93 citations · 375 across the 30 of their papers we have counts for
32 papers
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…
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization
Nicolò Dal Fabbro, Michele Rossi, Luca Schenato +1
Edge networks call for communication efficient (low overhead) and robust distributed optimization (DO) algorithms. These are, in fact, desirable qualities for DO frameworks, such a…
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 Competition Outperform Collaboration? The Role of Misbehaving Agents
Luca Ballotta, Giacomo Como, Jeff S. Shamma +1
We investigate a novel approach to resilient distributed optimization with quadratic costs in a multi-agent system prone to unexpected events that make some agents misbehave. In co…
Self-triggered MPC robust to bounded packet loss via a min-max approach: extended version
Stefan Wildhagen, Matthias Pezzutto, Luca Schenato +1
Networked Control Systems typically come with a limited communication bandwidth and thus require special care when designing the underlying control and triggering law. A method tha…
Competition-Based Resilience in Distributed Quadratic Optimization
Luca Ballotta, Giacomo Como, Jeff S. Shamma +1
This paper proposes a novel approach to resilient distributed optimization with quadratic costs in a networked control system (e.g., wireless sensor network, power grid, robotic te…
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing
Nicolò Dal Fabbro, Subhrakanti Dey, Michele Rossi +1
There is a growing interest in the distributed optimization framework that goes under the name of Federated Learning (FL). In particular, much attention is being turned to FL scena…
Control of over-redundant cooperative manipulation via sampled communication
Enrica Rossi, Marco Tognon, Ruggero Carli +2
In this work we consider the problem of mobile robots that need to manipulate/transport an object via cables or robotic arms. We consider the scenario where the number of manipulat…
Coordinated Multi-Robot Trajectory Tracking Control over Sampled Communication
Enrica Rossi, Marco Tognon, Luca Ballotta +4
In this paper, we propose an inverse-kinematics controller for a class of multi-robot systems in the scenario of sampled communication. The goal is to make a group of robots perfor…
EASE: Energy-Aware Job Scheduling for Vehicular Edge Networks With Renewable Energy Resources
Giovanni Perin, Francesca Meneghello, Ruggero Carli +2
The energy sustainability of multi-access edge computing (MEC) platforms is here addressed by developing Energy-Aware job Scheduling at the Edge (EASE), a computing resource schedu…
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…
Transmission Power Allocation for Remote Estimation with Multi-packet Reception Capabilities
Matthias Pezzutto, Luca Schenato, Subhrakanti Dey
In this paper we consider the problem of transmission power allocation for remote estimation of a dynamical system in the case where the estimator is able to simultaneously receive…
Optimal Network Topology of Multi-Agent Systems subject to Computation and Communication Latency (with proofs)
Luca Ballotta, Mihailo R. Jovanović, Luca Schenato
We study minimum-variance feedback-control design for a networked control system with retarded dynamics, where inter-agent communication is subject to latency. We prove that such a…
Accelerated Probabilistic State Estimation in Distribution Grids via Model Order Reduction
Samuel Chevalier, Luca Schenato, Luca Daniel
This paper applies a custom model order reduction technique to the distribution grid state estimation problem. Specifically, the method targets the situation where, due to pseudo-m…
Accelerated Probabilistic Power Flow in Electrical Distribution Networks via Model Order Reduction and Neumann Series Expansion
Samuel Chevalier, Luca Schenato, Luca Daniel
This paper develops a computationally efficient algorithm which speeds up the probabilistic power flow (PPF) problem by exploiting the inherently low-rank nature of the voltage pro…
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…