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Muon with Finite Newton-Schulz: The Smoothing Benefit in Nonsmooth Nonconvex Optimization
Not specified in the abstract
nonsmooth optimizationnonconvex optimizationmatrix optimization
2608.26288
Builder Relevance
1h ago70%
Abstract
Muon demonstrates that finite Newton-Schulz iterations can enhance optimization in nonsmooth nonconvex scenarios.
Reality Card
Core Claim
Finite Newton-Schulz iterations in Muon can improve convergence to stationary points in nonsmooth nonconvex optimization, contrary to previous assumptions.
Method / Result
A Newton-Schulz depth growing logarithmically in target accuracy suffices for convergence.
Limitations
The paper does not specify authors or provide detailed experimental validation, which may affect reproducibility.
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