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Structured Spectral Step-Sizes and Rebound-Aware Ordering for Gradient Methods
The performance of gradient methods depends critically on step-size selection. Spectral step-size design involves constructing candidates from finite history and ordering them during iteration. We address both through a finite-moment spectral framework. For candidate construction, we relate Huang--D
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