@inproceedings{135e9a4021534ea3bb4538f45dfe118f,
title = "D-SOP: Distributed Second Order Proximal Method for Convex Composite Optimization",
abstract = "This paper investigates a class of distributed optimization problems where the objective function is given by the sum of twice differentiable convex functions and a convex non-differentiable part. The setting assumes a network of communicating agents in which each individual agent's objective is captured by a summand of the aggregate objective function, and agents cooperate through an information exchange with their neighbors. We devise a second order method by transforming the problem into a continuously differentiable form using proximal operators, and truncating the Taylor expansion of the Hessian inverse so that a distributed implementation of the algorithm is possible. We prove global linear convergence (without backtracking), under usual strong convexity assumptions, and further demonstrate the effectiveness of our scheme through numerical simulations.",
author = "Yichuan Li and Freris, {Nikolaos M.} and Petros Voulgaris and Dusan Stipanovic",
note = "*This work was supported by the Ministry of Science and Technology of China under grant 2019YFB2102200, the Anhui Dept. of Science and Technology under grant 201903a05020049, and NSF under grants CCF-1717207 and CCF-1717154.; 2020 American Control Conference, ACC 2020 ; Conference date: 01-07-2020 Through 03-07-2020",
year = "2020",
month = jul,
doi = "10.23919/ACC45564.2020.9147777",
language = "English (US)",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2844--2849",
booktitle = "2020 American Control Conference, ACC 2020",
address = "United States",
}