55 citations · 94 across the 18 of their papers we have counts for
20 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…
Finite sample learning of moving targets
Nikolaus Vertovec, Kostas Margellos, Maria Prandini
We consider a moving target that we seek to learn from samples. Our results extend randomized techniques developed in control and optimization for a constant target to the case whe…
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…
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…
Sampling-based optimal kinodynamic planning with motion primitives
Basak Sakcak, Luca Bascetta, Gianni Ferretti +1
This paper proposes a novel sampling-based motion planner, which integrates in RRT* (Rapidly exploring Random Tree star) a database of pre-computed motion primitives to alleviate i…
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…
Switched control for quantized feedback systems: invariance and limit cycle analysis
Alessandro Vittorio Papadopoulos, Federico Terraneo, Alberto Leva +1
We study feedback control for discrete-time linear time-invariant systems in the presence of quantization both in the control action and in the measurement of the controlled variab…
Price of anarchy in electric vehicle charging control games: When Nash equilibria achieve social welfare
Luca Deori, Kostas Margellos, Maria Prandini
We consider the problem of optimal charging of plug-in electric vehicles (PEVs). We treat this problem as a multi-agent game, where vehicles/agents are heterogeneous since they are…
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…
A relaxation technique to ensure feasibility in stochastic control with input and state constraints
Luca Deori, Simone Garatti, Maria Prandini
We consider a stochastic linear system and address the design of a finite horizon control policy that is optimal according to some average cost criterion and accounts also for prob…
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…
Regularized Jacobi iteration for decentralized convex optimization with separable constraints
Luca Deori, Kostas Margellos, Maria Prandini
We consider multi-agent, convex optimization programs subject to separable constraints, where the constraint function of each agent involves only its local decision vector, while 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…
Performance assessment and design of abstracted models for stochastic hybrid systems through a randomized approach
M. Prandini, S. Garatti, R. Vignali
In this paper, a simulation-based method for the analysis and design of abstracted models for a stochastic hybrid system is proposed. The accuracy of a model is evaluated in terms…
On the connection between compression learning and scenario based optimization
Kostas Margellos, Maria Prandini, John Lygeros
We investigate the connections between compression learning and scenario based optimization. We first show how to strengthen, or relax the consistency assumption at the basis of co…