Adaptive Uncertainty-Weighted ADMM for Distributed Optimization

Autor: Ye, Jianping, Wan, Caleb, Fung, Samy Wu
Rok vydání: 2021
Předmět:
Druh dokumentu: Working Paper
Popis: 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.
Comment: 16 pages, 10 figures
Databáze: arXiv