# Accelerated Stochastic Gradient Descent for Minimizing Finite Sums

arXiv: https://arxiv.org/abs/1506.03016 · HTML: https://mlsanity.com/paper/1506.03016/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2015.

## Built on by (most cited first, 2 of 2)

- [Stochastic Steffensen method](https://mlsanity.com/paper/2211.15310/) (2022)
- [Adaptive Stochastic Optimisation of Nonconvex Composite Objectives](https://mlsanity.com/paper/2211.11710/) (2022)

## Abstract (arXiv)

We propose an optimization method for minimizing the finite sums of smooth convex functions. Our method incorporates an accelerated gradient descent (AGD) and a stochastic variance reduction gradient (SVRG) in a mini-batch setting. Unlike SVRG, our method can be directly applied to non-strongly and strongly convex problems. We show that our method achieves a lower overall complexity than the recently proposed methods that supports non-strongly convex problems. Moreover, this method has a fast rate of convergence for strongly convex problems. Our experiments show the effectiveness of our method.