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Poster

Adaptive Consensus ADMM for Distributed Optimization

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

Gallery #28

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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