# Accelerated Convex Optimization with Stochastic Gradients: Generalizing the Strong-Growth Condition

arXiv: https://arxiv.org/abs/2207.11833 · HTML: https://mlsanity.com/paper/2207.11833/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation](https://mlsanity.com/paper/2404.02378/) (2024)

## Abstract (arXiv)

This paper presents a sufficient condition for stochastic gradients not to slow down the convergence of Nesterov's accelerated gradient method. The new condition has the strong-growth condition by Schmidt & Roux as a special case, and it also allows us to (i) model problems with constraints and (ii) design new types of oracles (e.g., oracles for finite-sum problems such as SAGA). Our results are obtained by revisiting Nesterov's accelerated algorithm and are useful for designing stochastic oracles without changing the underlying first-order method.