Fabio Fagnani
Polytechnic University of Turin
205 Papers
1.3K Citations
Fabio Fagnani is an academic researcher from Polytechnic University of Turin. The author has contributed to research in topics: Computer science & Linear system. The author has an hindex of 29, co-authored 187 publications. Previous affiliations of Fabio Fagnani include Instituto Politécnico Nacional & University of Padua.
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Papers
Feedback Control Under Data Rate Constraints: An Overview
Girish N. Nair,Fabio Fagnani,Sandro Zampieri,Robin J. Evans +3 more
- 05 Mar 2007
TL;DR: In this article, the authors review the results available in the literature on data-rate-limited control for linear systems and show how fundamental tradeoffs between the data rate and control goals, such as stability, mean entry times, and asymptotic state norms, emerge naturally.
1.1K
Randomized consensus algorithms over large scale networks
Fabio Fagnani,Sandro Zampieri +1 more
TL;DR: This presentation will focus on random algorithms, reviewing some algorithms present in the literature and proposing some new ones, and establishing some probabilistic concentration results which will give a stronger significance to previous results.
441
Opinion Fluctuations and Disagreement in Social Networks
TL;DR: In large-scale societies, which are highly fluid, the product of the mixing time of the Markov chain on the graph describing the social network and the relative size of the linkages to stubborn agents vanishes as the population size grows large, a condition of homogeneous influence emerges.
432
Communication constraints in the average consensus problem
TL;DR: It is shown that time-invariant communication networks with circulant symmetries yield slow convergence if the amount of information exchanged by the agents does not scale well with their number, and that randomly time-varying communication networks allow very fast convergence rates.
334
Opinion fluctuations and disagreement in social networks
TL;DR: In this article, a tractable opinion dynamics model that generates long-run disagreements and persistent opinion fluctuations is proposed. But the model is not suitable for large-scale social networks.
287