@inproceedings{7ff6229980604786bc714e6366bc45ba,
title = "A projection-free decentralized algorithm for non-convex optimization",
abstract = "This paper considers a decentralized projection free algorithm for non-convex optimization in high dimension. More specifically, we propose a Decentralized Frank-Wolfe (DeFW) algorithm which is suitable when high dimensional optimization constraints are difficult to handle by conventional projection/proximal-based gradient descent methods. We present conditions under which the DeFW algorithm converges to a stationary point and prove that the rate of convergence is as fast as O(l/√T), where T is the iteration number. This paper provides the first convergence guarantee for FrankWolfe methods applied to non-convex decentralized optimization. Utilizing our theoretical findings, we formulate a novel robust matrix completion problem and apply DeFW to give an efficient decentralized solution. Numerical experiments are performed on realistic and synthetic data to support our findings.",
keywords = "Decentralized algorithms, Frank-Wolfe method, Gossip algorithms, Matrix completion, Non-convex optimization",
author = "Wai, \{Hoi To\} and Anna Scaglione and Jean Lafond and Eric Moulines",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 ; Conference date: 07-12-2016 Through 09-12-2016",
year = "2017",
month = apr,
day = "19",
doi = "10.1109/GlobalSIP.2016.7905887",
language = "English",
series = "2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "475--479",
booktitle = "2016 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2016 - Proceedings",
}