Leader Selection for Minimizing Convergence Error in Leader-Follower Systems: A Supermodular Optimization Approach

Abstract : In leader-follower systems, follower nodes receive inputs from a set of leader nodes, exchange information, and update their states according to an iterative algorithm. In such algorithms, the node states may deviate from their desired values before the algorithm converges, leading to disruptions in network performance. In this paper, we study the problem of choosing leader nodes in order to minimize convergence errors. We first develop a connection between a class of weighted averaging algorithms and random walks on graphs, and then show that the convergence error is a supermodular function of the set of leader nodes. Based on the supermodularity of the convergence error, we derive efficient algorithms for selecting leader nodes that are within a provable bound of the optimum. Our approach is demonstrated through a simulation study.
Type de document :
Communication dans un congrès
WiOpt'12: Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, May 2012, Paderborn, Germany. pp.111-115, 2012
Liste complète des métadonnées

Littérature citée [5 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00763391
Contributeur : Service Ist Inria Sophia Antipolis-Méditerranée / I3s <>
Soumis le : lundi 10 décembre 2012 - 16:33:52
Dernière modification le : mercredi 12 décembre 2012 - 10:26:02
Document(s) archivé(s) le : lundi 11 mars 2013 - 12:46:03

Fichier

p111-clark.pdf
Accord explicite pour ce dépôt

Identifiants

  • HAL Id : hal-00763391, version 1

Collections

Citation

Andrew Clark, Linda Bushnell, Radha Poovendran. Leader Selection for Minimizing Convergence Error in Leader-Follower Systems: A Supermodular Optimization Approach. WiOpt'12: Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, May 2012, Paderborn, Germany. pp.111-115, 2012. 〈hal-00763391〉

Partager

Métriques

Consultations de la notice

55

Téléchargements de fichiers

92