/H2, where H=n=0(An)An is a positive definite solution of the discrete-time Lyapunov matrix equation HAHA=I. Moreover, the magnitude H2 allows us to derive the decay estimate (see, e.g. [Citation6, chap. 9] or Remark 1.1) (2) An2H2(11/H2)n/2,n0.(2) The interest of this estimate is not so much its sharpness (although it is very good for sufficiently large n), but rather the fact that it is easily implementable as it is based on standard Lyapunov matrix equations and eigenvalues of Hermitian positive-definite matrices. The literature on efficient algorithms dealing with these issues is indeed rich, see, e.g. [Citation7, Citation8]. As we shall see later in this section and throughout the paper, our goal is to extend this estimate to system (Equation1). This will be achieved again by means of some Lyapunov matrix equations, but unlike the time-invariant case, here the literature is still in its infancy when it comes to implementation issues, see the discussion at the end of this section.

The concept of matrix stability is naturally extended to the concept of spectral dichotomy of matrices and regular matrix pencils, or operators as in [Citation9]. For example, a matrix A is said to possess the circular spectral dichotomy if it has no eigenvalues on the unit circle {zC:|z|=1}. A sensitivity theory of the circular dichotomy of matrices is given in [Citation10]. Its extension to regular matrix pencils is developed in [Citation11, Citation12]. The monograph [Citation6] presents a unified theory of the spectral dichotomy of matrices and regular matrix pencils.

The spectral dichotomy theory developed in [Citation6, Citation10–12] can be extended to discrete-time periodic systems. A standard way of extension is based on the so called lifting method [Citation13], which transforms the N×N periodic system (Equation1) to an augmented time-invariant system with matrix coefficients of size NP×NP, for example of the form (3) BXnAXn1=Fn,nZ,(3) where Xn=[xnPT,xnP+1T,,xnP+P1T]T, Fn=[fnPT,fnP+1T,,fnP+P1T]T and A=[A0],B=[B0A1B1A2B2AP1BP1].Throughout this section we assume that the matrix BeiϕA is nonsingular for all ϕR (when this assumption fails, the system (Equation3) may not be solvable, see Proposition 1.1). Consider the Fourier series (4) (BeiϕA)1=n=Gneinϕ(4) with the matrix coefficients Gn, which we refer to as the Green matrices for the system (Equation3). This denomination is used, for example, in [Citation10] for systems of type (Equation3), where B is the identity matrix. It follows that the unique l2-solution to (Equation3) is represented in the convolutional form (5) Xn=k=GnkFk,nZ.(5) The matrix of the inverse operator determined by (Equation5) is block Toeplitz and doubly infinite. The Toeplitz blocks are of size NP×NP.

Proposition 1.1

If BAeiϕ0 is singular for some ϕ0R, then system (Equation2) has no solution Xn in l2 for some Fnl2.

Proof.

We may assume that the matrices B and A are upper triangular and that BNNANNeiϕ0=0 by using the QZ factorization of the matrix pencil BμA with eigenvalue reordering [Citation7]. Then the entries yn=(Xn)N and gn=(Fn)N satisfy the doubly infinite system (6) BNNynANNyn1=gn.(6) When BNN=ANN=0, the right-hand side gn must be 0 identically and this contradicts to existence of a solution for all sequences gn. Now assume that BNN0 and ANN0. Then the solution of (Equation6) with g0=1 and gn=0 for all n0 has the components yn=x0einϕ0 for n0 and yn=y1ei(n+1)ϕ0 for n<0. The sequence yn belongs to l2 only if y0=y1=0, i.e. g0 must equal 0, which contradicts the assumption g0=1.

Proposition 1.1 proves that the operator L in l2 defined by (Equation2) is bijective under the condition that BAeiϕ is nonsingular for all ϕR. Since the inverse operator L1 is block convolutional as in (Equation5), Gn0 as |n|. The matrix equation I=n=Gneinϕ(BAeiϕ) yields the following identities for the matrix Fourier coefficients Gn: (7) GnBGn1A=δnI,nZ,(7) where δn is the Kronecker symbol such that δ0=1 and δn=0 for n0. Similarly, I=(BAeiϕ)n=Gneinϕ yields BGnAGn1=δnI,nZ.

The following theorem shows that the decay rate of Gn as |n| can still be expressed analogously to (Equation2), but the derivation is rather involved (see appendix for more detail).

