| Tracking-ADMM for distributed constraint-coupled optimization A Falsone, I Notarnicola, G Notarstefano, M Prandini Automatica 117, 108962, 2020 | 133 | 2020 |
| Distributed optimization for smart cyber-physical networks G Notarstefano, I Notarnicola, A Camisa Foundations and Trends® in Systems and Control 7 (3), 253-383, 2019 | 106 | 2019 |
| Constraint-coupled distributed optimization: A relaxation and duality approach I Notarnicola, G Notarstefano IEEE Transactions on Control of Network Systems 7 (1), 483-492, 2019 | 94 | 2019 |
| Asynchronous distributed optimization via randomized dual proximal gradient I Notarnicola, G Notarstefano IEEE Transactions on Automatic Control 62 (5), 2095-2106, 2016 | 87 | 2016 |
| Disropt: a python framework for distributed optimization F Farina, A Camisa, A Testa, I Notarnicola, G Notarstefano IFAC-PapersOnLine 53 (2), 2666-2671, 2020 | 47 | 2020 |
| Distributed partitioned big-data optimization via asynchronous dual decomposition I Notarnicola, R Carli, G Notarstefano IEEE Transactions on Control of Network Systems 5 (4), 1910-1919, 2017 | 35 | 2017 |
| A duality-based approach for distributed min–max optimization I Notarnicola, M Franceschelli, G Notarstefano IEEE Transactions on Automatic Control 64 (6), 2559-2566, 2018 | 29 | 2018 |
| GTAdam: Gradient tracking with adaptive momentum for distributed online optimization G Carnevale, F Farina, I Notarnicola, G Notarstefano IEEE Transactions on Control of Network Systems 10 (3), 1436-1448, 2022 | 27 | 2022 |
| Distributed primal decomposition for large-scale MILPs A Camisa, I Notarnicola, G Notarstefano IEEE Transactions on Automatic Control 67 (1), 413-420, 2021 | 26 | 2021 |
| Triggered gradient tracking for asynchronous distributed optimization G Carnevale, I Notarnicola, L Marconi, G Notarstefano Automatica 147, 110726, 2023 | 21 | 2023 |
| Distributed constraint-coupled optimization via primal decomposition over random time-varying graphs A Camisa, F Farina, I Notarnicola, G Notarstefano Automatica 131, 109739, 2021 | 21 | 2021 |
| A system theoretical perspective to gradient-tracking algorithms for distributed quadratic optimization M Bin, I Notarnicola, L Marconi, G Notarstefano 2019 IEEE 58th Conference on Decision and Control (CDC), 2994-2999, 2019 | 21 | 2019 |
| Distributed personalized gradient tracking with convex parametric models I Notarnicola, A Simonetto, F Farina, G Notarstefano IEEE Transactions on Automatic Control 68 (1), 588-595, 2022 | 20 | 2022 |
| Distributed big-data optimization via blockwise gradient tracking I Notarnicola, Y Sun, G Scutari, G Notarstefano IEEE Transactions on Automatic Control 66 (5), 2045-2060, 2020 | 19 | 2020 |
| Distributed big-data optimization via block-iterative convexification and averaging I Notarnicola, Y Sun, G Scutari, G Notarstefano 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2281-2288, 2017 | 18 | 2017 |
| A primal decomposition method with suboptimality bounds for distributed mixed-integer linear programming A Camisa, I Notarnicola, G Notarstefano 2018 IEEE Conference on Decision and Control (CDC), 3391-3396, 2018 | 15 | 2018 |
| A duality-based approach for distributed optimization with coupling constraints I Notarnicola, G Notarstefano IFAC-PapersOnLine 50 (1), 14326-14331, 2017 | 14 | 2017 |
| The gradient tracking is a distributed integral action I Notarnicola, M Bin, L Marconi, G Notarstefano IEEE Transactions on Automatic Control 68 (12), 7911-7918, 2023 | 12 | 2023 |
| Stability, linear convergence, and robustness of the wang-elia algorithm for distributed consensus optimization M Bin, I Notarnicola, T Parisini 2022 IEEE 61st Conference on Decision and Control (CDC), 1610-1615, 2022 | 12 | 2022 |
| A randomized primal distributed algorithm for partitioned and big-data non-convex optimization I Notarnicola, G Notarstefano 2016 IEEE 55th Conference on Decision and Control (CDC), 153-158, 2016 | 12 | 2016 |
Giuseppe NotarstefanoProfessor of Automatic Control, University of BolognaEmail verificata su unibo.it
