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Arithmetic Simplicity in Stochastic Gradient Methods

arXiv 2609.32240 ↗First posted 26 Sep 2026cs.LGcited by 0

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Abstract (arXiv)

A gradient descent method is arithmetically simple if the operations are limited to +,-, ×, and division x/2t with integer t. An arthmetically simple gradient method is easy to implement in chip design. We show how to transform AdaGrad, Adam, and AdamW into arithmetically simple. AdamW is based on the recursion xt+1=(1-λη)xt-fracη smt and Adam is the special case of AdamW with λ=0. We transform them into a static case with s=S(T), where T is the number of iterations, and S(T) is a fixed function. The convergence analysis is given for a static Adam, which is also arithmetically simple.

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References · 4

  1. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  2. 2018AdaGrad stepsizes: Sharp convergence over nonconvex landscapescited by 182
  3. 2019Linear Convergence of Adaptive Stochastic Gradient Descentcited by 29
  4. 2026Adaptivity via a Parallel Architecture for Stochastic Gradient Methodscited by 1

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