Theorem 1.2

(8) Gn2H+2(11Π+BH+BΠ+2)n/2,n0,(8) (9) Gn2H2(11ΠAHAΠ2)(n1)/2,n>0,(9) where Π+ and Π are the orthogonal projectors onto the right deflating subspaces of the pencil λBA corresponding to the eigenvalues respectively inside and outside the unit circle, and H+=n=0GnGn, H=n=1GnGn are Hermitian positive semidefinite matrices satisfying the discrete-time Lyapunov matrix equations (10) BH+BAH+A=(G0B)(G0B),(10) (11) AHABHB=(G1A)(G1A).(11)

Remark 1.1

In the particular case when P = 1, Ap=A and Bp=I for all p and A is discrete-time stable, then Π+=I, Π=0, Gn=An if n0, Gn=0 if n<0, H+=n=0(An)An, H=0, and the estimate (Equation8) reduces to (Equation2).

Given a sequence Fn such that Fn=0 whenever |n|>nK for some positive integer nK, the corresponding solution Xn to (Equation3) decays as |n|. Owing to (Equation5) the decay rate of Xn is bounded by the decay rate of Gn as |n|.

A drawback of estimates (Equation8) and (Equation9) is that they involve the dense matrices H+ and H of large size NP×NP (which is the case, e.g. for small N and large P or for medium sizes N and P). This drawback is overcome by means of special periodic Lyapunov matrix equations of size N×N as in [Citation14]. However, this reference restricts the study to the case where Bp=I and nonsingular Ap and furthermore, the periodic Lyapunov matrix equations are constructed in a form which is not suitable for numerical computation. The present paper removes these restrictions and provides periodic Lyapunov matrix equations in the general case, where the matrices Bp and Ap can be singular. In addition, we give tighter decay estimates than those in [Citation14].

More specifically, our approach, where periodicity plays an essential role, is based on the periodic Schur decomposition of matrices with reordering and solutions of generalized periodic Sylvester systems, which allow us to block diagonalize periodic matrix sequences according to the spectrum parts of the monodromy matrix inside and outside the unit circle. The periodic Lyapunov matrix equations are then written for the diagonal blocks thus avoiding projected right-hand sides. The non-projected periodic Lyapunov equations that is without projected right-hand side have advantage that they admit unique solutions, which can be found by efficient algorithms of numerical linear algebra; see e.g. [Citation15]. These issues are developed in Sections 23 and 4. Finally, in Sections 5 and 6, the desired decay estimates are written in terms of solutions to the non-projected periodic Lyapunov matrix equations.

The periodic Schur decomposition is a powerful tool for solving periodic Lyapunov matrix equations and periodic Riccati matrix equations; see e.g. [Citation3, Citation4, Citation13, Citation16] and references therein. Periodic Lyapunov matrix equations in the context of balanced model reduction for periodic linear descriptor systems are treated in [Citation17]. Note however that, so far, convergence failure issues in the periodic Schur decomposition limit its practical utility, but progress is being made; see [Citation18].

The exponential decay of the entries of inverse of band matrices has been of some use in spline approximations as mentioned in [Citation19]. Decay patterns of matrix inverses have also attracted considerable interest in linear system preconditioning, low-rank approximation strategies such as hierarchical matrices, wavelets etc; see [Citation20, Citation21] and references therein. The exponential decay estimates of the present paper can be useful in the estimation of stability regions near periodic trajectories and in the Smith type methods [Citation22, Citation23] for periodic matrix equations; see e.g. the analysis in [Citation24] based on the decay bounds for the continuous periodic systems. The decay estimates also provide a quantitative convergence analysis of the so called doubling iterations for solving certain matrix equations [Citation25].

Part IV of [Citation26] contains other applications of exponential decay estimates and describes alternative decay estimates based on the Kreiss matrix theorem. However, the constants in the Kreiss matrix theorem are too expensive for computing [Citation27]. Moreover, these estimates depend on the matrix size.

2. Green's matrices and periodic Lyapunov equations

The doubly infinite system (Equation1) determines a bounded linear transform L:x=(xp)f=(fp) in the space l2 of square-summable doubly infinite vector sequences. We assume that L is invertible and represent its bounded inverse L1 in the matrix form (12) xl=pZGl,pfp,lZ,(12) where we call the coefficient matrices Gl,p by Green's matrices for (Equation1) in l2. In the control theory literature, the Green matrices are usually called the fundamental matrices.

