@Article{JCM-37-6,
author = {Ryu, K., Ernest and Yin, Wotao},
title = {Proximal-Proximal-Gradient Method},
journal = {Journal of Computational Mathematics},
year = {2019},
volume = {37},
number = {6},
pages = {778--812},
abstract = {<p style="text-align: justify;">In this paper, we present the proximal-proximal-gradient method (PPG), a novel optimization method that is simple to implement and simple to parallelize. PPG generalizes the
proximal-gradient method and ADMM and is applicable to minimization problems written as a sum of many differentiable and many non-differentiable convex functions. The
non-differentiable functions can be coupled. We furthermore present a related stochastic
variation, which we call stochastic PPG (S-PPG). S-PPG can be interpreted as a generalization of Finito and MISO over to the sum of many coupled non-differentiable convex
functions.<br/>We present many applications that can benefit from PPG and S-PPG and prove convergence for both methods. We demonstrate the empirical effectiveness of both methods
through experiments on a CUDA GPU. A key strength of PPG and S-PPG is, compared
to existing methods, their ability to directly handle a large sum of non-differentiable non-separable functions with a constant step size independent of the number of functions. Such
non-diminishing step size allows them to be fast.</p>},
issn = {1991-7139},
doi = {https://doi.org/10.4208/jcm.1906-m2018-0282},
url = {https://global-sci.com/article/84447/proximal-proximal-gradient-method}
}