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The asymptotic consensus problem on convex metric spaces

dc.contributor.advisorBaras, John
dc.contributor.authorMatei, Ion
dc.contributor.authorBaras, John
dc.date.accessioned2010-03-10T16:08:45Z
dc.date.available2010-03-10T16:08:45Z
dc.date.issued2010-03
dc.identifier.urihttp://hdl.handle.net/1903/10045
dc.description.abstractA consensus problem consists of a group of dynamic agents who seek to agree upon certain quantities of interest. The agents exchange information according to a communication network modeled as a directed time-varying graph. A convex metric space is a metric space on which we define a convex structure. Using this convex structure we define convex sets and in particular the convex hull of a (finite) set. In this paper we generalize the asymptotic consensus problem to convex metric spaces. Under minimal connectivity assumptions, we show that if at each iteration an agent updates its state by choosing a point from a particular subset of the convex hull of the agent's current state and the ones of his neighbors, the asymptotic agreement is achieved. As application example, we use this framework to introduce an iterative algorithm for reaching consensus of opinion. In this example, the agents take values in the space of discrete random variable on which we define an appropriate metric and convex structure. In addition, we provide a more detail analysis of the convex hull of a finite set for this particular convex metric space.en_US
dc.description.sponsorshipThis material is base upon the work supported by the US Air Force Office of Scientific Research award FA9550-09-0538en_US
dc.language.isoenen_US
dc.relation.ispartofseriesTR_2010-4;
dc.subjectconsensusen_US
dc.subjectconvexen_US
dc.subjectmetric spaceen_US
dc.titleThe asymptotic consensus problem on convex metric spacesen_US
dc.typeArticleen_US
dc.relation.isAvailableAtInstitute for Systems Researchen_us
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_us
dc.relation.isAvailableAtUniversity of Maryland (College Park, MD)en_us


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