Chiara Ravazzi

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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)
The &#x2113;<sub>0</sub>/&#x2113;<sub>1</sub>-regularized least-squares approach is used to deal with linear inverse problems under sparsity constraints, which arise in mathematical and engineering fields. In particular, multiagent models have recently emerged in this context to describe diverse kinds of networked systems, ranging from medical databases to(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 “gossiping”. Second, agents are not completely open-minded, but instead take into account their initial(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)
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 networked(More)
Distributed optimization in multi-agent systems under sparsity constraints has recently received a lot of attention. In this paper, we consider the in-network minimization of a continuously differentiable nonlinear function which is a combination of local agent objective functions subject to sparsity constraints on the variables. A crucial issue of(More)
In this paper, we propose a new class of iteratively re-weighted least squares (IRLS) for sparse recovery problems. The proposed methods are inspired by constrained maximum-likelihood estimation under a Gaussian scale mixture (GSM) distribution assumption. In the noise-free setting, we provide sufficient conditions ensuring the convergence of the sequences(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)
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)
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)