Abstract
The computation of the distance of a quadratic matrix polynomial to the quadratic matrix polynomials that are singular on the unit circle is investigated. The emphasis is placed on backward stable methods that transform the computation of the distance to a palindromic eigenvalue problem for which structure-preserving eigensolvers can be utilized in conjunction with a bisection algorithm. Reliability of the suggested methods is guaranteed by a novel error analysis.
Similar content being viewed by others
References
Benner, P., Mehrmann, V., Xu, H.: A numerically stable, structure preserving method for computing the eigenvalues of real Hamiltonian or symplectic pencils. Numer. Math. 78(3), 329–358 (1998)
Bini, D.A., Meini, B.: Effective methods for solving banded Toeplitz systems. SIAM J. Matrix Anal. Appl. 20, 700–719 (1999)
Boyd, S., Balakrishnan, V.: A regularity result for the singular values of a transfer matrix and a quadratically convergent algorithm for computing its L ∞-norm. Syst. Control Lett. 15, 1–7 (1990)
Boyd, S., Balakrishnan, V., Kabamba, P.: On computing the H ∞-norm of a transfer function matrix. In: Proc. 1988 American Control Conference, Atlanta, GA, pp. 396–397 (1988)
Boyd, S., Balakrishnan, V., Kabamba, P.: A bisection method for computing the H ∞-norm of a transfer function matrix and related problems. Math. Control Signals Syst. 2, 207–219 (1989)
Bruinsma, N.A., Steinbruch, M.: A fast algorithm to compute the H ∞-norm of a transfer function matrix. Syst. Control Lett. 14, 287–293 (1990)
Byers, R.: A bisection method for measuring the distance of a stable matrix to the unstable matrices. SIAM J. Sci. Stat. Comput. 9, 875–881 (1988)
Genin, Y., Stefan, R., Van Dooren, P.: Real and complex stability radii of polynomial matrices. Linear Algebra Appl. 351–352, 381–410 (2002)
Gohberg, I., Lancaster, P., Rodman, L.: Matrix Polynomials. Academic Press, New York (1982)
Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. The Johns Hopkins University Press, Baltimore (1996)
Higham, N.J., Tisseur, F., Van Dooren, P.M.: Detecting a definite Hermitian pair and a hyperbolic or elliptic quadratic eigenvalue problem, and associated nearness problems. Linear Algebra Appl. 351–352, 455–474 (2002)
Hinrichsen, D., Pritchard, A.J.: Stability radii of linear systems. Syst. Control Lett. 7, 1–10 (1986)
Hinrichsen, D., Pritchard, A.J.: Stability radius for structured perturbations and algebraic Riccati equation. Syst. Control Lett. 8, 105–113 (1986)
Hinrichsen, D., Pritchard, A.J.: Mathematical Systems Theory I. Modelling, State Space Analysis, Stability and Robustness. Springer, Berlin (2005)
Hinrichsen, D., Son, N.K.: Stability radii of linear discrete-time systems and symplectic pencils. Int. J. Robust Nonlinear Control 1, 79–97 (1991)
Kressner, D., Mengi, E.: Structure-preserving eigenvalue solvers for robust stability and controllability estimates. In: Proc. 45th IEEE Conf. Decision Control, San Diego, pp. 5174–5179 (2006)
Kressner, D., Schröder, C., Watkins, D.S.: Implicit algorithms for palindromic and even eigenvalue problems. Numer. Algorithms 51, 209–238 (2009)
Mackey, D.S., Mackey, N., Mehl, C., Mehrmann, V.: Structured polynomial eigenvalue problems: good vibrations from good linearizations. SIAM J. Matrix Anal. Appl. 28, 1029–1051 (2006)
Mackey, D.S., Mackey, N., Mehl, C., Mehrmann, V.: Numerical methods for palindromic eigenvalue problems: computing the anti-triangular Schur form. Numer. Linear Algebra Appl. 16, 63–86 (2009)
