mlsanityData to 14 Oct 2026

On a Conjecture Regarding the Adam Optimizer

arXiv 2111.08162 ↗First posted 16 Nov 2021cs.LGcited by 0

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Abstract (arXiv)

Why does the Adam optimizer work so well in deep-learning applications? Adam's originators, Kingma and Ba, presented a mathematical argument that was meant to help explain its success, but Bock and colleagues have since reported that a key piece is missing from that argument - an unproven lemma which we will call Bock's conjecture. Here we show that this conjecture is false, but we prove a modified version of it - a generalization of a result of Reddi and colleagues - which can take its place in analyses of Adam.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 7 references that are in the map. First posted on arXiv 16 Nov 2021.

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References · 4

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019On the Convergence of Adam and Beyondcited by 841
  3. 2018An improvement of the convergence proof of the ADAM-Optimizercited by 19
  4. 2021Local Convergence of Adaptive Gradient Descent Optimizerscited by 5

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