Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate
arXiv:2605.25134v1 Announce Type: new Abstract: Sparse optimization is a fundamental challenge in various practical applications. A popular approach to sparse optimization is $\ell_p$ regularization. However, it may encounter optimization instability due to the unbounded gradients when $0<p<1$. In this paper, we introduce a novel approach to sparse optimization termed ReWA, based on Reparameterization, Weight decay, and Adaptive learning rate. ReWA is closely connected to $\ell_p$-regularization, yet it unveils a distinct optimization landscape that helps mitigate instability issues. Experimen
This paper addresses a known instability issue in sparse optimization, which is a fundamental challenge in AI model development, indicating ongoing refinement in foundational AI algorithms.
Improved sparse optimization techniques can lead to more efficient and stable AI models, impacting the computational cost and performance of various AI applications, particularly those requiring resource efficiency.
The introduction of ReWA provides a new methodological approach to mitigate instability in sparse optimization, potentially enabling more robust and practical implementations of sparse AI models.
- · AI researchers
- · Machine learning developers
- · Cloud computing providers
- · AI-driven industries with sparse data
- · Less efficient sparse optimization methods
More stable and efficient training of sparse AI models becomes possible.
Reduced computational resource demands for certain classes of AI problems, indirectly lowering operational costs for AI companies.
Acceleration in the development and deployment of lightweight AI models suitable for edge devices or applications with limited computational budgets.
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