93 citations · 221 across the 21 of their papers we have counts for
21 papers
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…
ADMM-Based Distributed Kalman-like Observer with Applications to Cooperative Localization
Nicola De Carli, Nicola Bastianello, Dimos V. Dimarogonas
This paper addresses distributed state estimation for multi-agent systems with local and relative measurements, motivated by cooperative localization problems in which the global s…
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…
Hierarchical Federated ADMM
Seyed Mohammad Azimi-Abarghouyi, Nicola Bastianello, Karl H. Johansson +1
In this paper, we depart from the widely-used gradient descent-based hierarchical federated learning (FL) algorithms to develop a novel hierarchical FL framework based on the alter…
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.…
Robust Online Learning over Networks
Nicola Bastianello, Diego Deplano, Mauro Franceschelli +1
The recent deployment of multi-agent networks has enabled the distributed solution of learning problems, where agents cooperate to train a global model without sharing their local,…
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…
Feedback-Based Optimization with Sub-Weibull Gradient Errors and Intermittent Updates
Ana M. Ospina, Nicola Bastianello, Emiliano Dall'Anese
This paper considers a feedback-based projected gradient method for optimizing systems modeled as algebraic maps. The focus is on a setup where the gradient is corrupted by random…
OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression
Nicola Bastianello, Andrea Simonetto, Emiliano Dall'Anese
This paper presents a new regularization approach -- termed OpReg-Boost -- to boost the convergence and lessen the asymptotic error of online optimization and learning algorithms.…
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…
tvopt: A Python Framework for Time-Varying Optimization
Nicola Bastianello
This paper introduces tvopt, a Python framework for prototyping and benchmarking time-varying (or online) optimization algorithms. The paper first describes the theoretical approac…
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…
Distributed and Inexact Proximal Gradient Method for Online Convex Optimization
Nicola Bastianello, Emiliano Dall'Anese
This paper develops and analyzes an online distributed proximal-gradient method (DPGM) for time-varying composite convex optimization problems. Each node of the network features a…
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…