# Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategies

arXiv: https://arxiv.org/abs/1704.00116 · HTML: https://mlsanity.com/paper/1704.00116/

## Key facts

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

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

- [Curvature-Aware Training for Coordinate Networks](https://mlsanity.com/paper/2305.08552/) (2023)
- [Stochastic Steffensen method](https://mlsanity.com/paper/2211.15310/) (2022)

## Abstract (arXiv)

We revisit the stochastic limited-memory BFGS (L-BFGS) algorithm. By proposing a new framework for the convergence analysis, we prove improved convergence rates and computational complexities of the stochastic L-BFGS algorithms compared to previous works. In addition, we propose several practical acceleration strategies to speed up the empirical performance of such algorithms. We also provide theoretical analyses for most of the strategies. Experiments on large-scale logistic and ridge regression problems demonstrate that our proposed strategies yield significant improvements vis-`a-vis competing state-of-the-art algorithms.