257 citations · 996 across the 51 of their papers we have counts for
28 papers · 1 filter
Adaptive Stepsizes With Certified Convergence in Distributed Gradient Tracking With Quadratic Costs
Yifan Wang, Luca Ballotta, Ruggero Carli +3
In this work, we propose an adaptive stepsize rule with guaranteed convergence for Distributed Gradient Tracking applied to scalar quadratic problems with heterogeneous curvatures.…
Pursuing Optimal Stepsize in Adaptive Gradient-Based Quadratic Optimization
Yifan Wang, Luca Ballotta, Ruggero Carli +2
In this paper, we address the problem of achieving fast convergence in gradient descent for quadratic functions without relying on a priori knowledge of global function parameters.…
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…
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…
A Control Theoretical Approach to Online Constrained Optimization
Umberto Casti, Nicola Bastianello, Ruggero Carli +1
In this paper we focus on the solution of online problems with time-varying, linear equality and inequality constraints. Our approach is to design a novel online algorithm by lever…
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.…
Internal Model-Based Online Optimization
Nicola Bastianello, Ruggero Carli, Sandro Zampieri
In this paper we propose a model-based approach to the design of online optimization algorithms, with the goal of improving the tracking of the solution trajectory (trajectories) w…
A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors
Nicola Bastianello, Liam Madden, Ruggero Carli +1
This paper proposes a framework to study the convergence of stochastic optimization and learning algorithms. The framework is modeled over the different challenges that these algor…
Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization
Nicola Bastianello, Andrea Simonetto, Ruggero Carli
This paper introduces a dual-regularized ADMM approach to distributed, time-varying optimization. The proposed algorithm is designed in a prediction-correction framework, in which…
Extrapolation-based Prediction-Correction Methods for Time-varying Convex Optimization
Nicola Bastianello, Ruggero Carli, Andrea Simonetto
In this paper, we focus on the solution of online optimization problems that arise often in signal processing and machine learning, in which we have access to streaming sources of…
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…
Prediction-Correction Splittings for Nonsmooth Time-Varying Optimization
Nicola Bastianello, Andrea Simonetto, Ruggero Carli
We address the solution of time-varying optimization problems characterized by the sum of a time-varying strongly convex function and a time-invariant nonsmooth convex function. We…
Prediction-Correction for Nonsmooth Time-Varying Optimization via Forward-Backward Envelopes
Nicola Bastianello, Andrea Simonetto, Ruggero Carli
We present an algorithm for minimizing the sum of a strongly convex time-varying function with a time-invariant, convex, and nonsmooth function. The proposed algorithm employs the…
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…
Newton-Raphson Consensus under asynchronous and lossy communications for peer-to-peer networks
Nicoletta Bof, Ruggero Carli, Giuseppe Notarstefano +2
In this work we study the problem of unconstrained convex-optimization in a fully distributed multi-agent setting which includes asynchronous computation and lossy communication. I…
Analysis of Newton-Raphson Consensus for multi-agent convex optimization under asynchronous and lossy communications
Ruggero Carli, Giuseppe Notarstefano, Luca Schenato +1
We extend a multi-agent convex-optimization algorithm named Newton-Raphson consensus to a network scenario that involves directed, asynchronous and lossy communications. We theoret…
Full-Pose Tracking Control for Aerial Robotic Systems with Laterally-Bounded Input Force
Antonio Franchi, Ruggero Carli, Davide Bicego +1
In this paper, we define a general class of abstract aerial robotic systems named Laterally Bounded Force (LBF) vehicles, in which most of the control authority is expressed along…
Voltage stress minimization by optimal reactive power control
Marco Todescato, John W. Simpson-Porco, Florian Dörfler +2
A standard operational requirement in power systems is that the voltage magnitudes lie within prespecified bounds. Conventional engineering wisdom suggests that such a tightly-regu…
Distributed reactive power feedback control for voltage regulation and loss minimization
Saverio Bolognani, Guido Cavraro, Ruggero Carli +1
We consider the problem of exploiting the microgenerators dispersed in the power distribution network in order to provide distributed reactive power compensation for power losses m…
Distributed Estimation via Iterative Projections with Application to Power Network Monitoring
Fabio Pasqualetti, Ruggero Carli, Francesco Bullo
This work presents a distributed method for control centers to monitor the operating condition of a power network, i.e., to estimate the network state, and to ultimately determine…