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

梯度下降最后一次迭代在没有事先步数计数的情况下是次优的

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

一篇新发表在arXiv上的论文探讨了梯度下降（GD）和随机梯度下降（SGD）算法的收敛性质。研究证明了一个猜想，即在不知道总步数（T）的情况下，任何步长序列都不能保证SGD最后一次迭代的最优误差率。此外，研究表明，即使在GD的无噪声情况下，当目标是任何时候的最后一次迭代保证时，T中的多对数因子过剩也是不可避免的。 AI

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在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Guy Kornowski, Ohad Shamir · 2026-10-05 04:00

梯度下降的最后一次迭代通常（略微）次优

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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梯度下降的最后一次迭代通常（略微）次优

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