Formula (Equation5) determines the block structure of Gnk: (13) Gnk=[GnP,kPGnP,kP+1GnP,kP+P1GnP+1,kPGnP+1,kP+1GnP+1,kP+P1GnP+P1,kPGnP+P1,kP+1GnP+P1,kP+P1].(13) It follows from (Equation13) that Gl,p is P-periodic: (14) Gl+Pm,p+Pm=Gl,pfor allmZ.(14) The structure (Equation13) also implies that the positive semidefinite Hermitian matrices (15) Hp+=l=pGl,pGl,p,Hp=l=p1Gl,pGl,p,pZ(15) are the diagonal N×N blocks of the matrices H+ and H used in (Equation10) and (Equation11). The matrices Hp+ and Hp are periodic in p with period P on account of (Equation14).

It follows from (Equation12) that Green's matrices satisfy the identity (16) BlGl,p=AlGl1,p+δl,pI(16) for all l,pZ. The Kronecker symbol δl,p equals 1 if l = p or 0 otherwise. The identity (Equation16) is simply the elementwise expression of equation LL1=I, where I stands for the identity operator in l2. Inserting the equality fp=BpxpApxp1 into (Equation12) gives the elementwise expression of equation L1L=I: (17) Gl,pBp=Gl,p+1Ap+1+δl,pI.(17) The matrices Hp+ and Hp are solutions to the following pair of periodic Lyapunov matrix equations (18) BpHp+BpAp+1Hp+1+Ap+1=(Gp,pBp)(Gp,pBp),(18) (19) Ap+1Hp+1Ap+1BpHpBp=(Gp,p+1Ap+1)(Gp,p+1Ap+1).(19) These equations are direct implications of the identity (Equation17). For instance, since Gl,pBp=Gl,p+1Ap+1 for lp, Ap+1Hp+1+Ap+1=l=p+1(Gl,p+1Ap+1)Gl,p+1Ap+1=l=p+1(Gl,pBp)(Gl,pBp)=BpHp+Bp(Gp,pBp)(Gp,pBp).Equation (Equation19) is derived analogously. We show in Section 4 that Gp,pBp and Gp,p+1Ap+1=IGp,pBp are projectors for all pZ; see Corollary 4.2.

Derivations in Section 4 show that the solution Hp+ of (Equation18) and solution Hp of (Equation19) are unique only under the additional conditions (20) (BpGp,p)Hp+BpGp,p=Hp+(20) (21) (ApGp1,p)HpApGp1,p=Hp(21) Note that computation of Hp+ and Hp from the systems of equations (Equation18), (Equation20) and (Equation19), (Equation21) is complicated by the fact that they are overdetermined. Below we get rid of the projected right-hand sides in (Equation18), (Equation19) and conditions (Equation20), (Equation21).

3. Periodic Schur decomposition and block diagonalization

By a slight abuse of notation we write the monodromy matrix formally as (22) M=BP1APBP11AP1B11A1(22) even if some matrices Bp are singular. Such usage can be justified for our purposes by small perturbation of the matrices Bp. A strict but more involved consideration is based on the lifting method. If some Bp are singular then the matrix (Equation22) may have infinite eigenvalues and indefinite eigenvalues. The eigenvalue structure of M is easily revealed by means of the periodic Schur decomposition, which is an extension of the QZ factorization of linear matrix pencils to periodic matrix pencils; see [Citation28, Citation29] for more details.

Theorem 3.1

Periodic Schur decomposition

There exist unitary matrices Qp, Zp, p=1,,P, and QP+1=Q1 such that all the transformed matrices B^p=ZpBpQp+1,A^p=ZpApQp,p=1,,P,are upper triangular.

The diagonal entries of B^p, A^p provide spectral information about system (Equation1). By the aid of Theorem 3.1 the monodromy matrix (Equation20) reads (23) M=Q1M^Q1,whereM^=B^P1A^PB^P11A^P1B^11A^1.(23) The eigenvalues λi of M are the diagonal entries of M^, namely λi=ai/bi,bi=p=1P(B^p)ii,ai=p=1P(A^p)ii.When (ai,bi)=(0,0), we say that λi is indefinite. From now on, we restrict ourselves to the regular case, when the monodromy matrix M has no indefinite eigenvalues. If bi0 (bi=0), then λi is a finite (infinite) eigenvalue of M.

