Publications

Pre-prints

  1. N. Bastianello, L. Schenato, R. Carli. “Multi-Agent Optimization and Learning: A Non-Expansive Operators Perspective.” arXiv
  2. N. Bastianello, C. Liu, K. H. Johansson. “Enhancing Privacy in Federated Learning through Local Training.” arXiv
  3. U. Casti, N. Bastianello, R. Carli, S. Zampieri. “A Control Theoretical Approach to Online Constrained Optimization.” arXiv
  4. G. Carnevale, N. Bastianello, G. Notarstefano, R. Carli. “ADMM-Tracking Gradient for Distributed Optimization over Asynchronous and Unreliable Networks.” arXiv
  5. N. Bastianello*, D. Deplano*, M. Franceschelli, K. H. Johansson. “Robust Online Learning over Networks.” [* equal contribution] arXiv

Journal articles

  1. N. Bastianello, L. Madden, R. Carli, E. Dall’Anese. “A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors.” IEEE Trans. Automatic Control [to appear] doi, link, arXiv
  2. N. Bastianello, R. Carli, S. Zampieri. “Internal Model-Based Online Optimization.” IEEE Trans. Automatic Control, vol. 69, no. 1, pp. 689–696, Jan. 2024 doi, link, arXiv
  3. N. Bastianello, R. Carli, A. Simonetto. “Extrapolation-based Prediction-Correction Methods for Time-varying Convex Optimization.” Signal Processing, vol. 210, pp. 109089, Sep. 2023 doi, link, arXiv
  4. N. Bastianello, L. Schenato, R. Carli. “A novel bound on the convergence rate of ADMM for distributed optimization.” Automatica, vol. 142, pp. 110403, Aug. 2022 doi, link
  5. A. M. Ospina, N. Bastianello, E. Dall’Anese. “Feedback-Based Optimization with Sub-Weibull Gradient Errors and Intermittent Updates.” IEEE Control Systems Letters, vol. 6, pp. 2521-2526, 2022 doi, link, arXiv
  6. N. Bastianello, R. Carli, L. Schenato, M. Todescato. “Asynchronous Distributed Optimization over Lossy Networks via Relaxed ADMM: Stability and Linear Convergence.” IEEE Trans. Automatic Control, vol. 66, no. 6, pp. 2620-2635, Jun. 2021 doi, link, arXiv
  7. N. Bastianello, A. Simonetto, R. Carli. “Prediction-Correction Splittings for Time-Varying Optimization with Intermittent Observations.” IEEE Control Systems Letters, vol. 4, no. 2, pp. 373-378, Apr. 2020 doi, link
  8. A. Olama, N. Bastianello, P. Da Costa Mendes, E. Camponogara. “Relaxed Hybrid Consensus ADMM for Distributed Convex Optimization with Coupling Constraints.” IET Control Theory & Applications, vol. 13, no. 17, pp. 2828-2837, Nov. 2019 doi, link

Conference proceedings

  1. A. Penacho Riveiros, Y. Xing, N. Bastianello, K. H. Johansson. “Real-Time Anomaly Detection and Categorization for Satellite Reaction Wheels.” 2024 European Control Conference (ECC’24), Jun. 2024
  2. Ö. T. Demir, L. Méndez-Monsanto, N. Bastianello, E. Fitzgerald, G. Callebaut. “Energy Reduction in Cell-Free Massive MIMO through Fine-Grained Resource Management.” 2024 EuCNC & 6G Summit, Jun. 2024
  3. N. Bastianello, A. I. Rikos, K. H. Johansson. “Online Distributed Learning with Quantized Finite-Time Coordination.” 2023 IEEE Conference on Decision and Control (CDC’23), Dec. 2023, pp. 5026-5032 doi, link, arXiv
  4. D. Deplano*, N. Bastianello*, M. Franceschelli, K. H. Johansson. “A unified approach to solve the dynamic consensus on the average, maximum, and median values with linear convergence.” 2023 IEEE Conference on Decision and Control (CDC’23), Dec. 2023, pp. 6442-6448 [* equal contribution] doi, link
  5. G. Carnevale, N. Bastianello, R. Carli, G. Notarstefano. “Distributed Consensus Optimization via ADMM-Tracking Gradient.” 2023 IEEE Conference on Decision and Control (CDC’23), Dec. 2023, pp. 290-295 doi, link
  6. N. Bastianello, R. Carli. “ADMM for Dynamic Average Consensus over Imperfect Networks.” IFAC Conference on Networked Systems (NecSys’22), Jul. 2022, IFAC-PapersOnLine vol. 55, no. 13, pp. 228-233 doi, link
  7. N. Bastianello, A. Simonetto, E. Dall’Anese. “OpReg-Boost: Learning to Accelerate Online Algorithms with Operator Regression.” Proceedings of The 4th Annual Learning for Dynamics and Control Conference (L4DC’22), PMLR 168, pp. 138-152 link, arXiv, code
  8. N. Bastianello. “tvopt: A Python Framework for Time-Varying Optimization.” 2021 IEEE Conference on Decision and Control (CDC’21), Dec. 2021, pp. 227-232 doi, link, arXiv, code
  9. N. Bastianello, E. Dall’Anese. “Distributed and Inexact Proximal Gradient Method for Online Convex Optimization.” 2021 European Control Conference (ECC’21), Jun. 2021, pp. 2432-2437 doi, link, arXiv
  10. N. Bastianello, A. Simonetto, R. Carli. “Distributed Prediction-Correction ADMM for Time-Varying Convex Optimization.” 54th Asilomar Conference on Signals, Systems and Computers, Nov. 2020, pp. 47-52 doi, link, arXiv
  11. N. Bastianello, A. Simonetto, R. Carli. “Prediction-Correction Splittings for Nonsmooth Time-Varying Optimization.” 2019 European Control Conference (ECC’19), Jun. 2019, pp. 1963-1968 doi, link, arXiv
  12. N. Bastianello, A. Simonetto, R. Carli. “Prediction-Correction for Nonsmooth Time-Varying Optimization via Forward-Backward Envelopes.” 2019 International Conference on Acoustics, Speech, and Signal Processing (ICASSP’19), May 2019, pp. 5581-5585 doi, link, arXiv
  13. N. Bastianello, R. Carli, L. Schenato, M. Todescato. “A Partition-Based Implementation of the Relaxed ADMM for Distributed Convex Optimization over Lossy Networks.” 2018 IEEE Conference on Decision and Control (CDC’18), Dec. 2018, pp. 3379-3384 doi, link, arXiv
  14. N. Bastianello, M. Todescato, R. Carli, L. Schenato. “Distributed Optimization over Lossy Networks via Relaxed Peaceman-Rachford Splitting: a Robust ADMM Approach.” 2018 European Control Conference (ECC’18), Jun. 2018, pp. 477-482 doi, link, arXiv

PhD Thesis

  • N. Bastianello, Supervisors: R. Carli, A. Simonetto. “Operator theory for optimization and learning.” University of Padova, 2021 pdf

Patent

  • A. Simonetto, N. Bastianello, R. Carli, T. T. Tchrakian. “Methods and Systems for Managing As-A-Service Systems in the Event of Connectivity Issues”, US-11886319-B2 link