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

Alejandro D. Dominguez-Garcia; Christoforos N. Hadjicostis

doi:10.1109/CDC.2014.7039890

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

Alejandro D. Dominguez-Garcia, Christoforos N. Hadjicostis

Research output: Contribution to journal › Conference article › peer-review

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 language	English (US)
Article number	7039890
Pages (from-to)	3240-3245
Number of pages	6
Journal	Proceedings of the IEEE Conference on Decision and Control
Volume	2015-February
Issue number	February
DOIs	https://doi.org/10.1109/CDC.2014.7039890
State	Published - Jan 1 2014
Event	2014 53rd IEEE Annual Conference on Decision and Control, CDC 2014 - Los Angeles, United States Duration: Dec 15 2014 → Dec 17 2014

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

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