The case for and against fixed step-size: Stochastic approximation algorithms in optimization and machine learning

Caio Kalil Lauand, Ioannis Kontoyiannis, Sean Meyn

Theory and application of stochastic approximation (SA) have become increasingly relevant due in part to applications in optimization and reinforcement learning. This paper takes a…