Skip to main content
SpringerLink
Log in

Convergence conditions for nonlinear programming algorithms

  • Published:
Cybernetics Aims and scope

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Literature Cited

  1. W. I. Zangwill, “Convergence conditions for nonlinear programming algorithms,” Management Science,16, No. 1 (1969).

  2. Yu. I. Lyubich and G. D. Maistrovskii, “General theory of relaxation processes for convex functionals,” Uspekhi Matem. Nauk,24, No. 1 (1970).

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

  4. M. A. Aizerman, É. M. Braverman, and L. I. Rozonoér, The Method of Potential Functions in the Theory of Learning Machines [in Russian], Nauka, Moscow (1970).

    Google Scholar 

Download references

Authors
  1. E. A. Nurminskii
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

Translated from Kibernetika, No. 6, pp. 79–81, November–December, 1972.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nurminskii, E.A. Convergence conditions for nonlinear programming algorithms. Cybern Syst Anal 8, 959–962 (1972). https://doi.org/10.1007/BF01068520

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01068520

Keywords

Search

Navigation