Skip to main content
Log in

Accurate solutions of M-matrix Sylvester equations

  • Published:
Numerische Mathematik Aims and scope Submit manuscript

Abstract

This paper is concerned with a relative perturbation theory and its entrywise relatively accurate numerical solutions of an M-matrix Sylvester equation AX + XB = C by which we mean both A and B have positive diagonal entries and nonpositive off-diagonal entries and \({P=I_m \otimes A+B^{\rm T} \otimes I_n}\) is a nonsingular M-matrix, and C is entrywise nonnegative. It is proved that small relative perturbations to the entries of A, B, and C introduce small relative errors to the entries of the solution X. Thus the smaller entries of X do not suffer bigger relative errors than its larger entries, unlikely the existing perturbation theory for (general) Sylvester equations. We then discuss some minor but crucial implementation changes to three existing numerical methods so that they can be used to compute X as accurately as the input data deserve.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Alfa A.S., Xue J., Ye Q.: Accurate computation of the smallest eigenvalue of a diagonally dominant M-matrix. Math. Comput. 71, 217–236 (2002)

    MathSciNet  MATH  Google Scholar 

  2. Alfa A.S., Xue J., Ye Q.: Entrywise perturbation theory for diagonally dominant M-matrices with applications. Numer. Math. 90(3), 401–414 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. American National Standards Institute and Institute of Electrical and Electronic Engineers: IEEE standard for binary floating-point arithmetic. ANSI/IEEE Standard, Std 754-1985, New York (1985)

  4. American National Standards Institute and Institute of Electrical and Electronic Engineers: IEEE standard for radix independent floating-point arithmetic. ANSI/IEEE Standard, Std 854-1987, New York (1987)

  5. Bartels R.H., Stewart G.W.: Algorithm 432: The solution of the matrix equation AXBX = C. Commun. ACM 8, 820–826 (1972)

    Article  Google Scholar 

  6. Benner P., Li R.-C., Truhar N.: On ADI method for Sylvester equations. J. Comput. Appl. Math. 233(4), 1035–1045 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  7. Berman, A., Plemmons, R.J.: Nonnegative Matrices in the Mathematical Sciences. SIAM, Philadelphia, 1994. This SIAM edition is a corrected reproduction of the work first published in 1979 by Academic Press, San Diego, CA

  8. Elsner L., Koltracht I., Neumann M., Xiao D.: On accurate computations of the Perron root. SIAM J. Matrix Anal. Appl. 14(2), 456–467 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  9. Gohberg I., Koltracht I.: Mixed, componentwise, and structured condition numbers. SIAM J. Matrix Anal. Appl. 14(3), 688–704 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  10. Goldberg D.: What every computer scientist should know about floating-point arithmetic. ACM Comput. Surv. 23(1), 5–47 (1991)

    Article  Google Scholar 

  11. Golub G.H., Nash S., Van Loan C.F.: Hessenberg–Schur method for the problem AX + XB = C. IEEE Trans. Autom. Control AC-24, 909–913 (1979)

    Article  Google Scholar 

  12. Grassmann W.K., Taksar M.J., Heyman D.P.: Regenerative analysis and steady-state distributions for Markov chains. Oper. Res. 33, 1107–1116 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  13. Guo C., Higham N.: Iterative solution of a nonsymmetric algebraic Riccati equation. SIAM J. Matrix Anal. Appl. 29, 396–412 (2007)

    Article  MathSciNet  Google Scholar 

  14. Guo C.-H.: Nonsymmetric algebraic Riccati equations and Wiener–Hopf factorization for M-matrices. SIAM J. Matrix Anal. Appl. 23, 225–242 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  15. Guo C.-H., Laub A.J.: On the iterative solution of a class of nonsymmetric algebraic Riccati equations. SIAM J. Matrix Anal. Appl. 22, 376–391 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  16. Guo X., Lin W., Xu S.: A structure-preserving doubling algorithm for nonsymmetric algebraic Riccati equation. Numer. Math. 103, 393–412 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  17. Higham N.J.: Accuracy and Stability of Numerical Algorithms. 2nd edn. SIAM, Philadephia (2002)

    Book  MATH  Google Scholar 

  18. Horn R.A., Johnson C.R.: Topics in Matrix Analysis. Cambridge University Press, Cambridge (1991)

    Book  MATH  Google Scholar 

  19. Juang J.: Existence of algebraic matrix Riccati equations arising in transport theory. Linear Algebra Appl. 230, 89–100 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  20. Juang J., Lin W.-W.: Nonsymmetric algebraic Riccati equations and Hamiltonian-like matrices. SIAM J. Matrix Anal. Appl. 20(1), 228–243 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  21. Li, R.-C.: Solving secular equations stably and efficiently. Technical Report UCB//CSD-94-851, Computer Science Division, Department of EECS, University of California at Berkeley (1993)

  22. Ramaswami, V.: Matrix analytic methods for stochastic fluid flows. In: Key, P., Smith, D. (eds.) Teletraffic Engineering in a Competitive World, vol. 3a of Teletraffic Science and Engineering. Elsevier Science, Amsterdam, pp. 1019–1030 (1999)

  23. Rogers L.: Fluid models in queueing theory and Wiener–Hopf factorization of Markov chains. Ann. Appl. Probab. 4, 390–413 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  24. Smith R.A.: Matrix equation XA + BX = C. SIAM J. Appl. Math. 16(1), 198–201 (1968)

    Article  MathSciNet  MATH  Google Scholar 

  25. Stoer J., Bulirsch R.: Introduction to Numerical Analysis. 2nd edn. Springer-Verlag, Berlin (1992)

    Google Scholar 

  26. Varga R.S.: Matrix Iterative Analysis. Englewood Cliffs, NJ (1962)

    Google Scholar 

  27. Virnik, E.: Analysis of positive descriptor systems. PhD thesis, Technischen Universität Berlin, Berlin (2008)

  28. Wachspress, E.L.: The ADI Model Problem. Windsor, CA (1995) (self-published) (http://www.netlib.org/na-digest-html/96/v96n36.html)

  29. Xue J.: Computing the smallest eigenvalue of an M-matrix. SIAM J. Matrix Anal. Appl. 17(4), 748–762 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  30. Xue J., Jiang E.: Entrywise relative perturbation theory for nonsingular M-matrices and applications. BIT 35(3), 417–427 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  31. Xue, J., Xu, S., Li, R.-C.: Accurate solutions of M-matrix algebraic Riccati equations. Numer. Math. doi:10.1007/s00211-011-0421-0

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ren-Cang Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Xue, J., Xu, S. & Li, RC. Accurate solutions of M-matrix Sylvester equations. Numer. Math. 120, 639–670 (2012). https://doi.org/10.1007/s00211-011-0420-1

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00211-011-0420-1

Mathematics Subject Classification (2000)

Navigation