Convergence Conditions for the Observed Mean Method in Stochastic Programming

Convergence Conditions for the Observed Mean Method in Stochastic Programming

The paper analyzes convergence conditions of the method of observed mean under nonstandard conditions, where dependent observations of random parameters are used and probabilistic optimization functions may be discontinuous indicators. For the case of dependent observations, large deviation type the...

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Veröffentlicht in:Cybernetics and systems analysis 2018-02, Vol.54 (1), p.45-59
Hauptverfasser: Knopov, P. S., Norkin, V. I.
Format: Artikel
Sprache:eng
Schlagworte:
Artificial Intelligence
Control
Convergence
Mathematics
Mathematics and Statistics
Processor Architectures
Software Engineering/Programming and Operating Systems
Stochastic programming
Systems Theory
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Zusammenfassung:The paper analyzes convergence conditions of the method of observed mean under nonstandard conditions, where dependent observations of random parameters are used and probabilistic optimization functions may be discontinuous indicators. For the case of dependent observations, large deviation type theorems for approximate optimal values and solutions are established.
ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-018-0006-3