27 citations · 54 across the 22 of their papers we have counts for
17 papers · 1 filter
Timescale Separation Through the Lens of Operator Theory
Guido Carnevale, Nicola Bastianello, Luca Schenato +2
Timescale separation is a powerful tool for analyzing interconnected dynamical systems. Meanwhile, operator theory provides a general framework for studying the convergence of iter…
Suboptimal and Reduced-Order MPC via Timescale Separation
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
In this paper, we propose a generalized framework for the design and analysis of suboptimal and reduced-order nonlinear Model Predictive Control (MPC) architectures. The proposed f…
A Distributed Bilevel Framework for the Macroscopic Optimization of Multi-Agent Systems
Riccardo Brumali, Guido Carnevale, Sonia Martínez +1
In this paper, we propose a novel distributed algorithm to optimize the emergent macroscopic behavior of large-scale multi-agent systems via microscopic actions. We cast this task…
Safe Control of Feedback-Interconnected Systems via Singular Perturbations
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
Control Barrier Functions (CBFs) have emerged as a powerful tool in the design of safety-critical controllers for nonlinear systems. In modern applications, complex systems often i…
Accelerated ADMM: Automated Parameter Tuning and Improved Linear Convergence
Meisam Tavakoli, Fabian Jakob, Guido Carnevale +2
This work studies the linear convergence of an accelerated scheme of the Alternating Direction Method of Multipliers (ADMM) for strongly convex and Lipschitz-smooth problems. We us…
Nonlinear MPC for Feedback-Interconnected Systems: a Suboptimal and Reduced-Order Model Approach
Stefano Di Gregorio, Guido Carnevale, Giuseppe Notarstefano
In this paper, we propose a suboptimal and reduced-order Model Predictive Control (MPC) architecture for discrete-time feedback-interconnected systems. The numerical MPC solver: (i…
Modular Distributed Nonconvex Learning with Error Feedback
Guido Carnevale, Nicola Bastianello
In this paper, we design a novel distributed learning algorithm using stochastic compressed communications. In detail, we pursue a modular approach, merging ADMM and a gradient-bas…
Data-Driven Distributed Optimization via Aggregative Tracking and Deep-Learning
Riccardo Brumali, Guido Carnevale, Giuseppe Notarstefano
In this paper, we propose a novel distributed data-driven optimization scheme. In detail, we focus on the so-called aggregative framework, a scenario in which a set of agents aim t…
Distributed Constraint-Coupled Optimization: Harnessing ADMM-consensus for robustness
Mohamed Abdelmouamin Messilem, Guido Carnevale, Ruggero Carli
In this paper, we consider a network of agents that jointly aim to minimise the sum of local functions subject to coupling constraints involving all local variables. To solve this…
Data-Driven LQR with Finite-Time Experiments via Extremum-Seeking Policy Iteration
Guido Carnevale, Nicola Mimmo, Giuseppe Notarstefano
In this paper, we address Linear Quadratic Regulator (LQR) problems through a novel iterative algorithm named EXtremum-seeking Policy iteration LQR (EXP-LQR). The peculiarity of EX…
A Unifying System Theory Framework for Distributed Optimization and Games
Guido Carnevale, Nicola Mimmo, Giuseppe Notarstefano
This paper introduces a systematic methodological framework to design and analyze distributed algorithms for optimization and games over networks. Starting from a centralized metho…
ADMM-Tracking Gradient for Distributed Optimization over Asynchronous and Unreliable Networks
Guido Carnevale, Nicola Bastianello, Giuseppe Notarstefano +1
In this paper, we propose a novel distributed algorithm for consensus optimization over networks and a robust extension tailored to deal with asynchronous agents and packet losses.…
Nonconvex Distributed Feedback Optimization for Aggregative Cooperative Robotics
Guido Carnevale, Nicola Mimmo, Giuseppe Notarstefano
Distributed aggregative optimization is a recently emerged framework in which the agents of a network want to minimize the sum of local objective functions, each one depending on t…
Triggered Gradient Tracking for Asynchronous Distributed Optimization
Guido Carnevale, Ivano Notarnicola, Lorenzo Marconi +1
This paper proposes Asynchronous Triggered Gradient Tracking, i.e., a distributed optimization algorithm to solve consensus optimization over networks with asynchronous communicati…
Extremum Seeking Tracking for Derivative-free Distributed Optimization
Nicola Mimmo, Guido Carnevale, Andrea Testa +1
In this paper, we deal with a network of agents that want to cooperatively minimize the sum of local cost functions depending on a common decision variable. We consider the challen…
Distributed Online Aggregative Optimization for Dynamic Multi-robot Coordination
Guido Carnevale, Andrea Camisa, Giuseppe Notarstefano
This paper focuses on an online version of the emerging distributed constrained aggregative optimization framework, which is particularly suited for applications arising in coopera…
GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization
Guido Carnevale, Francesco Farina, Ivano Notarnicola +1
This paper deals with a network of computing agents aiming to solve an online optimization problem in a distributed fashion, i.e., by means of local computation and communication,…