# Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization

arXiv: https://arxiv.org/abs/1703.00439 · HTML: https://mlsanity.com/paper/1703.00439/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

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

- [Distributed stochastic proximal algorithm with random reshuffling for non-smooth finite-sum optimization](https://mlsanity.com/paper/2111.03820/) (2021)
- [LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion](https://mlsanity.com/paper/2201.08516/) (2022)

## Abstract (arXiv)

In this paper, we develop a new accelerated stochastic gradient method for efficiently solving the convex regularized empirical risk minimization problem in mini-batch settings. The use of mini-batches is becoming a golden standard in the machine learning community, because mini-batch settings stabilize the gradient estimate and can easily make good use of parallel computing. The core of our proposed method is the incorporation of our new "double acceleration" technique and variance reduction technique. We theoretically analyze our proposed method and show that our method much improves the mini-batch efficiencies of previous accelerated stochastic methods, and essentially only needs size √n mini-batches for achieving the optimal iteration complexities for both non-strongly and strongly convex objectives, where n is the training set size. Further, we show that even in non-mini-batch settings, our method achieves the best known convergence rate for both non-strongly and strongly convex objectives.