Skip to main content

Factoring Block Fiedler Companion Matrices

  • Chapter
  • First Online:

Part of the book series: Springer INdAM Series ((SINDAMS,volume 30))

Abstract

When Fiedler published his “A note on Companion matrices” in 2003 on Linear Algebra and its Applications, he could not have foreseen the significance of this elegant factorization of a companion matrix into essentially two-by-two Gaussian transformations, which we will name (scalar) elementary Fiedler factors. Since then, researchers extended these results and studied the various resulting linearizations, the stability of Fiedler companion matrices, factorizations of block companion matrices, Fiedler pencils, and even looked at extensions to non-monomial bases. In this chapter, we introduce a new way to factor block Fiedler companion matrices into the product of scalar elementary Fiedler factors. We use this theory to prove that, e.g. a block (Fiedler) companion matrix can always be written as the product of several scalar (Fiedler) companion matrices. We demonstrate that this factorization in terms of elementary Fiedler factors can be used to construct new linearizations. Some linearizations have notable properties, such as low bandwidth, or allow for factoring the coefficient matrices into unitary-plus-low-rank matrices. Moreover, we will provide bounds on the low-rank parts of the resulting unitary-plus-low-rank decomposition. To present these results in an easy-to-understand manner, we rely on the flow-graph representation for Fiedler matrices recently proposed by Del Corso and Poloni in Linear Algebra and its Applications, 2017.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   59.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    We typically abbreviate column companion matrix and omit the word column, unless we want to emphasize it.

  2. 2.

    Often, the term companion matrix indicates a matrix obtained from the coefficients of the polynomial without performing arithmetic operations. Here, we have not added this constraint into the definition but—as we will discuss later—all the matrices obtained in our framework satisfy it.

  3. 3.

    This is a variation on the usual construction of a row companion matrix having the elements a i in its first row.

  4. 4.

    Here, by condition number we mean the non-homogeneous absolute or relative condition number defined in [34] (see also [1] where several definitions are compared). It is easy to verify that substituting the change of basis in the formula for the non-homogeneous condition number in [34] does not change the result.

  5. 5.

    Similarly, one could factor it into dk row companion matrices linked to polynomials of degree 3.

References

  1. Anguas, L.M., Bueno, M.I., Dopico, F.M.: A comparison of eigenvalue condition numbers for matrix polynomials, arXiv preprint arXiv:1804.09825 (2018)

    Google Scholar 

  2. Antoniou, E.N., Vologiannidis, S.: A new family of companion forms of polynomial matrices. Electron. J. Linear Algebra 11, 78–87 (2004)

    Article  MathSciNet  Google Scholar 

  3. Aurentz, J.L., Vandebril, R., Watkins, D.S.: Fast computation of the zeros of a polynomial via factorization of the companion matrix. SIAM J. Sci. Comput. 35, A255–A269 (2013)

    Article  MathSciNet  Google Scholar 

  4. Aurentz, J.L., Mach, T., Vandebril, R., Watkins, D.S.: Fast and backward stable computation of roots of polynomials. SIAM J. Matrix Anal. Appl. 36, 942–973 (2015)

    Article  MathSciNet  Google Scholar 

  5. Aurentz, J.L., Mach, T., Vandebril, R., Watkins, D.S.: Fast and stable unitary QR algorithm. Electron. Trans. Numer. Anal. 44, 327–341 (2015)

    MathSciNet  MATH  Google Scholar 

  6. Aurentz, J.L., Mach, T., Robol, L., Vandebril, R., Watkins, D.S.: Core-Chasing Algorithms for the Eigenvalue Problem, vol. 13 of Fundamentals of Algorithms. Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA (2018)

    Google Scholar 

  7. Aurentz, J.L., Mach, T., Robol, L., Vandebril, R., Watkins, D.S.: Fast and backward stable computation of the eigenvalues of matrix polynomials. Math. Comput. 88, 313–247 (2019)

