101 citations · 174 across the 22 of their papers we have counts for
29 papers
Affine-coupled Distributed Optimization via Distributed Proximal Jacobian ADMM with Quantized Communication
Xu Du, Boyu Han, Ivano Notarnicola +2
This paper investigates distributed resource allocation optimization over directed graphs with limited communication bandwidth. We develop a novel distributed algorithm that integr…
An exponentially stable discrete-time primal-dual algorithm for distributed constrained optimization
Xiaoxing Ren, Michelangelo Bin, Ivano Notarnicola +1
This paper studies a distributed algorithm for constrained consensus optimization that is obtained by fusing the Arrow-Hurwicz-Uzawa primal-dual gradient method for centralized con…
Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient
Lorenzo Sforni, Guido Carnevale, Ivano Notarnicola +1
In this paper, we investigate a data-driven framework to solve Linear Quadratic Regulator (LQR) problems when the dynamics is unknown, with the additional challenge of providing st…
Achievement and Fragility of Long-term Equitability
Andrea Simonetto, Ivano Notarnicola
Equipping current decision-making tools with notions of fairness, equitability, or other ethically motivated outcomes, is one of the top priorities in recent research efforts in ma…
Stability, Linear Convergence, and Robustness of the Wang-Elia Algorithm for Distributed Consensus Optimization
Michelangelo Bin, Ivano Notarnicola, Thomas Parisini
We revisit an algorithm for distributed consensus optimization proposed in 2010 by J. Wang and N. Elia. By means of a Lyapunov-based analysis, we prove input-to-state stability of…
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…
GoPRONTO: a Feedback-based Framework for Nonlinear Optimal Control
Lorenzo Sforni, Sara Spedicato, Ivano Notarnicola +1
In this paper we We propose GoPRONTO, a first-order, feedback-based approach to solve nonlinear discrete-time optimal control problems. This method is a generalized first-order fra…
Distributed Constraint-Coupled Optimization via Primal Decomposition over Random Time-Varying Graphs
Andrea Camisa, Francesco Farina, Ivano Notarnicola +1
The paper addresses large-scale, convex optimization problems that need to be solved in a distributed way by agents communicating according to a random time-varying graph. Specific…
Distributed Primal Decomposition for Large-Scale MILPs
Andrea Camisa, Ivano Notarnicola, Giuseppe Notarstefano
This paper deals with a distributed Mixed-Integer Linear Programming (MILP) set-up arising in several control applications. Agents of a network aim to minimize the sum of local lin…
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,…
Distributed Personalized Gradient Tracking with Convex Parametric Models
Ivano Notarnicola, Andrea Simonetto, Francesco Farina +1
We present a distributed optimization algorithm for solving online personalized optimization problems over a network of computing and communicating nodes, each of which linked to a…
A System Theoretical Perspective to Gradient-Tracking Algorithms for Distributed Quadratic Optimization
Michelangelo Bin, Ivano Notarnicola, Lorenzo Marconi +1
In this paper we consider a recently developed distributed optimization algorithm based on gradient tracking. We propose a system theory framework to analyze its structural propert…
DISROPT: a Python Framework for Distributed Optimization
Francesco Farina, Andrea Camisa, Andrea Testa +2
In this paper we introduce DISROPT, a Python package for distributed optimization over networks. We focus on cooperative set-ups in which an optimization problem must be solved by…
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…
Distributed Optimization for Smart Cyber-Physical Networks
Giuseppe Notarstefano, Ivano Notarnicola, Andrea Camisa
The presence of embedded electronics and communication capabilities as well as sensing and control in smart devices has given rise to the novel concept of cyber-physical networks,…
Distributed Submodular Minimization over Networks: a Greedy Column Generation Approach
Andrea Testa, Ivano Notarnicola, Giuseppe Notarstefano
Submodular optimization is a special class of combinatorial optimization arising in several machine learning problems, but also in cooperative control of complex systems. In this p…
A Primal Decomposition Method with Suboptimality Bounds for Distributed Mixed-Integer Linear Programming
Andrea Camisa, Ivano Notarnicola, Giuseppe Notarstefano
In this paper we deal with a network of agents seeking to solve in a distributed way Mixed-Integer Linear Programs (MILPs) with a coupling constraint (modeling a limited shared res…
Distributed Big-Data Optimization via Block-wise Gradient Tracking
Ivano Notarnicola, Ying Sun, Gesualdo Scutari +1
We study distributed big-data nonconvex optimization in multi-agent networks. We consider the (constrained) minimization of the sum of a smooth (possibly) nonconvex function, i.e.,…
Distributed Big-Data Optimization via Block Communications
Ivano Notarnicola, Ying Sun, Gesualdo Scutari +1
We study distributed multi-agent large-scale optimization problems, wherein the cost function is composed of a smooth possibly nonconvex sum-utility plus a DC (Difference-of-Convex…
Distributed Partitioned Big-Data Optimization via Asynchronous Dual Decomposition
Ivano Notarnicola, Ruggero Carli, Giuseppe Notarstefano
In this paper we consider a novel partitioned framework for distributed optimization in peer-to-peer networks. In several important applications the agents of a network have to sol…
Distributed Big-Data Optimization via Block-Iterative Convexification and Averaging
Ivano Notarnicola, Ying Sun, Gesualdo Scutari +1
In this paper, we study distributed big-data nonconvex optimization in multi-agent networks. We consider the (constrained) minimization of the sum of a smooth (possibly) nonconvex…
A Duality-Based Approach for Distributed Optimization with Coupling Constraints
Ivano Notarnicola, Giuseppe Notarstefano
In this paper we consider a distributed optimization scenario in which a set of agents has to solve a convex optimization problem with separable cost function, local constraint set…
Constraint Coupled Distributed Optimization: a Relaxation and Duality Approach
Ivano Notarnicola, Giuseppe Notarstefano
In this paper we consider a general, challenging distributed optimization set-up arising in several important network control applications. Agents of a network want to minimize the…
A duality-based approach for distributed min-max optimization with application to demand side management
Ivano Notarnicola, Mauro Franceschelli, Giuseppe Notarstefano
In this paper we consider a distributed optimization scenario in which a set of processors aims at minimizing the maximum of a collection of "separable convex functions" subject to…