Convergence Rate Analysis of the AdamW-Style Shampoo: Unifying One-Sided and Two-Sided Preconditioning
arXiv:2601.07326v3 Announce Type: replace-cross Abstract: This paper studies the AdamW-style Shampoo optimizer, an effective implementation of classical Shampoo that notably won the external tuning track of the AlgoPerf neural network training algorithm competition. Our analysis unifies one-sided and two-sided preconditioning and establishes the convergence rate $\frac{1}{K}\sum_{k=1}^K E\left[\|\nabla f(X_k)\|_*\right]\leq O(\frac{\sqrt{m+n}C}{K^{1/4}})$ measured by nuclear norm, where $K$ represents the iteration number, $(m,n)$ denotes the size of matrix parameters, and $C$ matches the cons
The continuous evolution of AI optimization algorithms is driven by the demand for more efficient and robust neural network training, critical for advancing AI capabilities amidst increasing computational loads.
Improved optimization algorithms directly translate to faster, more stable, and potentially cheaper training of large AI models, impacting the pace and cost of AI development and deployment.
The analysis of AdamW-style Shampoo provides a deeper theoretical understanding and performance assurance for advanced optimization techniques, potentially guiding future AI research and application.
- · AI researchers
- · Neural network developers
- · Cloud AI providers
More efficient training of complex neural networks, leading to faster model development cycles.
Reduced computational costs for AI training could lower barriers to entry for AI development.
Accelerated progress in AI capabilities across various domains, potentially intensifying the AI race.
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Read at arXiv cs.LG