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Adaptive Uncertainty-Weighted ADMM for Distributed Optimization by Ye, Jianping; Wan, Caleb; Fung, Samy Wu is a scholarly article available to read on EtoBox.

What is Adaptive Uncertainty-Weighted ADMM for Distributed Optimization about?

We present AUQ-ADMM, an adaptive uncertainty-weighted consensus ADMM method for solving large-scale convex optimization problems in a distributed manner. Our key contribution is a novel adaptive weighting scheme that empirically increases the progress made by consensus ADMM scheme and is attractive when using a large number of subproblems. The weights are related to the uncertainty associated with the solutions of each subproblem, and are efficiently computed using low-rank approximations. We show AUQ-ADMM provably converges and demonstrate its effectiveness on a series of machine learning applications, including elastic net regression, multinomial logistic regression, and support vector machines. We provide an implementation based on the PyTorch package.

Author

Ye, Jianping; Wan, Caleb; Fung, Samy Wu

Published

2021

Language

EN