On equivalence of some noise conditions for stochastic approximation algorithms

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

Research output: Contribution to journalConference article

4 Scopus citations

Abstract

We study four conditions on noise sequences for convergence of stochastic approximation algorithms on a general Hilbert space: Kushner and Clark's condition, Chen's condition, Kulkarni and Horn's condition, and a decomposition condition. We discuss various properties of these conditions. In our main result we show that the four conditions are all equivalent, and are both necessary and sufficient for convergence of stochastic approximation algorithms under appropriate assumptions.

Original languageEnglish (US)
Pages (from-to)3849-3854
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume4
StatePublished - Dec 1 1995
Externally publishedYes
EventProceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4) - New Orleans, LA, USA
Duration: Dec 13 1995Dec 15 1995

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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