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New gradient descent lower bounds established in optimization research

By PulseAugur Editorial · [1 sources] ·

Researchers have established new lower bounds for gradient descent in smooth convex optimization, improving upon existing theoretical limits. The study proves an $\Omega(n^{-1.6342})$ non-anytime lower bound and an $\Omega(n^{-1.2408})$ anytime lower bound. These findings represent advancements over previous bounds and demonstrate a clear distinction between achievable convergence rates in different settings. AI

IMPACT Refines theoretical understanding of optimization algorithms crucial for training AI models.

RANK_REASON Academic paper detailing theoretical advancements in optimization algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New gradient descent lower bounds established in optimization research

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Academic paper detailing theoretical advancements in optimization algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 Nederlands(NL) · Yuhan Ye, Kaizhao Liu ·

    Improved Gradient Descent Lower Bounds Beyond Nesterov

    arXiv:2609.02855v1 Announce Type: cross Abstract: We study how far gradient descent (GD) can be accelerated by predetermined stepsizes in smooth convex optimization. Going beyond the classical $\Omega(n^{-2})$ first-order oracle lower bound of Nemirovsky and Yudin, we prove an $\…

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