AISTATS 2017poster141 citations
Adaptive ADMM with Spectral Penalty Parameter Selection
Zheng Xu, Mario Figueiredo, Tom Goldstein
Abstract
The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems. However, its performance is highly sensitive to a penalty parameter, making ADMM often unreliable and hard to automate for a non-expert user. We tackle this weakness of ADMM by proposing a method that adaptively tunes the penalty parameter to achieve fast convergence. The resulting adaptive ADMM (AADMM) algorithm, inspired by the successful Barzilai-Borwein spectral method for gradient descent, yields fast convergence and relative insensitivity to the initial stepsize and problem scaling.
BibTeX
@InProceedings{pmlr-v54-xu17a,
title = {{Adaptive ADMM with Spectral Penalty Parameter Selection}},
author = {Xu, Zheng and Figueiredo, Mario and Goldstein, Tom},
booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
pages = {718--727},
year = {2017},
editor = {Singh, Aarti and Zhu, Jerry},
volume = {54},
series = {Proceedings of Machine Learning Research},
month = {20--22 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v54/xu17a/xu17a.pdf},
url = {https://proceedings.mlr.press/v54/xu17a.html},
abstract = {The alternating direction method of multipliers (ADMM) is a versatile tool for solving a wide range of constrained optimization problems. However, its performance is highly sensitive to a penalty parameter, making ADMM often unreliable and hard to automate for a non-expert user. We tackle this weakness of ADMM by proposing a method that adaptively tunes the penalty parameter to achieve fast convergence. The resulting adaptive ADMM (AADMM) algorithm, inspired by the successful Barzilai-Borwein spectral method for gradient descent, yields fast convergence and relative insensitivity to the initial stepsize and problem scaling.}
}