Journal of Applied and Numerical Optimization

Full Text: PDF
DOI: 10.23952/jano.4.2022.2.10
Volume 4, Issue 2, 1 August 2022, Pages 273-290 

 

Abstract. 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.

 

How to Cite this Article:
J. Ye, C. Wan, S. Wu Fung, Adaptive uncertainty-weighted ADMM for distributed optimization, J. Appl. Numer. Optim. 4 (2022), 273-290.