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pro vyhledávání: '"Tempo, Roberto"'
Autor:
Proskurnikov, Anton, Tempo, Roberto
Recent years have witnessed a significant trend towards filling the gap between Social Network Analysis (SNA) and control theory. This trend was enabled by the introduction of new mathematical models describing dynamics of social groups, the developm
Externí odkaz:
http://arxiv.org/abs/1801.06719
We consider the problem of multi-agent consensus where some agents are subject to faults/attacks and might make updates arbitrarily. The network consists of agents taking integer-valued (i.e., quantized) states under directed communication links. The
Externí odkaz:
http://arxiv.org/abs/1710.06994
Investigation of social influence dynamics requires mathematical models that are "simple" enough to admit rigorous analysis, and yet sufficiently "rich" to capture salient features of social groups. Thus, the mechanism of iterative opinion pooling fr
Externí odkaz:
http://arxiv.org/abs/1704.06900
Autor:
Proskurnikov, Anton V., Tempo, Roberto
Publikováno v:
Annual Reviews in Control, 2017, vol. 43, pp. 65-79
In recent years, we have observed a significant trend towards filling the gap between social network analysis and control. This trend was enabled by the introduction of new mathematical models describing dynamics of social groups, the advancement in
Externí odkaz:
http://arxiv.org/abs/1701.06307
This paper considers a distributed convex optimization problem with inequality constraints over time-varying unbalanced digraphs, where the cost function is a sum of local objectives, and each node of the graph only knows its local objective and ineq
Externí odkaz:
http://arxiv.org/abs/1612.09029
Autor:
Chamanbaz, Mohammadreza, Dabbene, Fabrizio, Peaucelle, Dimitri, Pittet, Christelle, Tempo, Roberto
R-RoMulOC is a freely distributed toolbox which aims at making easily available to the users different optimization-based methods for dealing with uncertain systems. It implements both deterministic LMI-based results, that provide guaranteed performa
Externí odkaz:
http://arxiv.org/abs/1612.07101
In this paper, probabilistic guarantees for constraint sampling of multistage robust convex optimization problems are derived. The dynamic nature of these problems is tackled via the so-called scenario-with-certificates approach. This allows to avoid
Externí odkaz:
http://arxiv.org/abs/1611.00980
This paper proposes distributed algorithms to solve robust convex optimization (RCO) when the constraints are affected by nonlinear uncertainty. We adopt a scenario approach by randomly sampling the uncertainty set. To facilitate the computational ta
Externí odkaz:
http://arxiv.org/abs/1607.05507
For discrete-time linear systems subject to parametric uncertainty described by random variables, we develop a sampling-based Stochastic Model Predictive Control algorithm. Unlike earlier results employing a scenario approximation, we propose an offl
Externí odkaz:
http://arxiv.org/abs/1606.06056