Abstract
A parametrized extension of the Kushner-Clark condition is introduced for the study of convergence of stochastic approximation algorithms. Our results provide necessary and sufficient conditions for convergence that hold in a Hilbert space setting and apply to general gain sequences. These results exhibit the interplay among the noise sequence, the gain sequence, and key properties of the underlying function. The proof is direct, completely deterministic, and is elementary, involving only basic notions of convergence. Some corollaries to our main result are also presented.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 3843-3848 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE Conference on Decision and Control |
| Volume | 4 |
| State | Published - 1995 |
| Event | Proceedings of the 1995 34th IEEE Conference on Decision and Control. Part 1 (of 4) - New Orleans, LA, USA Duration: Dec 13 1995 → Dec 15 1995 |
All Science Journal Classification (ASJC) codes
- Control and Optimization
- Control and Systems Engineering
- Modeling and Simulation