    Article  MathSciNet  Google Scholar 

  8. Bevilacqua, R., Del Corso, G.M., Gemignani, L.: A CMV-based eigensolver for companion matrices. SIAM J. Matrix Anal. Appl. 36, 1046–1068 (2015)

    Article  MathSciNet  Google Scholar 

  9. Bevilacqua, R., Del Corso, G.M., Gemignani, L.: Compression of unitary rank-structured matrices to CMV-like shape with an application to polynomial rootfinding. J. Comput. Appl. Math. 278, 326–335 (2015)

    Article  MathSciNet  Google Scholar 

  10. Bevilacqua, R., Del Corso, G.M., Gemignani, L.: Fast QR iterations for unitary plus low-rank matrices, arXiv preprint arXiv:1810.0270 (2018)

    Google Scholar 

  11. Bini, D.A., Robol, L.: On a class of matrix pencils and -ifications equivalent to a given matrix polynomial. Linear Algebra Appl. 502, 275–298 (2016)

    Article  MathSciNet  Google Scholar 

  12. Bini, D.A., Eidelman, Y., Gemignani, L., Gohberg, I.: Fast QR eigenvalue algorithms for Hessenberg matrices which are rank-one perturbations of unitary matrices. SIAM J. Matrix Anal. Appl. 29, 566–585 (2007)

    Article  MathSciNet  Google Scholar 

  13. Bini, D.A., Boito, P., Eidelman, Y., Gemignani, L., Gohberg, I.: A fast implicit QR eigenvalue algorithm for companion matrices. Linear Algebra Appl. 432, 2006–2031 (2010)

    Article  MathSciNet  Google Scholar 

  14. Boito, P., Eidelman, Y., Gemignani, L.: Implicit QR for rank-structured matrix pencils. BIT Numer. Math. 54, 85–111 (2014)

    Article  MathSciNet  Google Scholar 

  15. Boito, P., Eidelman, Y., Gemignani, L.: A real QZ algorithm for structured companion pencils, arXiv preprint arXiv:1608.05395 (2016)

    Google Scholar 

  16. Bueno, M.I., De Terán, F.: Eigenvectors and minimal bases for some families of Fiedler-like linearizations. Linear Multilinear Algebra 62, 39–62 (2014)

    Article  MathSciNet  Google Scholar 

  17. Bueno, M.I., Furtado, S.: Palindromic linearizations of a matrix polynomial of odd degree obtained from Fiedler pencils with repetition. Electron. J. Linear Algebra 23, 562–577 (2012)

    Article  MathSciNet  Google Scholar 

  18. Bueno, M.I., de Terán, F., Dopico, F.M.: Recovery of eigenvectors and minimal bases of matrix polynomials from generalized Fiedler linearizations. SIAM J. Matrix Anal. Appl. 32, 463–483 (2011)

    Article  MathSciNet  Google Scholar 

  19. Bueno, M.I., Curlett, K., Furtado, S.: Structured strong linearizations from Fiedler pencils with repetition I. Linear Algebra Appl. 460, 51–80 (2014)

    Article  MathSciNet  Google Scholar 

  20. Bueno, M.I., Dopico, F.M., Furtado, S., Rychnovsky, M.: Large vector spaces of block-symmetric strong linearizations of matrix polynomials. Linear Algebra Appl. 477, 165–210 (2015)

    Article  MathSciNet  Google Scholar 

  21. Bueno, M.I., Dopico, F.M., Furtado, S., Medina, L.: A block-symmetric linearization of odd-degree matrix polynomials with optimal eigenvalue condition number and backward error. Calcolo 55(3), 32 (2018)

    Article  MathSciNet  Google Scholar 

  22. De Terán, F., Dopico, F.M., Mackey, D.S.: Fiedler companion linearizations and the recovery of minimal indices. SIAM J. Matrix Anal. Appl. 31, 2181–2204 (2010)

