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Convergence of algorithms for finding saddle points

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Literature Cited

  1. 1.

    D. Blackwell and M. A. Grishick, Theory of Games and Statistical Decisions, Wiley, New York (1966).

  2. 2.

    K. J. Arrow, L. Hurwicz, and H. Uzawa (editors), Studies in Linear and Non-Linear Programming, Stanford University Press, Stanford (1972).

  3. 3.

    E. G. Gol'shtein, “Generalized gradient method of finding saddle points,” Ekonom. Mat. Metody,8, No. 4 (1972).

  4. 4.

    Yu. M. Ermol'ev and N. Z. Shor, “On the minimization of nondifferentiable functions,” Kibernetika, No. 1, 101–102 (1967).

  5. 5.

    E. A. Nurminskii, “Convergence conditions for nonlinear programming algorithms,” Kibernetika, No. 6, 79–81 (1972).

  6. 6.

    Yu. M. Ermol'ev, Methods of Stochastic Programming [in Russian], Nauka, Moscow (1976).

  7. 7.

    Yu. M. Ermol'ev, “Methods for solving nonlinear extremal problems,” Kibernetika, No. 4, 1–18 (1966).

  8. 8.

    Yu. M. Ermol'ev and E. A. Nurminskii, “Limiting extremal problems,” Kibernetika, No. 4, 130–132 (1973).

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Additional information

Translated from Kibernetika, No. 3, pp. 112–116, May–June, 1977.

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Nurminskii, E.A., Verchenko, P.I. Convergence of algorithms for finding saddle points. Cybern Syst Anal 13, 430–435 (1977). https://doi.org/10.1007/BF01069664

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Keywords

  • Operating System
  • Artificial Intelligence
  • System Theory
  • Saddle Point
  • Find Saddle Point