Name
Abstract | ||
|---|---|---|
Alternating Direction Method of Multipliers (ADMM) is a popular method in solving Machine Learning problems. Stochastic ADMM was firstly proposed in order to reduce the per iteration computational complexity, which is more suitable for big data problems. Recently, variance reduction techniques have been integrated with stochastic ADMM in order to get a fast convergence rate, such as SAG-ADMM and SVRG-ADMM,but the convergence is still suboptimal w.r.t the smoothness constant. In this paper, we propose a new accelerated stochastic ADMM algorithm with variance reduction, which enjoys a faster convergence than all the other stochastic ADMM algorithms. We theoretically analyze its convergence rate and show its dependence on the smoothness constant is optimal. We also empirically validate its effectiveness and show its priority over other stochastic ADMM algorithms. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Numerical Analysis | Convergence (routing),Mathematical optimization,Rate of convergence,Smoothness,Variance reduction,Big data,Mathematics,Computational complexity theory |
DocType | Volume | Citations |
Journal | abs/1611.04074 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chao Zhang | 1 | 8 | 5.49 |
| Zebang Shen | 2 | 17 | 9.36 |
| Hui Qian | 3 | 59 | 13.26 |
| Tengfei Zhou | 4 | 22 | 5.08 |