    Article  MathSciNet  Google Scholar 

  23. De Terán, F., Dopico, F.M., Mackey, D.S.: Palindromic companion forms for matrix polynomials of odd degree. J. Comput. Math. 236, 1464–1480 (2011)

    Article  MathSciNet  Google Scholar 

  24. De Terán, F., Dopico, F.M., Pérez, J.: Condition numbers for inversion of Fiedler companion matrices. Linear Algebra Appl. 439, 944–981 (2013)

    Article  MathSciNet  Google Scholar 

  25. De Terán, F., Dopico, F.M., Mackey, D.S.: Spectral equivalence of matrix polynomials and the Index Sum Theorem. Linear Algebra Appl. 459, 264–333 (2014)

    Article  MathSciNet  Google Scholar 

  26. Dopico, F.M., Lawrence, P.W., Pérez, J., Van Dooren, P.: Block Kronecker linearizations of matrix polynomials and their backward errors, arXiv preprint arXiv:1707.04843 (2017)

    Google Scholar 

  27. Fiedler, M.: A note on companion matrices. Linear Algebra Appl. 372, 325–331 (2003)

    Article  MathSciNet  Google Scholar 

  28. Hammarling, S., Munro, C.J., Tisseur, F.: An algorithm for the complete solution of quad ratic eigenvalue problems. ACM Trans. Math. Softw. 39, 18 (2013)

    Article  Google Scholar 

  29. Higham, N.J., Mackey, D.S., Mackey, N., Tisseur, F.: Symmetric linearizations for matrix polynomials. SIAM J. Matrix Anal. Appl. 29, 143–159 (2006)

    Article  MathSciNet  Google Scholar 

  30. Mackey, D.S., Mackey, N., Mehl, C., Mehrmann, V.: Vector spaces of linearizations for matrix polynomials. SIAM J. Matrix Anal. Appl. 28, 971–1004 (2006)

    Article  MathSciNet  Google Scholar 

  31. Poloni, F., Del Corso, G.M.: Counting Fiedler pencils with repetitions. Linear Algebra Appl. 532, 463–499 (2017)

    Article  MathSciNet  Google Scholar 

  32. Raz, R.: On the complexity of matrix product. In: Proceedings of the Thirty-fourth Annual ACM Symposium on Theory of Computing, STOC ’02, New York, NY, USA, ACM, pp. 144–151 (2002)

    Google Scholar 

  33. Robol, L., Vandebril, R., Dooren, P.V.: A framework for structured linearizations of matrix polynomials in various bases. SIAM J. Matrix Anal. Appl. 38, 188–216 (2017)

    Article  MathSciNet  Google Scholar 

  34. Tisseur, F.: Backward error and condition of polynomial eigenvalue problems. Linear Algebra Appl. 309, 339–361 (2000)

    Article  MathSciNet  Google Scholar 

  35. Van Barel, M., Tisseur., F.: Polynomial eigenvalue solver based on tropically scaled Lagrange linearization. Linear Algebra Appl. 542, 186–208 (2018)

    Google Scholar 

  36. Vologiannidis, S., Antoniou, E.N.: A permuted factors approach for the linearization of polynomial matrices. Math. Control Signals Syst. 22, 317–342 (2011)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The research of the first three authors was partially supported by GNCS projects “Metodi numerici avanzati per equazioni e funzioni di matrici con struttura” and “Tecniche innovative per problemi di algebra lineare”; the research of the first two was also supported by University of Pisa under the grant PRA-2017-05. The research of the fourth author was supported by the Research Council KU Leuven, project C14/16/056 (Inverse-free Rational Krylov Methods: Theory and Applications).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gianna M. Del Corso .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Corso, G.M.D., Poloni, F., Robol, L., Vandebril, R. (2019). Factoring Block Fiedler Companion Matrices. In: Bini, D., Di Benedetto, F., Tyrtyshnikov, E., Van Barel, M. (eds) Structured Matrices in Numerical Linear Algebra. Springer INdAM Series, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-04088-8_7

Download citation

Publish with us

Policies and ethics