# Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization

arXiv: https://arxiv.org/abs/1705.04138 · HTML: https://mlsanity.com/paper/1705.04138/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2017.

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

- [Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence](https://mlsanity.com/paper/2104.08736/) (2021)
- [A General Continuous-Time Formulation of Stochastic ADMM and Its Variants](https://mlsanity.com/paper/2404.14358/) (2024)

## Abstract (arXiv)

We consider the stochastic composition optimization problem proposed in citewang2017stochastic, which has applications ranging from estimation to statistical and machine learning. We propose the first ADMM-based algorithm named com-SVR-ADMM, and show that com-SVR-ADMM converges linearly for strongly convex and Lipschitz smooth objectives, and has a convergence rate of O( log S/S), which improves upon the O(S-4/9) rate in citewang2016accelerating when the objective is convex and Lipschitz smooth. Moreover, com-SVR-ADMM possesses a rate of O(1/√S) when the objective is convex but without Lipschitz smoothness. We also conduct experiments and show that it outperforms existing algorithms.