Some guidelines for using iterate averaging in stochastic approximation

被引：0
|
作者：
Maryak, JL [1 ]
机构：
[1] Johns Hopkins Univ, Appl Phys Lab, Laurel, MD 20723 USA
来源：
PROCEEDINGS OF THE 36TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-5 | 1997年
关键词：
stochastic approximation; iterate averaging; mean square error; finite-difference stochastic approximation; simultaneous perturbation stochastic approximation;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Averaging of the output (iterates) from a stochastic approximation (SA) recursion has been shown to be a useful technique for the gradient-based Robbins-Monro, form of SA. For the gradient-free (e.g., Kiefer-Wolfowitz) form, iterate averaging can produce an improvement in the stability of the algorithm and competitive mean-square errors relative to the standard (unaveraged) recursion. We discuss guidelines on how and when to use averaging in this context.
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页码：2287 / 2290
页数：4
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