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Error Bounds for Lanczos Approximations of Rational Functions of Matrices

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Numerical Validation in Current Hardware Architectures

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5492))

Abstract

Having good estimates or even bounds for the error in computing approximations to expressions of the form f(A)v is very important in practical applications. In this paper we consider the case that A is Hermitian and that f is a rational function. We assume that the Lanczos method is used to compute approximations for f(A)v and we show how to obtain a posteriori upper and lower bounds on the ℓ2-norm of the approximation error. These bounds are computed by minimizing and maximizing a rational function whose coefficients depend on the iteration step. We use global optimization based on interval arithmetic to obtain these bounds and include a number of experimental results illustrating the quality of the error estimates.

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References

  1. Alefeld, G., Herzberger, J.: Introduction to Interval Computation. Academic Press, London (1983)

    MATH  Google Scholar 

  2. Arnold, G., Cundy, N., van den Eshof, J., Frommer, A., Krieg, S., Lippert, T., Schäfer, K.: Numerical methods for the QCD overlap operator. II: Optimal Krylov subspace methods. In: Boriçi, et al. (eds.) [4]

    Google Scholar 

  3. Baker, G.A., Graves-Morris, P.: Padé Approximants, Encyclopedia of Mathematics and its applications. Cambridge University Press, Cambridge (1996)

    Google Scholar 

  4. Boriçi, A., Frommer, A., Joó, B., Kennedy, A., Pendleton, B. (eds.): Methods of Algorithmic Language Implementation. Lecture Notes in Computational Science and Engineering, vol. 47. Springer, Berlin (2005)

    MATH  Google Scholar 

  5. Carpenter, A.J., Ruttan, A., Varga, R.S.: Extended numerical computations on the 1/9 conjecture in rational approximation theory. In: Graves-Morris, P.R., Saff, E.B., Varga, R.S. (eds.) Rational Approximation and Interpolation. Lecture Notes in Mathematics, vol. 1105, pp. 383–411. Springer, Berlin (1984)

    Chapter  Google Scholar 

  6. Druskin, V., Knizhnerman, L.: Two polynomial methods of calculating functions of symmetric matrices. U.S.S.R. Comput. Math. Math. Phys. 29, 112–121 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  7. Frommer, A., Simoncini, V.: Stopping criteria for rational matrix functions of hermitian and symmetric matrices. SIAM J. Sci. Comput. 30, 1387–1412 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  8. Gallopoulos, E., Saad, Y.: Efficient solution of parabolic equations by Krylov approximation methods. SIAM J. Sci. Stat. Comput. 13, 1236–1264 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  9. Grimm, V., Hochbruck, M.: Rational approximation to trigonometric operators. BIT 48, 215–229 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  10. Hairer, E., Lubich, C., Wanner, G.: Geometric numerical integration. In: Structure-preserving algorithms for ordinary differential equations. Springer Series in Computational Mathematics, vol. 31. Springer, Berlin (2002)

    Google Scholar 

  11. Hansen, E.R., Walster, W.G.: Global Optimization Using Interval Analysis, 2nd edn. Marcel Dekker, New York (2004)

    MATH  Google Scholar 

  12. Higham, N.J.: Matrix Functions – Theory and Applications. SIAM, Philadelphia (2008)

    Google Scholar 

  13. Hochbruck, M., Lubich, C.: On Krylov subspace approximations to the matrix exponential operator. SIAM J. Numer. Anal. 34, 1911–1925 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  14. Hochbruck, M., Ostermann, A.: Exponential Runge-Kutta methods for parabolic problems. Applied Numer. Math. 53, 323–339 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  15. Hochbruck, M., van den Eshof, J.: Preconditioning Lanczos approximations to the matrix exponential. SIAM J. Sci. Comput. 27, 1438–1457 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  16. Horn, R.A., Johnson, C.R.: Topics in Matrix Analysis. Cambridge University Press, Cambridge (1994)

    MATH  Google Scholar 

  17. Kearfott, R.B.: Rigorous Global Search: Continuous Problems. Kluwer Academic Publishers, Dordrecht (1996)

    Book  MATH  Google Scholar 

  18. Lopez, L., Simoncini, V.: Analysis of projection methods for rational function approximation to the matrix exponential. SIAM J. Numer. Anal. 44, 613–635 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  19. The MathWorks, Inc., MATLAB 7 (September 2004)

    Google Scholar 

  20. Moret, I., Novati, P.: RD-rational approximations of the matrix exponential. BIT, Numerical Mathematics 44, 595–615 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  21. National Institute of Standards and Technology, Matrix market

    Google Scholar 

  22. Neumaier, A.: Interval Methods for Systems of Equations. Cambridge University Press, Cambridge (1990)

    MATH  Google Scholar 

  23. Paige, C.C., Parlett, B.N., van der Vorst, H.A.: Approximate solutions and eigenvalue bounds from Krylov subspaces. Numerical Linear Algebra with Applications 2, 115–134 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  24. Petrushev, P.P., Popov, V.A.: Rational Approximation of Real Functions. Cambridge University Press, Cambridge (1987)

    MATH  Google Scholar 

  25. Popolizio, M., Simoncini, V.: Acceleration techniques for approximating the matrix exponential operator. SIAM J. Matrix Analysis and Appl. 30, 657–683 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  26. Ratschek, H., Rokne, J.: New Computer Methods for Global Optimization. Ellis Horwood, Chichester (1988)

    MATH  Google Scholar 

  27. Rump, S.M.: INTLAB – INTerval LABoratory. In: Csendes, T. (ed.) Developments in Reliabale Computing, pp. 77–104. Kluwer, Dordrecht (1999)

    Chapter  Google Scholar 

  28. Saad, Y.: Analysis of some Krylov subspace approximations to the matrix exponential operator. SIAM J. Numer. Anal. 29, 209–228 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  29. van den Eshof, J., Frommer, A., Lippert, T., Schilling, K., van der Vorst, H.A.: Numerical methods for the QCD overlap operator. I: Sign-function and error bounds. Comput. Phys. Commun. 146, 203–224 (2002)

    Article  MATH  Google Scholar 

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Frommer, A., Simoncini, V. (2009). Error Bounds for Lanczos Approximations of Rational Functions of Matrices. In: Cuyt, A., Krämer, W., Luther, W., Markstein, P. (eds) Numerical Validation in Current Hardware Architectures. Lecture Notes in Computer Science, vol 5492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01591-5_13

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  • DOI: https://doi.org/10.1007/978-3-642-01591-5_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01590-8

  • Online ISBN: 978-3-642-01591-5

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