Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition
type: Dataset
description: We consider optimizing a function smooth convex function $f$ that is the average of a set of differentiable functions $f_i$, under the assumption considered by Solodov [1998] and Tseng [1998] that the norm of each gradient $f_i'$ is bounded by a linear function of the norm of the average gradient $f'$. We show that under these assumptions the basic stochastic gradient method with a sufficiently-small constant step-size has an $O(1/k)$ convergence rate, and has a linear convergence rate if $g$ is strongly-convex.
issued: 2026-05-09T23:48:19.454797
language: en
modified: 2026-05-09T23:48:19.454803
contact point: Organization
  fn: CCSD
keyword: convex-optimization
keyword: infoeu-reposemanticspreprint
keyword: mathmath-ocmathematics-mathoptimization-and-control-mathoc
keyword: preprints-working-papers-
keyword: stochastic-gradient
landing page: https://inria.hal.science/hal-00855113

Fast Convergence of Stochastic Gradient Descent under a Strong Growth Condition
type: Distribution
format: HTML
issued: 2026-05-09T23:48:19.456569
modified: 2026-05-09T23:48:19.447418
access URL: https://inria.hal.science/hal-00855113

test_moissonnage_selune
type: Agent