TL;DR
This paper reviews the evolution of deep learning optimization algorithms, providing a comprehensive empirical evaluation, identifying trends, and offering guidance for future efficient and robust methods.
Contribution
It offers a retrospective analysis, empirical comparison across models, and synthesizes insights to guide future optimization method development.
Findings
Identifies key trends and trade-offs in optimization algorithms.
Provides extensive empirical evaluation across diverse scenarios.
Synthesizes theoretical and practical insights for future research.
Abstract
Balancing convergence speed, generalization capability, and computational efficiency remains a core challenge in deep learning optimization. First-order gradient descent methods, epitomized by stochastic gradient descent (SGD) and Adam, serve as the cornerstone of modern training pipelines. However, large-scale model training, stringent differential privacy requirements, and distributed learning paradigms expose critical limitations in these conventional approaches regarding privacy protection and memory efficiency. To mitigate these bottlenecks, researchers explore second-order optimization techniques to surpass first-order performance ceilings, while zeroth-order methods reemerge to alleviate memory constraints inherent to large-scale training. Despite this proliferation of methodologies, the field lacks a cohesive framework that unifies underlying principles and delineates…
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