A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization
Abstract
We introduce the primal-dual quasi-Newton (PD-QN) method as an approximated second order method for solving decentralized optimization problems. The PD-QN method performs quasi-Newton updates on both the primal and dual variables of the consensus optimization problem to find the optimal point of the augmented Lagrangian. By optimizing the augmented Lagrangian, the PD-QN method is able to find the exact solution to the consensus problem with a linear rate of convergence. We derive fully decentralized quasi-Newton updates that approximate second order information to reduce the computational burden relative to dual methods and to make the method more robust in ill-conditioned problems relative to first order methods. The linear convergence rate of PD-QN is established formally and strong performance advantages relative to existing dual and primal-dual methods are shown numerically.
Cite
@article{arxiv.1809.01212,
title = {A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization},
author = {Mark Eisen and Aryan Mokhtari and Alejandro Ribeiro},
journal= {arXiv preprint arXiv:1809.01212},
year = {2020}
}