# Conditional Accelerated Lazy Stochastic Gradient Descent

arXiv: https://arxiv.org/abs/1703.05840 · HTML: https://mlsanity.com/paper/1703.05840/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Efficient Projection-Free Online Convex Optimization with Membership Oracle](https://mlsanity.com/paper/2111.05818/) (2021)
- [Zeroth and First Order Stochastic Frank-Wolfe Algorithms for Constrained Optimization](https://mlsanity.com/paper/2107.06534/) (2021)
- [Projection-Free Functional Constrained Optimization for Risk Aversion and Sparsity Control](https://mlsanity.com/paper/2210.05108/) (2022)
- [Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms](https://mlsanity.com/paper/2507.17545/) (2025)

## Abstract (arXiv)

In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate Oleft(frac1varepsilon2right) improving over the projection-free, Online Frank-Wolfe based stochastic gradient descent of Hazan and Kale [2012] with convergence rate Oleft(frac1varepsilon4right).