Emergent Mind
Adaptive Consensus ADMM for Distributed Optimization
(1706.02869)
Published Jun 9, 2017
in
cs.LG
,
cs.NA
,
and
cs.SY
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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