9 citations · 22 across the 11 of their papers we have counts for
13 papers
Scenario theory for multi-criteria data-driven decision making
Simone Garatti, Lucrezia Manieri, Alessandro Falsone +3
The scenario approach provides a powerful data-driven framework for designing solutions under uncertainty with rigorous probabilistic robustness guarantees. Existing theory, howeve…
Robustness certificates in data-driven non-convex optimization with additively-uncertain constraints
Alexander J Gallo, Massimiliano Zoggia, Alessandro Falsone +2
We consider decision-making problems that are formulated as non-convex optimization programs where uncertainty enters the constraints through an additive term, independent of the d…
DualBi: A dual bisection algorithm for non-convex problems with a scalar complicating constraint
Lucrezia Manieri, Alessandro Falsone, Maria Prandini
This paper addresses non-convex constrained optimization problems that are characterized by a scalar complicating constraint. We propose an iterative bisection method for the dual…
Vehicle-to-Grid and ancillary services:a profitability analysis under uncertainty
Federico Bianchi, Alessandro Falsone, Riccardo Vignali
The rapid and massive diffusion of electric vehicles poses new challenges to the electric system, which must be able to supply these new loads, but at the same time opens up new op…
On the sensitivity of linear resource sharing problems to the arrival of new agents
Alessandro Falsone, Kostas Margellos, Jacopo Zizzo +2
We consider a multi-agent optimal resource sharing problem that is represented by a linear program. The amount of resource to be shared is fixed, and agents belong to a population…
Tracking-ADMM for Distributed Constraint-Coupled Optimization
Alessandro Falsone, Ivano Notarnicola, Giuseppe Notarstefano +1
We consider constraint-coupled optimization problems in which agents of a network aim to cooperatively minimize the sum of local objective functions subject to individual constrain…
A compositional modeling framework for the optimal energy management of a district network
Daniele Ioli, Alessandro Falsone, Alessandro Vittorio Papadopoulos +1
This paper proposes a compositional modeling framework for the optimal energy management of a district network. The focus is on cooling of buildings, which can possibly share resou…
A decentralized approach to multi-agent MILPs: finite-time feasibility and performance guarantees
Alessandro Falsone, Kostas Margellos, Maria Prandini
We address the optimal design of a large scale multi-agent system where each agent has discrete and/or continuous decision variables that need to be set so as to optimize the sum o…
Energy management for building district cooling: a distributed approach to resource sharing
Fabio Belluschi, Alessandro Falsone, Daniele Ioli +3
This paper deals with energy management in a district where multiple buildings can communicate over a time-varying network and aim at optimizing the use of shared resources like st…
Finite time distributed averaging over ring networks
Alessandro Falsone, Kostas Margellos, Simone Garatti +1
We consider a multi-agent system where each agent has its own estimate of a given quantity and the goal is to reach consensus on the average. To this purpose, we propose a distribu…
Randomised Algorithm for Feature Selection and Classification
Aida Brankovic, Alessandro Falsone, Maria Prandini +1
We here introduce a novel classification approach adopted from the nonlinear model identification framework, which jointly addresses the feature selection and classifier design tas…
Dual decomposition for multi-agent distributed optimization with coupling constraints
Alessandro Falsone, Kostas Margellos, Simone Garatti +1
We study distributed optimization in a cooperative multi-agent setting, where agents have to agree on the usage of shared resources and can communicate via a time-varying network t…
Distributed constrained optimization and consensus in uncertain networks via proximal minimization
Kostas Margellos, Alessandro Falsone, Simone Garatti +1
We provide a unifying framework for distributed convex optimization over time-varying networks, in the presence of constraints and uncertainty, features that are typically treated…