2024/06/14 by Son Luu Nguyen, Lizhang Chen, Nguyen, Son +5 · 4 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Advanced Neural Network Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2406.09958
openalex publication_date 2024/06/14 · openalex created_date 2024/06/19 · openalex updated_date 2026/07/28
Modern deep learning heavily depends on adaptive optimizers such as Adam and its variants, which are renowned for their capacity to handle model scaling and streamline hyperparameter tuning. However, these algorithms typically experience high memory overhead caused by the accumulation of optimization states, leading to a critical challenge in training large-scale network models. In this study, we introduce a novel adaptive optimizer, H-Fac, which incorporates a memory-efficient factorization approach to address this challenge. By employing a rank-1 parameterization for both momentum and scaling parameter estimators, H-Fac reduces memory costs to a sublinear level while maintaining competitive performance across a wide range of architectures. We develop our algorithms based on principles derived from Hamiltonian dynamics, providing robust theoretical underpinnings in optimization dynamics and convergence guarantees. These optimization algorithms are designed to be both straightforward and adaptable, facilitating easy implementation in diverse settings.