We also assume below that the monodromy matrix M in (Equation22) has no eigenvalues on the unit circle {λC:|λ|=1}. Otherwise, it is easy to construct a solution xp of (Equation1) such that the solution xpl2 and the corresponding right-hand side fpl2.

It is possible to select an arbitrary order of eigenvalues in the periodic Schur decomposition of Theorem 3.1; see [Citation30]. Let the eigenvalues be ordered such that λ1, …, λN0 lie in the open unit disk, i.e. |λi|<1 for i=1,,N0, and λN0+1, …, λN0+N lie outside the closed unit disk, so that N0+N=N and |λi|>1 for i=N0+1,,N.

Let us block partition the transformed matrices subject to the above ordering (24) B^p=[Bp0Bp00Bp],A^p=[Ap0Ap00Ap].(24) The matrices B^p and A^p are then simultaneously block-diagonalized as (25) [ILp0I]B^p[IKp+10I]=[Bp000Bp]=B~p,(25) (26) [ILp0I]A^p[IKp0I]=[Ap000Ap]=A~p,(26) where the blocks Kp and Lp satisfy the generalized P-periodic Sylvester system (an analog of the generalized Sylvester matrix equations; see, e.g. [Citation31]) (27) Bp0Kp+1LpBp=Bp0,Ap0KpLpAp=Ap0,p=1,,P,(27) (28) KP+1=K1.(28) Since Bp0 and Ap are nonsingular due to the ordering, the Sylvester matrix equations can be rewritten in the form Kp+1=(Bp0)1LpBp(Bp0)1Bp0 and Lp=Ap0Kp(Ap)1+Ap0(Ap)1. After subsequent elimination of Lp we obtain the periodic Lyapunov matrix system with respect to the set of matrices Kp, p=1,,P, (29) Kp+1=(Bp0)1Ap0Kp(Ap)1BpFp,(29) where KP+1=K1, and Fp=(Bp0)1Bp0(Bp0)1Ap0(Ap)1Bp.

The system of P equations (Equation29) can be further reduced to a single nonsymmetric discrete-time Lyapunov matrix equation (30) KpMp0KpMp1=Cp,(30) where Mp0=(Bp+P10)1Ap+P10(Bp0)1Ap0,Mp1=(Ap)1Bp(Ap+P1)1Bp+P1,and Cp=j=1P1((i=P1j(Bp+i0)1Ap+i0)Fp+j1i=jP1(Ap+i)1Bp+i)Fp+P1.Equation (Equation30) has a unique solution because both matrices Mp0 and Mp1 are discrete-time stable; see, e.g. [Citation6]. This leads to the following

Theorem 3.2

Periodic block diagonalization with eigenvalue ordering

Assume that the monodromy matrix M has no indefinite eigenvalues and has no eigenvalues on the unit circle. Then, there exist nonsingular matrices Q~p, Z~p, p=1,,P, and Q~P+1=Q~1 such that B~p=Z~p1BpQ~p+1A~p=Z~p1ApQ~p,where all the transformed matrices B~p, A~p, p=1,,P, are upper triangular and block diagonal. The diagonal blocks Bp0, Ap0, Bp, Ap of the transformed matrices B~p=[Bp000Bp],A~p=[Ap000Ap]are such that the eigenvalues of the monodromy matrices Mp0=(Bp+P10)1Ap+P10(Bp0)1Ap0 and Mp=(ApP+1)1BpP+1(Ap)1Bp lie inside the open unit disk for all integer p.

Proof.

The matrices Q~p=Qp[IKp0I],Z~p=Zp[ILp0I]provide the desired block diagonalization; see (Equation25), (Equation26).

4. Block diagonal representations

Recall that the matrix sequences Bp, Ap, Hp+, Hp are P-periodic in p. The Green matrices Gl,p are also periodic as Gl,p=Gl+Pk,p+Pk for all kZ. If necessary, the matrices Qp, Zp, Kp, Lp are extended P-periodically for all pZ.

The transformed Green matrices (31) G~l,p=Q~l+11Gl,pZ~p(31) satisfy the analogue of (Equation7), namely (32) G~l,pB~p=G~l,p+1A~p+1+δl,pI(32) and also the similar equation B~lG~l,p=A~lG~l1,p+δl,pI.

