Cybernetics

, Volume 12, Issue 4, pp 547–553

Method of the generalized gradient for finding the absolute minimum of a convex function

  • M. A. Shepilov
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Literature Cited

  1. 1.
    H. Busemann, Convex Surfaces, Wiley, New York (1958).Google Scholar
  2. 2.
    M. M. Vainberg, The Variational Method and the Method of Monotonic Operators [in Russian], Fizmatgiz, Moscow (1972).Google Scholar
  3. 3.
    E. G. Gol'shtein, “Generalized gradient method for finding saddle points” Ékonom. Mat. Metody,8, No. 4 (1972).Google Scholar
  4. 4.
    Yu. M. Ermol'ev, “Methods for solving nonlinear extremal problems,” Kibernetika, No. 4 (1966).Google Scholar
  5. 5.
    B. T. Polyak, “A general method for solving extremal problems” Dok. Akad. Nauk SSSR174, No. 1 (1967).Google Scholar
  6. 6.
    Ch'ang Wang T'uk, “On certain iterative methods of block programming,” Candidate's Dissertation, Moscow (1972).Google Scholar
  7. 7.
    N. Z. Shor, “Generalized gradient descent,” in: Proceedings of the First Winter School on Mathematical Programming, Drogobych [in Russian], Moscow (1969).Google Scholar
  8. 8.
    N. Z. Shor, “On the structure of numerical algorithms for solving optimal planning and design problems,” Candidate's Dissertation, Institute of Cybernetics, Academy of Sciences of the Ukrainian SSR, Kiev (1964).Google Scholar
  9. 9.
    N. Z. Shor, “Methods for minimizing nondifferentiable functions,” Author's Abstract of Doctoral Dissertation, Kiev (1970).Google Scholar

Copyright information

© Plenum Publishing Corporation 1977

Authors and Affiliations

  • M. A. Shepilov

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