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In this tutorial paper, we study three specific applications: opinion formation in social networks, centrality measures in complex networks and estimation problems in large-scale power systems. These applications fall under a general framework which aims at the construction of algorithms for distributed computation over a network. The two key ingredients of(More)
—The 0//1-regularized least squares approach is used to deal with linear inverse problems under sparsity constraints, which arise in mathematical and engineering fields, e.g., statistics , signal processing, machine learning, and coding theory. In particular, multi-agent models have been recently emerged in this context to describe diverse kinds of(More)
In this paper we consider ensembles of codes, denoted RA<sup>m</sup>, obtained by a serial concatenation of a repetition code and m accumulate codes through uniform random inter-leavers. We analyze their average spectrum functions for each m showing that they are equal to 0 below a threshold distance isin<sub>m</sub> and positive beyond it. One of our main(More)
Algorithms and dynamics over networks often involve randomization and randomization can induce oscillating dynamics that fail to converge in a deterministic sense. Under assumptions of independence across time and linearity of the updates, we show that the oscillations are ergodic if the expected dynamics is stable. We apply this result to three problems of(More)
In this paper we study a novel model of opinion dynamics in social networks, which has two main features. First, agents asynchronously interact in pairs, and these pairs are chosen according to a random process. We refer to this communication model as " gos-siping ". Second, agents are not completely open-minded, but instead take into account their initial(More)
— This paper regards the relative localization problem in sensor networks. We propose for its solution a distributed randomized algorithm, which is based on input-driven consensus dynamics and features pairwise " gossip " communications and updates. Due to the randomness of the updates, the state of this algorithm oscillates in time around a certain limit(More)
— This paper regards the relative localization problem in sensor networks. We study a randomized algorithm, which is based on input-driven consensus dynamics and involves pairwise " gossip " communications and updates. Due to the ran-domness of the updates, the state of this algorithm ergodically oscillates around a limit value. Exploiting the ergodicity of(More)
—In this paper, we address the problem of distributed sparse recovery of signals acquired via compressed measurements in a sensor network. We propose a new class of distributed algorithms to solve Lasso regression problems, when the communication to a fusion center is not possible, e.g., due to communication cost or privacy reasons. More precisely, we(More)
— This paper deals with the problem of simultaneously classifying sensors and estimating hidden parameters in a network with communication constraints. In particular, we consider a network where sensors measure a common parameter with different precision rank. The goal of each unit is to estimate the unknown parameter and its own specific type through local(More)
In this paper, we address the problem of simultaneous classification and estimation of hidden parameters in a sensor network with communications constraints. In particular, we consider a network of noisy sensors which measure a common scalar unknown parameter. We assume that a fraction of the nodes represent faulty sensors, whose measurements are poorly(More)