Theorem 4.1

The transformed Green matrices G~l,p are block diagonal (33) G~l,p=[Gl,p000Gl,p](33) with the blocks (34) Gl,p0={0,l<p,(Bp0)1,l=p,((Bl0)1Al0)((Bp+10)1Ap+10)(Bp0)1,l>p.(34) (35) Gl,p={0,l>p1,(Ap)1,l=p1,((Al+1)1Bl+1)((Ap1)1Bp1)(Ap)1,l<p1.(35)

Proof.

It is easy to verify that matrices (Equation33) with the blocks (Equation34) and (Equation35) satisfy Equation (Equation32). Moreover, (36) liml+Gl,p0=0,limlGl,p=0.(36) Convergence in (Equation36) is linear. Therefore, the operator (Equation12) constructed from the matrices (Equation33) is bounded in l2 and coincides with L1 owing to uniqueness of the inverse operator in l2.

Corollary 4.2

The identities Gp,p0Bp0=I and Gp1,pAp=I are valid and Gp,pBp=Q~p+1[I0]Q~p+11,Gp1,pAp=Q~p[0I]Q~p1are projectors.

Let us denote the upper triangular factor of the Cholesky factorization of I+LpLp by (I+LpLp)1/2 and that of I+KpKp by (I+KpKp)1/2.

Proposition 4.3

(37) Gl,p2=Gl,p0(I+LpLp)1/22,lp,(37) (38) Gl,p2=(I+Kl+1Kl+1)1/2Gl,p2,lp1.(38)

Proof.

If lp, then Gl,pGl,p=Ql+1[Gl,p0(I+LpLp)(Gl,p0)000]Ql+1. If lp1, then Gl,pGl,p=Zp[000(Gl,p)(I+Kl+1Kl+1)Gl,p]Zp.

The transformed matrices (39) H~p+=Z~pHp+Z~p,H~p=Z~pHpZ~p.(39) are block diagonal (40) H~p+=[Hp0000],H~p=[000Hp](40) with the diagonal blocks (41) Hp0=lp(Gl,p0)Gl,p0andHp=lp1(Gl,p)(I+Kl+1Kl+1)Gl,p.(41) The P-periodic matrix sequences Hp0 and Hp satisfy the periodic discrete-time Lyapunov matrix systems (42) (Bp0)Hp0Bp0(Ap+10)Hp+10Ap+10=I,(42) (43) (Ap+1)Hp+1Ap+1(Bp)HpBp=(I+Kp+1Kp+1).(43) To estimate the norms in (Equation37) and (Equation38) we introduce the following matrices: (44) Bˇp0=(I+LpLp)1/2Bp0,Bˇp=Bp(I+Kp+1Kp+1)1/2,(44) (45) Aˇp0=(I+LpLp)1/2Ap0,Aˇp=Ap(I+KpKp)1/2,(45) (46) Gˇl,p0=Gl,p0(I+LpLp)1/2,Gˇl,p=(I+Kl+1Kl+1)1/2Gl,p,(46) (47) Hˇp0=lp(Gˇl,p0)Gˇl,p0,Hˇp=lp1(Gˇl,p)Gˇl,p=Hp.(47) Note that the monodromy matrices Mˇp0=(Bˇp+P10)1Aˇp+P10(Bˇp0)1Aˇp0=Mp0 and Mˇp=(AˇpP+1)1BˇpP+1(Aˇp)1Bˇp=(I+Kp+1Kp+1)1/2Mp(I+Kp+1Kp+1)1/2are discrete-time stable. We also obtain Gl,p2=Gˇl,p02 if lp and Gl,p2=Gˇl,p2 if lp1.

The matrices Hˇp0 and Hˇp satisfy the periodic discrete-time Lyapunov matrix systems (48) (Bˇp0)Hˇp0Bˇp0(Aˇp+10)Hˇp+10Aˇp+10=I(48) (49) (Aˇp+1)Hˇp+1Aˇp+1(Bˇp)HˇpBˇp=I(49) which are uniquely solvable. Moreover, all coefficient matrices Bˇp0, Aˇp+10, Bˇp and Aˇp+1 in systems (Equation48) and (Equation49) are upper triangular. Therefore, (Equation48) and (Equation49) are triangular linear systems when writing them with the Kronecker product of matrices.

