Adaptive Consensus ADMM for Distributed Optimization

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.

Authors

• Zheng Xu 0002
• Gavin Taylor
• Hao Li 0022
• Mário A. T. Figueiredo
• Xiaoming Yuan
• Tom Goldstein

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