Matrix AdaGrad: Row-wise and Column-wise Adaptive Subgradient Methods
Quick summary
arXiv:2609.21815v1 Announce Type: cross Abstract: Adaptive optimization methods such as AdaGrad and Adam are widely used in modern neural-network training, but their adaptive scaling is primarily designed for vector-valued parameters and does not explicitly exploit matrix structure. Recent matrix-aware optimizers demonstrate the benefits of structured optimization, yet a general theoretical framework for deriving matrix-aware adaptivity comparable to that of AdaGrad remains lacking. In this work, we develop a general Online Mirror Descent framework with adaptive proximal functions for matrix-v
Key takeaways
- arXiv:2609.21815v1 Announce Type: cross Abstract: Adaptive optimization methods such as AdaGrad and Adam are widely used in modern neural-network training, but their adaptive scaling is primarily designed for vector-valued parameters and does not explicitly exploit matrix structure.
- Recent matrix-aware optimizers demonstrate the benefits of structured optimization, yet a general theoretical framework for deriving matrix-aware adaptivity comparable to that of AdaGrad remains lacking.
- In this work, we develop a general Online Mirror Descent framework with adaptive proximal functions for matrix-v
Why it matters
“Matrix AdaGrad: Row-wise and Column-wise Adaptive Subgradient Methods” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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