5. Decay estimates I

Theorem 5.1

The following decay estimates hold (50) Gl,p02Hˇp02i=pl1(11/(Bˇi0)Hˇi0Bˇi02)12,lp,(50) (51) Gl,p2Hˇp2i=l+2p(11/(Aˇi)HˇiAˇi)12,lp1,(51) where the product over an empty set of indices is equal to 1.

Proof.

The bound (Equation50) is valid for l = p as a consequence of the inequality (Gˇp,p0)Gˇp,p0Hˇp0. Further we need the variables xl0=Gˇl,p0f0 for lp such that Bˇl0xl0=Aˇl0xl10 when l>p and Bˇp0xp0=f0. If l>p, then owing to (Equation48) and Bˇl0xl0=Aˇl0xl10 we obtain the estimates (xl0)(Bˇl0)Hˇl0Bˇl0xl0=(xl10)(Aˇl0)Hˇl0Aˇl0xl10=(xl10)(Bˇl10)Hˇl10Bˇl10xl10(xl10)xl10(11/(Bˇl10)Hˇl10Bˇl102)(xl10)(Bˇl10)Hˇl10Bˇl10xl10[i=pl1(11/(Bˇi0)Hˇi0Bˇi02)](xp0)(Bˇp0)Hˇp0Bˇp0xp0.Corollary 4.2 and (Equation44)–(Equation46) imply that Gˇl,l0Bˇl0=I. Since (xl0)(xl0)=(xl0)(Bˇl0)(Gˇl,l0)Gˇl,l0Bˇl0xl0(xl0)(Bˇl0)Hˇl0Bˇl0xl0we arrive at the inequality Gˇl,p0f022[i=pl1(11/(Bˇi0)Hˇi0Bˇi02)](f0)Hˇp0f0,which yields (Equation50). The estimate (Equation51) is derived similarly using (Equation47), (Equation49), the variables xl=Gˇl,pf for lp1 satisfying Bˇlxl=Aˇlxl1 when l<p−1 and Aˇpxp1=f, the identities Gp,p+1Ap+1=I and Gˇp,p+1Aˇp+1=(I+Kp+1Kp+1)1/2Gp,p+1Ap+1(I+Kp+1Kp+1)1/2=I.

Remark 5.1

Let Πp+ and Πp be the orthogonal projectors corresponding to the spectral projector pair Pp=Gp,pBp and IPp=Gp,p+1Ap+1. It can be shown that for all p=1,2,P Hˇp02=Hp+2,Hˇp2=Hp2,(Bˇp0)Hˇp0Bˇp02=Πp+BpHp+BpΠp+2,(Aˇp)HˇpAˇp2=ΠpApHpApΠp2.

6. Decay estimates II

In this section we propose decay estimates which are based on the P-periodic sequences of the discrete-time stable monodromy matrices Mp0 and Mp from Theorem 3.2. The discrete-time Lyapunov matrix equations Hp0(Mp0)Hp0Mp0=I and Hp(Mp)HpMp=I have unique solutions (52) Hp0=k0((Mp0))k(Mp0)k,Hp=k0((Mp))k(Mp)k,(52) and (Equation2) yields the following estimates for all integer k0: (53) (Mp0)k2Hp02(11/Hp02)k/2,(Mp)k2Hp2(11/Hp2)k/2.(53)

Theorem 6.1

For all integer k0 (54) Gl+Pk,p02Hl+102(11/Hl+102)k/2Gl,p02,l=p,p+1,,p+P1,(54) (55) GlPk,p2Hl2(11/Hl2)k/2Gl,p2,l=p1,,pP.(55)

Proof.

It is easy to see that Gl+Pk,p0=(Ml+10)kGl,p0 for lp and GlPk,p=(Ml)kGl,p for lp1. The estimates (Equation53) yield the desired estimates.

Alternatively, one can apply the decay estimates from [Citation32] formulated in the following

Theorem 6.2

If A is a discrete-time stable matrix, then (56) An{e(n+1)(1dA)n,n>(1dA)/dA,1/dA,otherwise,(56) where dA=minϕRσmin(eiϕIA).

