Adaptive Consensus ADMM for Distributed Optimization

Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein

ICML 2017 pp. 3841-3850

/icml/2017/xu2017icml-adaptive/

Abstract

The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed ADMM methods that boost performance by using different fine-tuned algorithm parameters on each worker node. We present a O(1/k) convergence rate for adaptive ADMM methods with node-specific parameters, and propose adaptive consensus ADMM (ACADMM), which automatically tunes parameters without user oversight.

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Cite

Text

Xu et al. "Adaptive Consensus ADMM for Distributed Optimization." International Conference on Machine Learning, 2017.

Markdown

[Xu et al. "Adaptive Consensus ADMM for Distributed Optimization." International Conference on Machine Learning, 2017.](https://mlanthology.org/icml/2017/xu2017icml-adaptive/)

BibTeX

@inproceedings{xu2017icml-adaptive,
  title     = {{Adaptive Consensus ADMM for Distributed Optimization}},
  author    = {Xu, Zheng and Taylor, Gavin and Li, Hao and Figueiredo, Mário A. T. and Yuan, Xiaoming and Goldstein, Tom},
  booktitle = {International Conference on Machine Learning},
  year      = {2017},
  pages     = {3841-3850},
  volume    = {70},
  url       = {https://mlanthology.org/icml/2017/xu2017icml-adaptive/}
}