Moler, C.B., Stewart, G.W.: An algorithm for generalized matrix eigenvalue problems. SIAM J. Numer. Anal. 10, 241–256 (1973)
Pappas, G., Hinrichsen, D.: Robust stability of linear systems described by higher order dynamic equations. IEEE Trans. Autom. Control 38, 1430–1435 (1993)
Robel, G.: On computing the infinity norm. IEEE Trans. Autom. Control 34, 882–884 (1989)
Schröder, C.: URV decomposition based structured methods for palindromic and even eigenvalue problems. Matheon Preprint 375, TU Berlin (2007)
Schröder, C.: A QR-like algorithm for the palindromic eigenvalue problem. Matheon Preprint 388, TU Berlin (2007)
Tisseur, F., Higham, N.J.: Structured pseudospectra for polynomial eigenvalue problems, with applications. SIAM J. Matrix Anal. Appl. 23, 187–208 (2001)
Tisseur, F., Meerbergen, K.: A survey of the quadratic eigenvalue problems. SIAM Rev. 42, 234–286 (2001)
Van Loan, C.F.: How near is a stable matrix to an unstable matrix. Contemp. Math. 47, 465–477 (1985)
Acknowledgements
The authors thank the referees for making useful remarks and suggestions on the manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Ahmed Salam.
Appendix: Another proof of Byers’ results in [7, Thm 3]
Appendix: Another proof of Byers’ results in [7, Thm 3]
Assume that the eigenvalues of the Hamiltonian matrix , parameterized by σ≥0, are computed by a structure-preserving backward stable numerical algorithm, i.e., the computed eigenvalues are the exact eigenvalues of a perturbed matrix H(σ)+Δ, where the perturbation Δ is Hamiltonian, that is, ΔJ is Hermitian for . Let us denote \(r=\min_{\omega\in\mathbb{R}}\sigma_{\min}(A-i\omega I)\) and δ=∥Δ∥2. Theorem 3 in [7] is essentially equivalent to the following two assertions:
-
if one of the computed eigenvalues lies on the imaginary axis, then r≤σ+δ;
-
if all the computed eigenvalues lie outside the imaginary axis, then r>σ−δ.
Below we provide a new proof, which uses the same technique as the arguments from Sect. 4.1.
First we observe that the eigenvalues of the Hermitian matrix
are ±s k (ω)−σ, k=1,…,n, where s k (ω) are the singular values of A−iωI, \(\omega\in\mathbb{R}\). It is easy to see that the set \(\{\min_{k}s_{k}(\omega)\colon\omega\in\mathbb{R}\}\) equals the interval [r,∞), and the set \(\{s_{k}(\omega)\colon\omega\in\mathbb{R}, k=1,\ldots,n\}\) coincides with [r,∞) too. Hence the set of all eigenvalues of S(ω) for all real ω equals (−∞,−r−σ]∪[r−σ,∞).
Owing to the Courant-Fischer minimax theorem [10] the set of all eigenvalues of the perturbed matrix S(ω)+ΔJ for all ω contains the interval [r+δ−σ,∞) and does not contain the interval (−r+δ−σ,r−δ−σ). When σ∈[r+δ,∞), the former interval contains 0, i.e., the perturbed Hamiltonian matrix H(σ)+Δ has an eigenvalue on the imaginary axis. When δ<r and σ∈[0,r−δ), the latter interval contains 0, and the perturbed Hamiltonian matrix H(σ)+Δ has no eigenvalues on the imaginary axis.
Rights and permissions
About this article
Cite this article
Malyshev, A., Sadkane, M. A bisection method for measuring the distance of a quadratic matrix polynomial to the quadratic matrix polynomials that are singular on the unit circle. Bit Numer Math 54, 189–200 (2014). https://doi.org/10.1007/s10543-013-0445-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10543-013-0445-1
Keywords
- Distance to instability
- Quadratic matrix polynomial
- Bisection algorithm
- Level set algorithm
- Error analysis