A versatile method for the Monte Carlo optimization of stochastic systems†
作者:
H. J. KUSHNER,
T. GAVIN,
期刊:
International Journal of Control
(Taylor Available online 1973)
卷期:
Volume 18,
issue 5
页码: 963-975
ISSN:0020-7179
年代: 1973
DOI:10.1080/00207177308932573
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
The paper discusses a versatile family of Monte Carlo methods for the sequential optimization of stochastic systems. The method selects a sequence of successive one-dimensional search directions, defines a (stochastic) search in each of the directions, where the data used for both the one-dimensional search and the direction determination are merely noise-corrupted observations on the system. The method is more general than stochastic approximation, it converges to a stationary point even in the presence of multiple minima, and it uses rather natural logics. A convergence theorem is proved.
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