| Optimal parameter selection for the alternating direction method of multipliers (ADMM): quadratic problems E Ghadimi, A Teixeira, I Shames, M Johansson IEEE Transactions on Automatic Control 60 (3), 644-658, 2014 | 508 | 2014 |
| Global convergence of the heavy-ball method for convex optimization E Ghadimi, HR Feyzmahdavian, M Johansson 2015 European control conference (ECC), 310-315, 2015 | 248 | 2015 |
| Low power, low delay: opportunistic routing meets duty cycling O Landsiedel, E Ghadimi, S Duquennoy, M Johansson Proceedings of the 11th international conference on Information Processing …, 2012 | 248 | 2012 |
| Learning radio resource management in RANs: Framework, opportunities, and challenges FD Calabrese, L Wang, E Ghadimi, G Peters, L Hanzo, P Soldati IEEE Communications Magazine 56 (9), 138-145, 2018 | 145 | 2018 |
| Opportunistic Routing in Low Duty-Cycle Wireless Sensor Networks E Ghadimi, O Landsiedel, P Soldati, S Duquennoy, M Johansson ACM Transactions on Sensor Networks (TOSN) 10 (4), 67, 2014 | 128 | 2014 |
| A reinforcement learning approach to power control and rate adaptation in cellular networks E Ghadimi, FD Calabrese, G Peters, P Soldati 2017 IEEE International Conference on Communications (ICC), 1-7, 2017 | 94 | 2017 |
| Multi-step gradient methods for networked optimization E Ghadimi, I Shames, M Johansson IEEE Transactions on Signal Processing 61 (21), 5417-5429, 2013 | 89 | 2013 |
| Accelerated gradient methods for networked optimization E Ghadimi, M Johansson, I Shames Proceedings of the 2011 American Control Conference, 1668-1673, 2011 | 51 | 2011 |
| An analytical model of delay in multi-hop wireless ad hoc networks E Ghadimi, A Khonsari, A Diyanat, M Farmani, N Yazdani Wireless Networks 17, 1679-1697, 2011 | 41 | 2011 |
| Optimal scaling of the ADMM algorithm for distributed quadratic programming A Teixeira, E Ghadimi, I Shames, H Sandberg, M Johansson ArXiv e-prints :1303.6680, 2014 | 39 | 2014 |
| The ADMM algorithm for distributed quadratic problems: Parameter selection and constraint preconditioning A Teixeira, E Ghadimi, I Shames, H Sandberg, M Johansson IEEE Transactions on Signal Processing 64 (2), 290-305, 2015 | 35 | 2015 |
| Optimal control of linear systems with limited control actions: Threshold-based event-triggered control B Demirel, E Ghadimi, DE Quevedo, M Johansson IEEE Transactions on Control of Network Systems 5 (3), 1275-1286, 2017 | 33 | 2017 |
| A metric for opportunistic routing in duty cycled wireless sensor networks E Ghadimi, O Landsiedel, P Soldati, M Johansson 2012 9th Annual IEEE Communications Society Conference on Sensor, Mesh and …, 2012 | 33 | 2012 |
| On the Optimal Step-size Selection for the Alternating Direction Method of Multipliers⋆ E Ghadimi, A Teixeira, I Shames, M Johansson Proceeding of IFAC Workshop on Estimation and Control of Networked Systems …, 2012 | 31 | 2012 |
| The ADMM algorithm for distributed averaging: Convergence rates and optimal parameter selection E Ghadimi, A Teixeira, MG Rabbat, M Johansson 2014 48th Asilomar Conference on Signals, Systems and Computers, 783-787, 2014 | 16 | 2014 |
| Hidden terminal-aware contention resolution with an optimal distribution E Ghadimi, P Soldati, F Osterlind, H Zhang, M Johansson 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor …, 2011 | 13 | 2011 |
| Efficient rule based structural algorithms for classification of tree structured data MH Chehreghani, MH Chehreghani, C Lucas, M Rahgozar, E Ghadimi Intelligent Data Analysis 13 (1), 165-188, 2009 | 9 | 2009 |
| A distributed clustering algorithm for fault-tolerant event region detection in wireless sensor networks A Abbasi, E Ghadimi, A Khonsari, N Yazdani, M Ould-Khaoua Frontiers of High Performance Computing and Networking ISPA 2007 Workshops …, 2007 | 9 | 2007 |
| Downlink power control in dense 5g radio access networks through deep reinforcement learning S Saeidian, S Tayamon, E Ghadimi ICC 2020-2020 IEEE International Conference on Communications (ICC), 1-6, 2020 | 8 | 2020 |
| Method and unit for radio resource management using reinforcement learning FD Calabrese, P Soldati, G Peters, E Ghadimi US Patent 11,071,122, 2021 | 7 | 2021 |
Mikael JohanssonProfessor of Electrical Engineering, KTHVerified email at kth.se
