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arXiv Machine Learning By Yuhan Ye, Kaizhao Liu

Improved Gradient Descent Lower Bounds Beyond Nesterov

Read the original on arXiv Machine Learning →

The paper investigates the limits of accelerating gradient descent (GD) using predetermined step sizes in smooth convex optimization. It establishes new lower bounds: an ≥·n−1.6342 non‑anytime bound and an ≥·n−1.2408 anytime bound, surpassing previous results. These findings also demonstrate a strict separation between convergence exponents achievable in non‑anytime versus anytime settings.

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