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On rank and null space computation of the generalized Sylvester matrix

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Abstract

In this paper, we present new approaches computing the rank and the null space of the (m n + p)×(n + p) generalized Sylvester matrix of (m + 1) polynomials of maximal degrees n,p. We introduce an algorithm which handles directly a modification of the generalized Sylvester matrix, computing efficiently its rank and null space and replacing n by log 2 n in the required complexity of the classical methods. We propose also a modification of the Gauss-Jordan factorization method applied to the appropriately modified Sylvester matrix of two polynomials for computing simultaneously its rank and null space. The methods can work numerically and symbolically as well and are compared in respect of their error analysis, complexity and efficiency. Applications where the computation of the null space of the generalized Sylvester matrix is required, are also given.

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References

  1. Kurt, A.M., Uriel, R.G.: Using Gauss-Jordan elimination to compute the index, generalized nullspaces, and drazin inverse. Linear Algebra Appl. 85, 221–239 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  2. Barnett, S.: Greatest common divisor of several polynomials. Linear Multilinear Algebra 8, 271–279 (1980)

    Article  MATH  MathSciNet  Google Scholar 

  3. Li, B., Liu, Z., Zhi, L.: A structured rank-revealing method for Sylvester matrix. J. Comput. Appl. Math. 213, 212–223 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  4. Bini, D., Boito, P.: Structured matrix-based methods for polynomial ε-gcd: analysis and comparisons, ISSAC’07. In: Proc. Internat. Symp. Symbolic Algebraic Comput., pp. 9–16 (2007)

  5. Bitmead, R.R., Kung, S.Y., Anderson, B.D.O., Kailath, T.: Greatest common divisors via generalized Sylvester and Bezout matrices. IEEE Trans. Automat. Contr. AC 23(6), 1043–1047 (1978)

    Article  MATH  MathSciNet  Google Scholar 

  6. Chan, T.: Rank revealing QR factorizations. Linear Algebra Appl. 88/89, 67–82 (1987)

    Article  Google Scholar 

  7. Corless, R.M., Watt, S.M., Zhi, L.: QR factoring to compute the GCD of univariate approximate polynomials. IEEE Trans. Signal Process. 52(12), 3394–3402 (2004)

    Article  MathSciNet  Google Scholar 

  8. Datta, B.N.: Numerical Linear Algebra and Applications, 2nd edn. Brooks/Cole, USA (1995)

    MATH  Google Scholar 

  9. Foster, L.: Rank and null space calculations using matrix decomposition without column interchanges. Linear Algebra Appl. 74, 47–71 (1984)

    Article  MathSciNet  Google Scholar 

  10. Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn, pp 248–254. The John Hopkins Univercity Press, Baltimore (1989)

    MATH  Google Scholar 

  11. Kailath, T., Chun, J.: Generalized displacement structure for block-Toeplitz, Toeplitz-block and Toeplitz-derived matrices. SIAM J. Matrix Anal. Appl. 15(1), 114–128 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  12. Karcanias, N., Mitrouli, M., Koukouvinos, C.: Numerical performance of the matrix pencil algorithm computing the greatest common divisor of polynomials and comparison with other matrix -based methodologies. J. Comput. Appl. Math. 76, 89–112 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  13. Karcanias, N., Mitrouli, M.: Normal factorization of polynomials and computational issues. Comput. Math. Appl. 45, 229–245 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  14. Zhi, L.: Displacement structure in computing approximate gcd of univariate polynomials mathematics, World scientific. In: Lecture Notes Series on Computing, pp. 288–298 (2003)

  15. Ana, M., Jose-Javier, M.: A new source of structured singular value decomposition problems. ETNA 18, 188–197 (2004)

    MATH  Google Scholar 

  16. Pace, I.S., Barnett, S.: Comparison of algorithms for calculation of GCD of polynomials. Int. J. Syst. Sci. 4, 211–226 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  17. Triantafyllou, D., Mitrouli, M.: Two resultant based methods computing the greatest common divisor of two polynomials. Lect. Notes Comput. Sci. 3401, 519–526 (2005)

    Article  Google Scholar 

  18. Trefethen, L.N., Bau, D.: Numerical linear algebra, III. In: SIAM, pp. 234–239 (1997)

  19. Huffel, V.: Partial singular value decomposition algorithm. J. Comput. Appl. Math. 33, 105–112 (1990)

    Article  MATH  Google Scholar 

  20. Huffel, V., Vandewalle J.: An efficient and reliable algorithm for computing the singular subspace of a matrix, associated with its smallest singular values. J. Comput. Appl. Math. 19, 313–330 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  21. Wang, S., Davison, E.J.: A minimization algorithm for the design of linear multivariable systems. IEEE Trans. Automat. Contr. 18, 220–225 (1973)

    Article  MATH  MathSciNet  Google Scholar 

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Correspondence to Marilena Mitrouli.

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Triantafyllou, D., Mitrouli, M. On rank and null space computation of the generalized Sylvester matrix. Numer Algor 54, 297–324 (2010). https://doi.org/10.1007/s11075-009-9336-6

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  • DOI: https://doi.org/10.1007/s11075-009-9336-6

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