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.

引用

页码：2287 / 2290

页数：4

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