Convergence rate of a distributed algorithm for matrix scaling to doubly stochastic form

Alejandro D. Dominguez-Garcia, Christoforos N. Hadjicostis

Research output: Contribution to journalConference article

Abstract

Motivated by matrix scaling applications and, more recently, distributed averaging previous work has considered settings where the interconnections between components in a distributed system are captured by a strongly connected directed graph (digraph) and each component aims to assign assigning weights on its outgoing edges (based on the weights on its incoming edges) so that the corresponding set of weights forms a doubly stochastic matrix. In particular, it has been shown that the system components can obtain a set of weights that form a doubly stochastic matrix via a variety of distributed algorithms. In this paper, we establish that the convergence rate of one such distributed algorithm is linear with rate between zero and one.

Original languageEnglish (US)
Article number7039890
Pages (from-to)3240-3245
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume2015-February
Issue numberFebruary
DOIs
StatePublished - Jan 1 2014
Event2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States
Duration: Dec 15 2014Dec 17 2014

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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