Stochastic Approximation

Stochastic approximation (SA) is an iterative method for finding the root of an operator using noisy observations, crucial for solving optimization problems where exact gradients are unavailable. Current research emphasizes improving SA's efficiency and robustness, focusing on variance reduction techniques, adaptive step sizes, and handling Markovian noise and delays in various settings, including distributed and federated learning, reinforcement learning, and temporal difference learning. These advancements are significant for tackling large-scale optimization problems in diverse fields, leading to improved algorithms with stronger convergence guarantees and enhanced applicability to real-world scenarios.