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Adaptive Consensus ADMM for Distributed Optimization

Xu, Z. ; Taylor, G. ; Li, H. ; Figueiredo, M. A. T. ; Yuan, X. ; Goldstein, T.

Adaptive Consensus ADMM for Distributed Optimization, Proc International Conf. on Machine Learning - ICML, Sidney, Australia, Vol. -, pp. 3841 - 3850, August, 2017.

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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.