Corollary 6.3

Determine the parameters dMp0=minϕRσmin(eiϕIMp0)anddMp=minϕRσmin(eiϕIMp).For all integer k(1dMp0)/dMp0, (57) Gl+Pk,p02e(k+1)(1dMp0)k,l=p,p+1,,p+P1,(57) For all integer k(1dMp)/dMp, (58) GlPk,p2e(k+1)(1dMp)k,l=p1,,pP.(58)

Example

This simple illustrative example shows that the decay estimates of Theorem 6.1 can be better than the decay estimates of Theorem 5.1.

Let P = 2 and α be a very large real number, say, α=250. The coefficients of a stable 2-periodic linear system are A1=(210), A2=(1α01), B1=B2=I. The monodromy matrices are M10=A2A1=(210) and M20=A1A2=21(1α00). Green's matrices Gl,p for p = 0 are G0,0=I, G2k1,0=G2k,0=(2k0), k=1,2,, and Gl,0=0 for l<0.

Solution to the system (Equation42) is H0=(8/35α/35α/35α2/3+2),H1=(5/3001). Since H025α2/3 and H12=5/3, the bound (Equation50) gives the weak estimate G2k,02H02(11/H02)k/2(11/H12)k/253α(135α2)k/2(135)k/2.On the other hand, from (Equation52) we obtain H10=(4/3001), so H102=4/3, and (Equation54) yields the tight estimate G2k,02H102(11/H10)k/2=232k.We also have dM10=1/2. Corollary 6.3 provides the weaker estimate G2k,02e(k+1)2k for k>1.

When the matrices Ap and Bp, p = 1, 2, are interchanged, we easily obtain an illustrative example with singular Bp. The Green matrices Gl,p for p = 0 are G1,0=I, G2k1,0=G2k,0=(2k0), k=1,2,, and Gl,0=0 for l0.

Solution to the system (Equation43) is H0=(5/3001), H1=(8/35α/35α/35α2/3+2). Since H02=5/3 and H125α2/3, the bound (Equation51) gives the estimate G2k1,02H02(11/H02)k/2(11/H12)k/253(135α2)k/2(135)k/2.On the other hand, from (Equation54) we obtain H1=(4/3001), so H12=4/3, and (Equation54) yields the estimate G2k1,02H12(11/H1)k/2=232k.

Acknowledgments

The authors thank the anonymous referees for their criticisms and suggestions that helped to improve the presentation of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the L. Meltzers Høyskolefond Research Fund.

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Appendix

Proof of Theorem 1.2

The proof requires some intermediate results which we give in the form of lemmas in order to facilitate the reading.

Lemma A.1

The Hermitian positive semidefinite matrices (A1) H+=n=0GnGn,H=n=1GnGn(A1) satisfy the discrete-time Lyapunov matrix equations (A2) BH+BAH+A=(G0B)(G0B),(A2) (A3) AHABHB=(G1A)(G1A).(A3)

Proof.

The proof is based on Equation (Equation7). For n1, Gn1A=GnB, and so AH+A=n=1(Gn1A)Gn1A=n=1(GnB)GnB=BH+B(G0B)G0B.Similarly, for n1, GnB=Gn1A, and so BHB=n=1(GnB)GnB=n=1(Gn1A)Gn1A=AHA(G1A)G1A.

Lemma A.2

  1. The matrices P+=G0B and P=IP+=G1A are the spectral projectors onto the right deflating subspaces of the pencil λBA corresponding to the eigenvalues respectively inside and outside the unit circle.

  2. The orthogonal projectors Π+ and Π corresponding to P+ and P satisfy (A4) P+=Π+P+,P=ΠP(A4)

  3. The matrices Gn satisfy the identities (A5) G0BGn=Gn=GnBG0ifn0,(A5) (A6) G1AGn=Gn=GnAG1ifn<0.(A6)

Proof.

  1. The spectral projectors onto the right deflating subspace of the pencil λBA corresponding to the eigenvalues inside the unit circle is given by (see, e.g. [Citation33, chap. 1] or [Citation6, chap. 10]) P+=12πiγ(zBA)1Bdz, where γ is any closed contour enclosing the eigenvalues in the open unit disk and excluding the other eigenvalues. Letting γ be the unit circle z=eiϕ, 0ϕ<2π leads to P+=12π02π(BeiϕA)1Bdϕ.

