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Siberian Mathematical Journal

, Volume 30, Issue 4, pp 505–510 | Cite as

Singular numbers of a boundary-value problem on the halfline for a linear system of ordinary differential equations

  • S. K. Godunov
  • V. M. Gordienko
Article

Keywords

Differential Equation Linear System Ordinary Differential Equation Singular Number 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Literature Cited

  1. 1.
    F. R. Gantmacher, Matrix Theory [in Russian], Nauka, Moscow (1967).Google Scholar
  2. 2.
    S. K. Godunov, A. G. Antonov, O. P. Kirilyuk, and V. I. Kostin, Guaranteed Sharpness of Solution of Systems of Linear Equations in Euclidean Spaces [in Russian], Nauka, Novosibirsk (1988).Google Scholar
  3. 3.
    I. Ts. Gokhberg and M. G. Krein, Introduction to the Theory of Nonself-Adjoint Linear Operators in a Hilbert Space [in Russian], Nauka, Moscow (1965).Google Scholar
  4. 4.
    K. I. Babenko, Fundamentals of Numerical Analysis [in Russian], Nauka, Moscow (1986).Google Scholar
  5. 5.
    S. K. Godunov and V. S. Ryaben'kii, Difference Schemes [in Russian], Nauka, Moscow (1977).Google Scholar
  6. 6.
    M. G. Krein, “Integral equations on the halfline with the kernel depending on the difference of arguments,” Usp. Mat. Nauk,13, No. 5, 3–10 (1958).Google Scholar
  7. 7.
    F. Riesz and B. Sz-Nagy, Functional Analysis, Ungar, Frederick Publ. Co., New York.Google Scholar
  8. 8.
    N. A. Akhiezer and I. M. Glazman, Theory of Linear Operators in a Hilbert Space [in Russian], Nauka, Moscow (1966).Google Scholar
  9. 9.
    B. L. Van Der Waerden, Algebra, 2 vols., Ungar, Frederick Publ. Co., New York (1974).Google Scholar

Copyright information

© Plenum Publishing Corporation 1990

Authors and Affiliations

  • S. K. Godunov
  • V. M. Gordienko

There are no affiliations available

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