In-Network Linear Regression with Arbitrarily Split Data Matrices

Autor:	Côté, François D., Psaromiligkos, Ioannis N., Gross, Warren J.
Rok vydání:	2014
Předmět:	Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	In this paper, we address the problem of how a network of agents can collaboratively fit a linear model when each agent only ever has an arbitrary summand of the regression data. This problem generalizes previously studied data-matrix-splitting scenarios, allowing for some agents to have more measurements of some features than of others and even have measurements that other agents have. We present a variable-centric framework for distributed optimization in a network, and use this framework to develop a proximal algorithm, based on the Douglas-Rachford method, that solves the problem. Comment: 3 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1408.1073 Zobrazit plný text záznamu View this record from Arxiv