    On the other hand, it follows from (Equation4) that Gn=12π02π(BeiϕA)1einϕdϕ=12πiγ(zBA)1zndz. Therefore G0B=P+, and equation (Equation7) gives P=IG0B=G1A.

  2. The orthogonal projectors associated with P+ and P are given by Π+=P+(P+P++PP)1P+ and Π=P(P+P++PP)1P. From (P+P++PP)P+=P+P+ we deduce that P+=(P+P++PP)1P+P+ and hence P+=Π+P+. Similarly, we have P=Π_P_.

    In fact, P+=P~+P+ for any projector P~+ onto the same subspace as P+, i.e. when range(P~+)=range(P+). Indeed, for each vector x, P~+(P+x)=P+x. Analogously, P=P~P for any projector P~ onto the same subspace as P.

  3. Let γ1 and γ2 be closed contours enclosing the eigenvalues in the open unit disk and excluding the other eigenvalues and suppose that γ1 is inside γ2. Then G0BGn=1(2πi)2γ2γ1(z1BA)1B(z2BA)1z2ndz1dz2=1(2πi)2γ2γ1(z1BA)1(z2BA)1z2z1z2ndz1dz2=12πiγ1(12πiγ2z2nz2z1dz2)(z1BA)1dz112πiγ2(12πiγ1dz1z2z1)(z2BA)1z2ndz2=Gn,where we have used the Cauchy formulas 12πiγ2z2nz2z1dz2=z1nand12πiγ1dz1z2z1=0.The other equalities in (EquationA5) and (EquationA6) can be derived similarly.

Proof of Theorem 1.2.

Note first that the equalities Gn=GnBG0 for n0 and Gn=GnAG1 for n<0 imply the equations (A7) H+=G0BH+BG0,(A7) (A8) H=G1AHAG1,(A8) which should be added to the Lyapunov Equations (EquationA2) and (EquationA3) in order to provide uniqueness of their solutions H+ and H.

Now, owing to (EquationA2), the equalities Gn(BH+B)Gn=Gn1AH+AGn1=Gn1[BH+B(G0B)G0B]Gn1hold if n1. In view of (EquationA4), G0B=Π+G0B. As a consequence of (EquationA7) Gn1BH+BGn1=Gn1BG0BH+BG0BGn1=Gn1BG0Π+BH+BΠ+G0BGn1Π+BH+BΠ+2(G0BGn1)G0BGn1and Gn(BH+B)Gn(11/Π+BH+BΠ+2)Gn1(BH+B)Gn1.Hence Gn(BH+B)Gn(11/Π+BH+BΠ+2)nG0(BH+B)G0,and using again (EquationA7) leads to the estimate Gn(BH+B)Gn(11/Π+BH+BΠ+2)nH+.Since G0BGn=Gn, we can derive the estimate (A9) Gn(BH+B)GnGnBG0G0BGn=GnGn.(A9) The estimate (Equation8) of Theorem 1.2 then follows from the inequality GnGn(11/Π+BH+BΠ+2)nH+. The estimate (Equation9) is derived analogously.

Comments

  1. The Lyapunov equations (EquationA2) and (EquationA3) are often called generalized Lyapunov equations [Citation6, chap. 10]) because the matrices P+=G0B and P=G1A are projectors. The generalized Lyapunov equations were first introduced by Godunov for continuous-time linear systems in [Citation34]. Since the equations from [Citation34] have nonunique solutions, Bulgakov supplemented them by additional equations in [Citation35] in order to get a matrix system, which has a unique solution. Generalized Lyapunov equations with unique solution in the general discrete-time case, including singular B, were derived by Malyshev in [Citation11, Citation12]. Generalized Lyapunov equations for descriptor continuous-time case were introduced by Stykel in [Citation36].

  2. To our best knowledge, the earliest publication which contains decay estimates of the form (Equation2) for the continuous-time Lyapunov matrix equations, is [Citation9, proof of Theorem 5.1]. Decay estimates for discrete-time linear systems BXnAXn1=Fn with an arbitrary matrix B first appeared in [Citation11, Citation12]. Decay estimates for descriptor continuous-time linear systems are derived in [Citation36].

  3. In previous publications, the denominators in (Equation8) and (Equation9) are BH+B2 instead of Π+BH+BΠ+2 and AHA2 instead of ΠAHAΠ2. The variant in (Equation8) and (Equation9) gives tighter estimates, which, as far as we know, are not available in the literature.