A unified algorithmic approach to distributed optimization

João F. C. Mota; João M. F. Xavier; Pedro M. Q. Aguiar; Markus Püschel

doi:10.1109/GlobalSIP.2013.6736951

A unified algorithmic approach to distributed optimization

João F. C. Mota, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

We address general optimization problems formulated on networks. Each node in the network has a function, and the goal is to find a vector x ⁿ that minimizes the sum of all the functions. We assume that each function depends on a set of components of x, not necessarily on all of them. This creates additional structure in the problem, which can be captured by the classification scheme we develop. This scheme not only to enables us to design an algorithm that solves very general distributed optimization problems, but also allows us to categorize prior algorithms and applications. Our general-purpose algorithm shows a performance superior to prior algorithms, including algorithms that are application-specific.

Original language	English
Title of host publication	2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings
Pages	607-610
Number of pages	4
ISBN (Electronic)	9781479902484
DOIs	https://doi.org/10.1109/GlobalSIP.2013.6736951
Publication status	Published - 2013
Event	1st IEEE Global Conference on Signal and Information Processing 2013 - Austin, United States Duration: 3 Dec 2013 → 5 Dec 2013

Conference

Conference	1st IEEE Global Conference on Signal and Information Processing 2013
Abbreviated title	GlobalSIP 2013
Country/Territory	United States
City	Austin
Period	3/12/13 → 5/12/13

Keywords

Distributed optimization
Sensor networks

ASJC Scopus subject areas

Information Systems
Signal Processing

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10.1109/GlobalSIP.2013.6736951

Cite this

@inproceedings{86160e836c9546d695762d62090bbcc3,

title = "A unified algorithmic approach to distributed optimization",

abstract = "We address general optimization problems formulated on networks. Each node in the network has a function, and the goal is to find a vector x n that minimizes the sum of all the functions. We assume that each function depends on a set of components of x, not necessarily on all of them. This creates additional structure in the problem, which can be captured by the classification scheme we develop. This scheme not only to enables us to design an algorithm that solves very general distributed optimization problems, but also allows us to categorize prior algorithms and applications. Our general-purpose algorithm shows a performance superior to prior algorithms, including algorithms that are application-specific.",

keywords = "Distributed optimization, Sensor networks",

author = "Mota, {Jo{\~a}o F. C.} and Xavier, {Jo{\~a}o M. F.} and Aguiar, {Pedro M. Q.} and Markus P{\"u}schel",

year = "2013",

doi = "10.1109/GlobalSIP.2013.6736951",

language = "English",

pages = "607--610",

booktitle = "2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings",

note = "1st IEEE Global Conference on Signal and Information Processing 2013, GlobalSIP 2013 ; Conference date: 03-12-2013 Through 05-12-2013",

}

Mota, JFC, Xavier, JMF, Aguiar, PMQ & Püschel, M 2013, A unified algorithmic approach to distributed optimization. in 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings., 6736951, pp. 607-610, 1st IEEE Global Conference on Signal and Information Processing 2013, Austin, Texas, United States, 3/12/13. https://doi.org/10.1109/GlobalSIP.2013.6736951

TY - GEN

T1 - A unified algorithmic approach to distributed optimization

AU - Mota, João F. C.

AU - Xavier, João M. F.

AU - Aguiar, Pedro M. Q.

AU - Püschel, Markus

PY - 2013

Y1 - 2013

N2 - We address general optimization problems formulated on networks. Each node in the network has a function, and the goal is to find a vector x n that minimizes the sum of all the functions. We assume that each function depends on a set of components of x, not necessarily on all of them. This creates additional structure in the problem, which can be captured by the classification scheme we develop. This scheme not only to enables us to design an algorithm that solves very general distributed optimization problems, but also allows us to categorize prior algorithms and applications. Our general-purpose algorithm shows a performance superior to prior algorithms, including algorithms that are application-specific.

AB - We address general optimization problems formulated on networks. Each node in the network has a function, and the goal is to find a vector x n that minimizes the sum of all the functions. We assume that each function depends on a set of components of x, not necessarily on all of them. This creates additional structure in the problem, which can be captured by the classification scheme we develop. This scheme not only to enables us to design an algorithm that solves very general distributed optimization problems, but also allows us to categorize prior algorithms and applications. Our general-purpose algorithm shows a performance superior to prior algorithms, including algorithms that are application-specific.

KW - Distributed optimization

KW - Sensor networks

UR - http://www.scopus.com/inward/record.url?scp=84897712489&partnerID=8YFLogxK

U2 - 10.1109/GlobalSIP.2013.6736951

DO - 10.1109/GlobalSIP.2013.6736951

M3 - Conference contribution

AN - SCOPUS:84897712489

SP - 607

EP - 610

BT - 2013 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013 - Proceedings

T2 - 1st IEEE Global Conference on Signal and Information Processing 2013

Y2 - 3 December 2013 through 5 December 2013

ER -

A unified algorithmic approach to distributed optimization

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