Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Somarakis, ChristoforosSubject: search.filters.subject.consensus

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    Linear Processes Under Vanishing Communications - The Consensus Problem
    (2012-04-07) Somarakis, Christoforos
    In this report, we revisit the classical multi-agent distributed consensus problem under the dropping of the general assumption that the existence of a connection between agent implies weights uniformly bounded away from zero. We reformulate and study the problem by establishing global convergence results both in discrete and continuous time, under fixed, switching and random topologies.
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    A randomized gossip consensus algorithm on convex metric spaces
    (2012-02-20) Matei, Ion; Somarakis, Christoforos; Baras, John
    A consensus problem consists of a group of dynamic agents who seek to agree upon certain quantities of interest. This problem can be generalized in the context of convex metric spaces that extend the standard notion of convexity. In this paper we introduce and analyze a randomized gossip algorithm for solving the generalized consensus problem on convex metric spaces. We study the convergence properties of the algorithm using stochastic differential equations theory. We show that the dynamics of the distances between the states of the agents can be upper bounded by the dynamics of a stochastic differential equation driven by Poisson counters. In addition, we introduce instances of the generalized consensus algorithm for several examples of convex metric spaces together with numerical simulations.