@inproceedings{8e3a0f4b4d4640a0b69e3a8818753a0f,
title = "BFGS-ADMM for Large-Scale Distributed Optimization",
abstract = "We consider a class of distributed optimization problem where the objective function consists of a sum of strongly convex and smooth functions and a (possibly nons-mooth) convex regularizer. A multi-agent network is assumed, where each agent holds a private cost function and cooperates with its neighbors to compute the optimum of the aggregate objective. We propose a quasi-Newton Alternating Direction Method of Multipliers (ADMM) where the primal update is solved inexactly with approximated curvature information. By introducing an intermediate consensus variable, we achieve a block diagonal Hessian which eliminates the need for inner communication loops within the network when computing the update direction. We establish global linear convergence to the optimal primal-dual solution without the need for backtracking line search, under the assumption that component cost functions are strongly convex with Lipschitz continuous gradients. Numerical simulations on real datasets demonstrate the advantages of the proposed method over state of the art.",
author = "Yichuan Li and Yonghai Gong and Freris, {Nikolaos M.} and Petros Voulgaris and Dusan Stipanovic",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 60th IEEE Conference on Decision and Control, CDC 2021 ; Conference date: 13-12-2021 Through 17-12-2021",
year = "2021",
doi = "10.1109/CDC45484.2021.9683678",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1689--1694",
booktitle = "60th IEEE Conference on Decision and Control, CDC 2021",
address = "United States",
}