93 citations · 375 across the 30 of their papers we have counts for
15 papers · 1 filter
Multi-Agent Optimization and Learning: A Non-Expansive Operators Perspective
Nicola Bastianello, Luca Schenato, Ruggero Carli
Multi-agent systems are increasingly widespread in a range of application domains, with optimization and learning underpinning many of the tasks that arise in this context. Differe…
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus
Alessio Maritan, Ganesh Sharma, Luca Schenato +1
This paper considers the problem of distributed multi-agent learning, where the global aim is to minimize a sum of local objective (empirical loss) functions through local optimiza…
ZO-JADE: Zeroth-order Curvature-Aware Multi-Agent Convex Optimization
Alessio Maritan, Luca Schenato
In this work we address the problem of convex optimization in a multi-agent setting where the objective is to minimize the mean of local cost functions whose derivatives are not av…
Can Decentralized Control Outperform Centralized? The Role of Communication Latency
Luca Ballotta, Mihailo R. Jovanović, Luca Schenato
In this paper, we examine the influence of communication latency on performance of networked control systems. Even though distributed control architectures offer advantages in term…
From Sensor to Processing Networks: Optimal Estimation with Computation and Communication Latency
Luca Ballotta, Luca Schenato, Luca Carlone
This paper investigates the use of a networked system (, swarm of robots, smart grid, sensor network) to monitor a time-varying phenomenon of interest in the presence of comm…
Smart Grid State Estimation with PMUs Time Synchronization Errors
Marco Todescato, Ruggero Carli, Luca Schenato +1
We consider the problem of PMU-based state estimation combining information coming from ubiquitous power demand time series and only a limited number of PMUs. Conversely to recent…
Computation-Communication Trade-offs and Sensor Selection in Real-time Estimation for Processing Networks
Luca Ballotta, Luca Schenato, Luca Carlone
Recent advances in electronics are enabling substantial processing to be performed at each node (robots, sensors) of a networked system. Local processing enables data compression a…
Asynchronous Distributed Optimization over Lossy Networks via Relaxed ADMM: Stability and Linear Convergence
Nicola Bastianello, Ruggero Carli, Luca Schenato +1
In this work we focus on the problem of minimizing the sum of convex cost functions in a distributed fashion over a peer-to-peer network. In particular, we are interested in the ca…
A Partition-Based Implementation of the Relaxed ADMM for Distributed Convex Optimization over Lossy Networks
Nicola Bastianello, Marco Todescato, Ruggero Carli +1
In this paper we propose a distributed implementation of the relaxed Alternating Direction Method of Multipliers algorithm (R-ADMM) for optimization of a separable convex cost func…
Distributed Optimization over Lossy Networks via Relaxed Peaceman-Rachford Splitting: a Robust ADMM Approach
Nicola Bastianello, Marco Todescato, Ruggero Carli +1
In this work we address the problem of distributed optimization of the sum of convex cost functions in the context of multi-agent systems over lossy communication networks. Buildin…
Lyapunov Theory for Discrete Time Systems
Nicoletta Bof, Ruggero Carli, Luca Schenato
In this work, we present the equivalent of many theorems available for continuous time systems. In particular, the theory is applied to Averaging Theory and Separation of time scal…
Generalized gradient optimization over lossy networks for partition-based estimation
Marco Todescato, Nicoletta Bof, Guido Cavraro +2
We address the problem of distributed convex unconstrained optimization over networks characterized by asynchronous and possibly lossy communications. We analyze the case where the…
Newton-Raphson Consensus under asynchronous and lossy communications for peer-to-peer networks
Nicoletta Bof, Ruggero Carli, Giuseppe Notarstefano +2
In this work we study the problem of unconstrained convex-optimization in a fully distributed multi-agent setting which includes asynchronous computation and lossy communication. I…
Analysis of Newton-Raphson Consensus for multi-agent convex optimization under asynchronous and lossy communications
Ruggero Carli, Giuseppe Notarstefano, Luca Schenato +1
We extend a multi-agent convex-optimization algorithm named Newton-Raphson consensus to a network scenario that involves directed, asynchronous and lossy communications. We theoret…
Newton-Raphson Consensus for Distributed Convex Optimization
Damiano Varagnolo, Filippo Zanella, Angelo Cenedese +2
We address the problem of distributed uncon- strained convex optimization under separability assumptions, i.e., the framework where each agent of a network is endowed with a local…