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.

Browse

Filters

2010 - 20125

Settings

Start date: 2010Subject: search.filters.subject.consensus

Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    A performance comparison between two consensus-based distributed optimization algorithms
    (2012-05-04) Matei, Ion; Baras, John
    In this paper we address the problem of multi-agent optimization for convex functions expressible as sums of convex functions. Each agent has access to only one function in the sum and can use only local information to update its current estimate of the optimal solution. We consider two consensus-based iterative algorithms, based on a combination between a consensus step and a subgradient decent update. The main difference between the two algorithms is the order in which the consensus-step and the subgradient descent update are performed. We show that updating first the current estimate in the direction of a subgradient and then executing the consensus step ensures better performance than executing the steps in reversed order. In support of our analytical results, we give some numerical simulations of the algorithms as well.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Consensus-Based Distributed Filtering
    (2010-03) Matei, Ion; Baras, John; Baras, John
    We address the consensus-based distributed linear filtering problem, where a discrete time, linear stochastic process is observed by a network of sensors. We assume that the consensus weights are known and we first provide sufficient conditions under which the stochastic process is detectable, i.e. for a specific choice of consensus weights there exists a set of filtering gains such that the dynamics of the estimation errors (without noise) is asymptotically stable. Next, we provide a distributed sub-optimal filtering scheme based on optimizing an upper bound on a quadratic filtering cost. In the stationary case, we provide sufficient conditions under which this scheme converges; conditions expressed in terms of the convergence properties of a set of coupled Riccati equations. We continue with presenting a connection between the consensus-based distributed linear filter and the optimal linear filter of a Markovian jump linear system, appropriately defined. More specifically, we show that if the Markovian jump linear system is (mean square) detectable, then the stochastic process is detectable under the consensus-based distributed linear filtering scheme. We also show that the state estimate given by the optimal linear filter of a Markovian jump linear system appropriately defined can be seen as an approximation of the optimal average state estimate obtained using the consensus-based linear filtering scheme.
  • Thumbnail Image
    Item
    The asymptotic consensus problem on convex metric spaces
    (2010-03) Matei, Ion; Baras, John; Baras, John
    A 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.