Adaptive Consensus ADMM for Distributed Optimization


Zheng Xu, Gavin Taylor, Hao Li, Mário A. T. Figueiredo, Xiaoming Yuan, Tom Goldstein ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3841-3850, 2017.


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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