Weighted averaging and stochastic approximation

I. Jeng Wang, Edwin K P Chong, Sanjeev R. Kulkarni

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

We explore the relationship between weighted averaging and stochastic approximation algorithms, and study their convergence via a sample-path analysis. We prove that the convergence of a stochastic approximation algorithm is equivalent to the convergence of the weighted average of the associated noise sequence. We also present necessary and sufficient noise conditions for convergence of the average of the output of a stochastic approximation algorithm in the linear case. We show that the averaged stochastic approximation algorithms can tolerate a larger class of noise sequences than the stand-alone stochastic approximation algorithms.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Conference on Decision and Control
Editors Anon
Volume1
StatePublished - Dec 1 1996
Externally publishedYes
EventProceedings of the 1996 35th IEEE Conference on Decision and Control. Part 3 (of 4) - Kobe, Jpn
Duration: Dec 11 1996Dec 13 1996

Other

OtherProceedings of the 1996 35th IEEE Conference on Decision and Control. Part 3 (of 4)
CityKobe, Jpn
Period12/11/9612/13/96

All Science Journal Classification (ASJC) codes

  • Chemical Health and Safety
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality

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    Wang, I. J., Chong, E. K. P., & Kulkarni, S. R. (1996). Weighted averaging and stochastic approximation. In Anon (Ed.), Proceedings of the IEEE Conference on Decision and Control (Vol. 1)