Randomized dual proximal gradient for large-scale distributed optimization
Abstract
In this paper we consider distributed optimization problems in which the cost function is separable (i.e., a sum of possibly non-smooth functions all sharing a common variable) and can be split into a strongly convex term and a convex one. The second term is typically used to encode constraints or to regularize the solution. We propose an asynchronous, distributed optimization algorithm over an undirected topology, based on a proximal gradient update on the dual problem. We show that by means of a proper choice of primal variables, the dual problem is separable and the dual variables can be stacked into separate blocks. This allows us to show that a distributed gossip update can be obtained by means of a randomized block-coordinate proximal gradient on the dual function.
Cite
@article{arxiv.1609.05713,
title = {Randomized dual proximal gradient for large-scale distributed optimization},
author = {Ivano Notarnicola and Giuseppe Notarstefano},
journal= {arXiv preprint arXiv:1609.05713},
year = {2016}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1509.08373