A Partition-Based Implementation of the Relaxed ADMM for Distributed Convex Optimization over Lossy Networks. - Nicola Bastianello; Ruggero Carli; Luca Schenato 001; Marco Todescato (CDC, 2018)

Distributed Optimization over Lossy Networks via Relaxed Peaceman-Rachford Splitting: a Robust ADMM Approach. - Nicola Bastianello; Marco Todescato; Ruggero Carli; Luca Schenato 001 (ECC, 2018)

Prediction-Correction Splittings for Nonsmooth Time-Varying Optimization. - Nicola Bastianello; Andrea Simonetto; Ruggero Carli (ECC, 2019)

Prediction-correction for Nonsmooth Time-varying Optimization via Forward-backward Envelopes. - Nicola Bastianello; Andrea Simonetto; Ruggero Carli (ICASSP, 2019)

Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization. - Nicola Bastianello; Andrea Simonetto; Ruggero Carli (ACSSC, 2020)

Primal and Dual Prediction-Correction Methods for Time-Varying Convex Optimization. - Nicola Bastianello; Andrea Simonetto; Ruggero Carli (CoRR, 2020)

Prediction-Correction Splittings for Time-Varying Optimization With Intermittent Observations. - Nicola Bastianello; Andrea Simonetto; Ruggero Carli (IEEE Control. Syst. Lett., 2020)

tvopt: A Python Framework for Time-Varying Optimization. - Nicola Bastianello (CDC, 2021)

Distributed and Inexact Proximal Gradient Method for Online Convex Optimization. - Nicola Bastianello; Emiliano Dall'Anese (ECC, 2021)

A Stochastic Operator Framework for Inexact Static and Online Optimization. - Nicola Bastianello; Liam Madden; Ruggero Carli; Emiliano Dall'Anese (CoRR, 2021)

Data-based Online Optimization of Networked Systems with Infrequent Feedback. - Ana M. Ospina; Nicola Bastianello; Emiliano Dall'Anese (CoRR, 2021)

Asynchronous Distributed Optimization Over Lossy Networks via Relaxed ADMM: Stability and Linear Convergence. - Nicola Bastianello; Ruggero Carli; Luca Schenato 001; Marco Todescato (IEEE Trans. Autom. Control., 2021)

OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression. - Nicola Bastianello; Andrea Simonetto; Emiliano Dall'Anese (L4DC, 2022)

A novel bound on the convergence rate of ADMM for distributed optimization. - Nicola Bastianello; Luca Schenato 001; Ruggero Carli (Autom., 2022)

Feedback-Based Optimization With Sub-Weibull Gradient Errors and Intermittent Updates. - Ana M. Ospina; Nicola Bastianello; Emiliano Dall'Anese (IEEE Control. Syst. Lett., 2022)

Online Distributed Learning with Quantized Finite-Time Coordination. - Nicola Bastianello; Apostolos I. Rikos; Karl Henrik Johansson (CDC, 2023)

Distributed Consensus Optimization via ADMM-Tracking Gradient. - Guido Carnevale; Nicola Bastianello; Ruggero Carli; Giuseppe Notarstefano (CDC, 2023)

A Unified Approach to Solve the Dynamic Consensus on the Average, Maximum, and Median Values with Linear Convergence. - Diego Deplano; Nicola Bastianello; Mauro Franceschelli; Karl Henrik Johansson (CDC, 2023)

Online Distributed Learning over Random Networks. - Nicola Bastianello; Diego Deplano; Mauro Franceschelli; Karl Henrik Johansson (CoRR, 2023)

Extrapolation-Based Prediction-Correction Methods for Time-varying Convex Optimization. - Nicola Bastianello; Ruggero Carli; Andrea Simonetto (Signal Process., 2023)

Asynchronous Distributed Learning with Quantized Finite-Time Coordination. - Nicola Bastianello; Apostolos I. Rikos; Karl Henrik Johansson (CDC, 2024)

Distributed Learning by Local Training ADMM. - Xiaoxing Ren; Nicola Bastianello; Karl Henrik Johansson; Thomas Parisini (CDC, 2024)

Real-Time Anomaly Detection and Categorization for Satellite Reaction Wheels. - Alejandro Penacho Riveiros; Yu Xing; Nicola Bastianello; Karl Henrik Johansson (ECC, 2024)

Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management. - Özlem Tugfe Demir; Lianet Méndez-Monsanto; Nicola Bastianello; Emma Fitzgerald; Gilles Callebaut (EuCNC/6G Summit, 2024)

Enhancing Privacy in Federated Learning through Local Training. - Nicola Bastianello; Changxin Liu 001; Karl Henrik Johansson (CoRR, 2024)

Multi-Agent Optimization and Learning: A Non-Expansive Operators Perspective. - Nicola Bastianello; Luca Schenato 001; Ruggero Carli (CoRR, 2024)

A survey on secure decentralized optimization and learning. - Changxin Liu 001; Nicola Bastianello; Wei Huo; Yang Shi 001; Karl Henrik Johansson (CoRR, 2024)

Learning and Verifying Maximal Taylor-Neural Lyapunov functions. - Matthieu Barreau; Nicola Bastianello (CoRR, 2024)

Internal Model-Based Online Optimization. - Nicola Bastianello; Ruggero Carli; Sandro Zampieri (IEEE Trans. Autom. Control., 2024)

A Stochastic Operator Framework for Optimization and Learning With Sub-Weibull Errors. - Nicola Bastianello; Liam Madden; Ruggero Carli; Emiliano Dall'Anese (IEEE Trans. Autom. Control., 2024)

A control theoretical approach to online constrained optimization. - Umberto Casti; Nicola Bastianello; Ruggero Carli; Sandro Zampieri (Autom., 2025)

Modular Distributed Nonconvex Learning With Error Feedback. - Guido Carnevale; Nicola Bastianello (IEEE Control. Syst. Lett., 2025)

Communication-Efficient Stochastic Distributed Learning. - Xiaoxing Ren; Nicola Bastianello; Karl Henrik Johansson; Thomas Parisini (CoRR, 2025)

Optimization and Learning in Open Multi-Agent Systems. - Diego Deplano; Nicola Bastianello; Mauro Franceschelli; Karl Henrik Johansson (CoRR, 2025)

Robust Online Learning Over Networks. - Nicola Bastianello; Diego Deplano; Mauro Franceschelli; Karl Henrik Johansson (IEEE Trans. Autom. Control., 2025)

Hierarchical Federated ADMM. - Seyed Mohammad Azimi-Abarghouyi; Nicola Bastianello; Karl Henrik Johansson; Viktoria Fodor (IEEE Netw. Lett., 2025)

Personalized and Resilient Distributed Learning Through Opinion Dynamics. - Luca Ballotta; Nicola Bastianello; Riccardo M. G. Ferrari; Karl Henrik Johansson (CoRR, 2025)

Security of Distributed Gradient Descent Against Byzantine Agents. - Sribalaji C. Anand; Nicola Bastianello (CoRR, 2025)

Distributed Optimization and Learning for Automated Stepsize Selection with Finite Time Coordination. - Apostolos I. Rikos; Nicola Bastianello; Themistoklis Charalambous; Karl Henrik Johansson (CoRR, 2025)

ADMM-Tracking Gradient for Distributed Optimization Over Asynchronous and Unreliable Networks. - Guido Carnevale; Nicola Bastianello; Giuseppe Notarstefano; Ruggero Carli (IEEE Trans. Autom. Control., 2025)