| Linear convergence of first order methods for non-strongly convex optimization I Necoara, Y Nesterov, F Glineur Mathematical Programming 175 (1), 69-107, 2019 | 269 | 2019 |
| Application of a smoothing technique to decomposition in convex optimization I Necoara, JAK Suykens IEEE Transactions on Automatic control 53 (11), 2674-2679, 2008 | 197 | 2008 |
| Parallel and distributed optimization methods for estimation and control in networks I Necoara, V Nedelcu, I Dumitrache Journal of Process Control 21 (5), 756-766, 2011 | 130 | 2011 |
| A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints I Necoara, A Patrascu Computational Optimization and Applications 57 (2), 307-337, 2014 | 102 | 2014 |
| Rate analysis of inexact dual first-order methods application to dual decomposition I Necoara, V Nedelcu IEEE Transactions on Automatic Control 59 (5), 1232-1243, 2013 | 100 | 2013 |
| Random coordinate descent algorithms for multi-agent convex optimization over networks I Necoara IEEE Transactions on Automatic Control 58 (8), 2001-2012, 2013 | 100 | 2013 |
| Efficient parallel coordinate descent algorithm for convex optimization problems with separable constraints: application to distributed MPC I Necoara, D Clipici Journal of Process Control 23 (3), 243-253, 2013 | 95 | 2013 |
| Computational complexity of inexact gradient augmented Lagrangian methods: application to constrained MPC V Nedelcu, I Necoara, Q Tran-Dinh SIAM Journal on Control and Optimization 52 (5), 3109-3134, 2014 | 92 | 2014 |
| Parallel random coordinate descent method for composite minimization: Convergence analysis and error bounds I Necoara, D Clipici SIAM Journal on Optimization 26 (1), 197-226, 2016 | 91 | 2016 |
| Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization A Patrascu, I Necoara Journal of Global Optimization 61 (1), 19-46, 2015 | 86 | 2015 |
| Interior-point lagrangian decomposition method for separable convex optimization I Necoara, JAK Suykens Journal of Optimization Theory and Applications 143 (3), 567-588, 2009 | 76 | 2009 |
| Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization A Patrascu, I Necoara The Journal of Machine Learning Research 18 (1), 7204-7245, 2017 | 60 | 2017 |
| On linear convergence of a distributed dual gradient algorithm for linearly constrained separable convex problems I Necoara, V Nedelcu Automatica 55, 209-216, 2015 | 59 | 2015 |
| Application of the proximal center decomposition method to distributed model predictive control I Necoara, D Doan, JAK Suykens 2008 47th IEEE Conference on Decision and Control, 2900-2905, 2008 | 58 | 2008 |
| Improved dual decomposition based optimization for DSL dynamic spectrum management P Tsiaflakis, I Necoara, JAK Suykens, M Moonen IEEE Transactions on Signal Processing 58 (4), 2230-2245, 2009 | 57 | 2009 |
| Complexity of first-order inexact Lagrangian and penalty methods for conic convex programming I Necoara, A Patrascu, F Glineur Optimization Methods and Software 34 (2), 305-335, 2019 | 43 | 2019 |
| An inexact perturbed path-following method for Lagrangian decomposition in large-scale separable convex optimization QT Dinh, I Necoara, C Savorgnan, M Diehl SIAM Journal on Optimization 23 (1), 95-125, 2013 | 43 | 2013 |
| Faster randomized block Kaczmarz algorithms I Necoara SIAM Journal on Matrix Analysis and Applications 40 (4), 1425-1452, 2019 | 42 | 2019 |
| Random block coordinate descent methods for linearly constrained optimization over networks I Necoara, Y Nesterov, F Glineur Journal of Optimization Theory and Applications 173 (1), 227-254, 2017 | 42 | 2017 |
| Randomized projection methods for convex feasibility: Conditioning and convergence rates I Necoara, P Richtárik, A Patrascu SIAM Journal on Optimization 29 (4), 2814-2852, 2019 | 41 | 2019 |
Bart De Schutterfull professor. hybrid control and intelligent transportation systems, Delft University of在 tudelft.nl 的电子邮件经过验证
