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Gradient Descent Last Iterate Suboptimal Without Prior Step Count

By PulseAugur Editorial · [1 sources] ·

A new paper published on arXiv explores the convergence properties of gradient descent (GD) and stochastic gradient descent (SGD) algorithms. The research proves a conjecture stating that without prior knowledge of the total number of steps (T), no stepsize sequence can guarantee the optimal error rate for SGD's last iterate. Furthermore, the study demonstrates that even in the noiseless case of GD, an excess poly-log factor in T is unavoidable when aiming for an anytime last iterate guarantee. AI

IMPACT Theoretical findings on optimization algorithms could influence future AI model training efficiency.

RANK_REASON Academic paper detailing theoretical findings on optimization algorithms. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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

Gradient Descent Last Iterate Suboptimal Without Prior Step Count

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Academic paper detailing theoretical findings on optimization algorithms. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Guy Kornowski, Ohad Shamir ·

    Gradient Descent's Last Iterate is Often (slightly) Suboptimal

    arXiv:2604.13870v2 Announce Type: replace-cross Abstract: We consider the well-studied setting of minimizing a convex Lipschitz function using either gradient descent (GD) or its stochastic variant (SGD), and examine the last iterate convergence. By now, it is known that standard…

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