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Exact Theory and Optimal Stepsizes for Stochastic Gradient Descent in Smooth Convex Minimization

Oct 30, 2026 Source ↗

Key Insights

This work considers the design of stepsize schedules for stochastic gradient descent in smooth convex optimization.
Throughout, we assume that gradient noise is conditionally zero-mean with bounded va.

Cite this synthesis DOI: 10.48550/arXiv.2610.42048 ↗

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@misc{ acaciamarkets-research-exact-theory-and-optimal-stepsizes-for-stochastic-gradient-d,
  title = { Exact Theory and Optimal Stepsizes for Stochastic Gradient Descent in Smooth Convex Minimization },
  author = { Leszek },
  year = { 2026 },
  doi = { 10.48550/arXiv.2610.42048 },
  url = { http://arxiv.org/abs/2610.42048v1 },
  note = {Summarized and classified by AcaciaFund}
}

TY  - GEN
TI  - Exact Theory and Optimal Stepsizes for Stochastic Gradient Descent in Smooth Convex Minimization
AU  - Leszek
PY  - 2026
DO  - 10.48550/arXiv.2610.42048
UR  - http://arxiv.org/abs/2610.42048v1
ER  -

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Overview

This work considers the design of stepsize schedules for stochastic gradient descent in smooth convex optimization. Throughout, we assume that gradient noise is conditionally zero-mean with bounded va

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market-microstructure quantitative-modeling signal-quality

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SQI 0.750