An Elementary Derivation of the Projection Method for Nonlinear Eigenvalue Problems Based on Complex Contour Integration

Conference paper
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 117)

Abstract

The Sakurai-Sugiura (SS) projection method for the generalized eigenvalue problem has been extended to the nonlinear eigenvalue problem A(z)w = 0, where A(z) is an analytic matrix valued function, by several authors. To the best of the authors’ knowledge, existing derivations of these methods rely on canonical forms of an analytic matrix function such as the Smith form or the theorem of Keldysh. While these theorems are powerful tools, they require advanced knowledge of both analysis and linear algebra and are rarely mentioned even in advanced textbooks of linear algebra. In this paper, we present an elementary derivation of the SS-type algorithm for the nonlinear eigenvalue problem, assuming that the wanted eigenvalues are all simple. Our derivation uses only the analyticity of the eigenvalues and eigenvectors of a parametrized matrix A(z), which is a standard result in matrix perturbation theory. Thus we expect that our approach will provide an easily accessible path to the theory of nonlinear SS-type methods.

Notes

Acknowledgements

We thank Professor Masaaki Sugihara for valuable comments. We are also grateful to the anonymous referees, whose suggestions helped us much in improving the quality of this paper. Prof. Akira Imakura brought reference [8] to our attention. This study is supported in part by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Scientific Research (Nos. 26286087, 15H02708, 15H02709, 16KT0016, 17H02828, 17K19966) and the Core Research for Evolutional Science and Technology (CREST) Program “Highly Productive, High Performance Application Frameworks for Post Petascale Computing” of Japan Science and Technology Agency (JST).

References

  1. 1.
    Amako, T., Yamamoto, Y., Zhang, S.-L.: A large-grained parallel algorithm for nonlinear eigenvalue problems and its implementation using OmniRPC. In: Proceedings of IEEE International Conference on Cluster Computing, 2008, pp. 42–49. IEEE Press (2008)Google Scholar
  2. 2.
    Asakura, J., Sakurai, T., Tadano, H., Ikegami, T., Kimura, K.: A numerical method for nonlinear eigenvalue problems using contour integrals. JSIAM Lett. 1, 52–55 (2009)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Betcke, T., Voss, H.: A Jacobi-Davidson-type projection method for nonlinear eigenvalue problems. Futur. Gener. Comput. Syst. 20, 363–372 (2004)CrossRefGoogle Scholar
  4. 4.
    Beyn, W.-J.: An integral method for solving nonlinear eigenvalue problems. Linear Algebra Appl. 436, 3839–3863 (2012)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Gantmacher, F.R.: The Theory of Matrices. Chelsea, New York (1959)MATHGoogle Scholar
  6. 6.
    Gohberg, I., Rodman, L.: Analytic matrix functions with prescribed local data. J. d’Analyse Mathématique 40, 90–128 (1981)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd edn. Johns Hopkins University Press, Baltimore (1996)MATHGoogle Scholar
  8. 8.
    Ikegami, T., Sakurai, T., Nagashima, U.: A filter diagonalization for generalized eigenvalue problems based on the Sakurai-Sugiura projection method. J. Comput. Appl. Math. 233, 1927–1936 (2010)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Kato, T.: Perturbation Theory for Linear Operators, 2nd edn. Springer, Berlin (1976)MATHGoogle Scholar
  10. 10.
    Kato, T.: A Short Introduction to Perturbation Theory for Linear Operators. Springer, New York (1982)CrossRefMATHGoogle Scholar
  11. 11.
    Keldysh, M.V.: On the characteristic values and characteristic functions of certain classes of non-self-adjoint equations. Doklady Akad. Nauk SSSR (N. S.) 77, 11–14 (1951)Google Scholar
  12. 12.
    Keldysh, M.V.: The completeness of eigenfunctions of certain classes of nonselfadjoint linear operators. Uspehi Mat. Nauk 26(4(160)), 15–41 (1971)Google Scholar
  13. 13.
    Neumaier, A.: Residual inverse iteration for the nonlinear eigenvalue problem. SIAM J. Numer. Anal. 22, 914–923 (1985)MathSciNetCrossRefMATHGoogle Scholar
  14. 14.
    Ruhe, A.: Algorithms for the nonlinear eigenvalue problem. SIAM J. Numer. Anal. 10, 674–689 (1973)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Saad, Y.: Numerical Methods for Large Eigenvalue Problems. Halsted Press, New York (1992)MATHGoogle Scholar
  16. 16.
    Sakurai, T., Sugiura, H.: A projection method for generalized eigenvalue problems using numerical integration. J. Comput. Appl. Math. 159, 119–128 (2003)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Sakurai, T., Tadano, H., Inadomi, Y., Nagashima, U.: A moment-based method for large-scale generalized eigenvalue problems. Appl. Numer. Anal. Comput. Math. 1, 516–523 (2004)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Scott, D.S., Ward, R.C.: Solving symmetric-definite quadratic problems without factorization. SIAM J. Sci. Stat. Comput. 3, 58–67 (1982)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Sloan, I.H., Joe, S.: Lattice Methods for Multiple Integration. Oxford University Press, New York (1994)MATHGoogle Scholar
  20. 20.
    Trefethen, L.N., Weideman, J.A.C.: The exponentially convergent trapezoidal rule. SIAM Rev. 56, 385–458 (2014)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Voss, H.: An Arnoldi method for nonlinear eigenvalue problems. BIT Numer. Math. 44, 387–401 (2004)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Yokota, S., Sakurai, T.: A projection method for nonlinear eigenvalue problems using contour integrals. JSIAM Lett. 5, 41–44 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.The University of Electro-CommunicationsChofuJapan

Personalised recommendations