Avtomatika i Telemekhanika
RUS  ENG    JOURNALS   PEOPLE   ORGANISATIONS   CONFERENCES   SEMINARS   VIDEO LIBRARY   PACKAGE AMSBIB  
General information
Latest issue
Archive
Impact factor
Guidelines for authors
Submit a manuscript

Search papers
Search references

RSS
Latest issue
Current issues
Archive issues
What is RSS


Avtomat. i Telemekh.:
Year:
Volume:
Issue:
Page:
Find




Personal entry:
Login:
Password:
Save password
Enter
Forgotten password?
Register

Avtomatika i Telemekhanika, 1978, Issue 2, Pages 62–71 (Mi at9658)  
Adaptive Systems

Design of multi-level gradient the algorithms in bivalent programming problems

M. I. Garusin

Moscow
Full-text PDF (1567 kB)
Abstract: Randomization leads to a variational way to design of multilevel adaptive bivalent programming algorithms by using the «conditions of local improvements». The probabilistic approach to solution of optimization problems helps unify the solution algorithms. Experimental results are given.

Received: 14.12.1976
Bibliographic databases:
Document Type: Article
UDC: 518.5:519.8
Language: Russian
Citation: M. I. Garusin, “Design of multi-level gradient the algorithms in bivalent programming problems”, Avtomat. i Telemekh., 1978, no. 2, 62–71; Autom. Remote Control, 39:2 (1978), 207–214
Citation in format AMSBIB
\Bibitem{Gar78}
\by M.~I.~Garusin
\paper Design of multi-level gradient the algorithms in bivalent programming problems
\jour Avtomat. i Telemekh.
\yr 1978
\issue 2
\pages 62--71
\mathnet{http://mi.mathnet.ru/at9658}
\zmath{https://zbmath.org/?q=an:0419.93047}
\transl
\jour Autom. Remote Control
\yr 1978
\vol 39
\issue 2
\pages 207--214
Linking options:
  • https://www.mathnet.ru/eng/at9658
  • https://www.mathnet.ru/eng/at/y1978/i2/p62
    • Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Avtomatika i Telemekhanika
     
      Contact us:
    		  Terms of Use  Registration to the website  Logotypes © Steklov Mathematical Institute RAS, 2024