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This is a Quantitative Study study.

October 3, 2025Open Access

Natural Spectral Fusion: p-Exponent Cyclic Scheduling and Early Decision-Boundary Alignment in First-Order Optimization

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Authors

GZGongyue Zhang HLHonghai LiuTongji University

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Implication

Observational analysis reveals p-exponent cyclic scheduling enhances spectral coverage in first-order optimization, suggesting better decision-boundary alignment.

Key Points

Natural Spectral Fusion reduces test error across multiple benchmarks while enabling early decision-boundary alignment, improving overall accuracy.
p-exponent cyclic scheduling improves optimization efficiency, achieving baseline accuracy with only one-quarter of the typical training cost on certain tasks.
Adaptive methods emphasize low frequencies, while SGD maintains neutrality; negative exponents boost high-frequency information during optimization.
Spectral behaviors significantly influence optimization paths, highlighting the optimizer's role as a spectral controller in first-order techniques.

Cite This Study

Zhang et al. (2025) studied this question.

synapsesocial.com/papers/68e02f46f0e39f13e7fa2bbe https://doi.org/10.48550/arxiv.2509.04713

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Implication

Observational analysis reveals p-exponent cyclic scheduling enhances spectral coverage in first-order optimization, suggesting better decision-boundary alignment.

Natural Spectral Fusion: p-Exponent Cyclic Scheduling and Early Decision-Boundary Alignment in First-Order Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

Natural Spectral Fusion: p-Exponent Cyclic Scheduling and Early Decision-Boundary Alignment in First-Order Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

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