Asymptotic Convergence and Stability of Adaptive Gradient Methods in Smooth Non-convex Optimization

Adaptive gradient methods, such as AdaGrad, have become fundamental tools in deep learning. Despite their widespread use, the asymptotic convergence of AdaGrad remains poorly understood in non-convex scenarios. In this work, we present the first rigorous asymptotic convergence analysis of AdaGrad-Norm for smooth non-convex optimization. Using a novel stopping-time partitioning technique, we establish a key stability result: the objective function values remain bounded in expectation, and the iterates are bounded almost surely under a mild coercivity assumption. Building on these stability results, we prove that AdaGrad-Norm achieves both almost sure and mean-square convergence. Furthermore, we extend our analysis to RMSProp and show that, with appropriate hyperparameter choices, it also enjoys stability and asymptotic convergence. The techniques developed herein may be of independent interest for analyzing other adaptive stochastic optimization algorithms.

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