Analysis of Adam Algorithms for Stochastic Dynamic Systems
The adaptive moment estimation algorithm, known as Adam, is widely used in modern machine learning, owing to its low per-iteration complexity and strong empirical performance. Despite its prevalent use, the theoretical foundation of Adam remains largely unexplored for time-varying and nonstationary systems. In fact, the existing theoretical analyses of Adam-type algorithms are primarily concerned with time-invariant model parameters and explicitly or implicitly rely on independent and identically distributed (i.i.d.) data assumptions, under which the learning taskcan be formulated as minimizing a fixed expected objective with a static minimizer. However, such assumptions are often violate...
| Authors | Xin Zheng, Yifei Jin, Lei Guo |
|---|---|
| Primary category | cs.LG |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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