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arXiv · 1706.02869

Adaptive Consensus ADMM for Distributed Optimization

Zheng Xu·Gavin Taylor·Hao Li·Mario Figueiredo·Xiaoming Yuan·Tom Goldstein
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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.

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Zheng Xu, Gavin Taylor, Hao Li, Mario Figueiredo, Xiaoming Yuan, Tom Goldstein. 2017-06-09. Adaptive Consensus ADMM for Distributed Optimization. https://arxiv.org/abs/1706.02869

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