Decentralized consensus optimization and resource allocation

Angelia Nedich, Alexander Olshevsky, Wei Shi

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

We consider the problems of consensus optimization and resource allocation, and we discuss decentralized algorithms for solving such problems. By “decentralized”, we mean the algorithms are to be implemented in a set of networked agents, whereby each agent is able to communicate with its neighboring agents. For both problems, every agent in the network wants to collaboratively minimize a function that involves global information, while having access to only partial information. Specifically, we will first introduce the two problems in the context of distributed optimization, review the related literature, and discuss an interesting “mirror relation” between the problems. Afterwards, we will discuss some of the state-of-the-art algorithms for solving the decentralized consensus optimization problem and, based on the “mirror relationship”, we then develop some algorithms for solving the decentralized resource allocation problem. We also provide some numerical experiments to demonstrate the efficacy of the algorithms and validate the methodology of using the “mirror relation”.

Original languageEnglish (US)
Title of host publicationLecture Notes in Mathematics
PublisherSpringer Verlag
Pages247-287
Number of pages41
DOIs
StatePublished - Jan 1 2018

Publication series

NameLecture Notes in Mathematics
Volume2227
ISSN (Print)0075-8434

    Fingerprint

Keywords

  • Consensus optimization
  • Convex constrained problems
  • Decentralized algorithms
  • Resource allocation

ASJC Scopus subject areas

  • Algebra and Number Theory

Cite this

Nedich, A., Olshevsky, A., & Shi, W. (2018). Decentralized consensus optimization and resource allocation. In Lecture Notes in Mathematics (pp. 247-287). (Lecture Notes in Mathematics; Vol. 2227). Springer Verlag. https://doi.org/10.1007/978-3-319-97478-1_10