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Convergence and rate of convergence for the method of parametric statistical gradients

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Literature Cited

  1. 1.

    V. Ya. Katkovnik, “Method of averaging operators in iterative algorithms for solving stochastic extremal problems,” Kibernetika, No. 3 (1972).

  2. 2.

    G. E. Antonov and V. Ya. Katkovnik, “Synthesis method for a class of random-search algorithms,” Avtomat i Telemekhan, No. 6 (1971).

  3. 3.

    V. Ya. Katkovnik and O. Yu. Kulchitskii, “Convergence of a class of random-search algorithms,” in: Theory and Application of Adaptive Systems [in Russian], Alma-Ata (1971).

  4. 4.

    Yu. M. Ermol'ev, “Method of generalized stochastic gradients and stochastic quasi-Fejer sequences,” Kibernetika, No. 2 (1969).

  5. 5.

    O. V. Guseva, “Rate of convergence of the method of generalized stochastic gradients,” Kibernetika, No. 4 (1971).

  6. 6.

    M. A. Aizerman, É. M. Braverman, and L. I. Rozanoer, Method of Potential Functions in the Theory of Machine Learning [in Russian], Nauka, Moscow (1971).

  7. 7.

    M. T. Wasan, Stochastic Approximation, Cambridge University Press (1969).

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Additional information

Translated from Kibernetika, No. 2, pp. 115–118, March–April, 1974.

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Katkovnik, V.Y., Ovcharova, L.V. Convergence and rate of convergence for the method of parametric statistical gradients. Cybern Syst Anal 10, 325–329 (1974). https://doi.org/10.1007/BF01068955

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Keywords

  • Operating System
  • Artificial Intelligence
  • System Theory
  • Statistical Gradient