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Adaptive Consensus ADMM for Distributed Optimization
Zheng Xu · Gavin Taylor · Hao Li · Mario Figueiredo · Xiaoming Yuan · Tom Goldstein
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.
Summary/NotesAuthor Information
Zheng Xu (University of Maryland)
Gavin Taylor (US Naval Academy)
Hao Li (University of Maryland at College Park)
Mario Figueiredo (Instituto Superior Tecnico)
Xiaoming Yuan
Tom Goldstein (University of Maryland)
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2017 Poster: Adaptive Consensus ADMM for Distributed Optimization »
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