Ion Necoara
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Geciteerd door
Linear convergence of first order methods for non-strongly convex optimization
I Necoara, Y Nesterov, F Glineur
Mathematical Programming 175, 69-107, 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
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
Random coordinate descent algorithms for multi-agent convex optimization over networks
I Necoara
IEEE Transactions on Automatic Control 58 (8), 2001-2012, 2013
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
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
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
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
Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization
A Patrascu, I Necoara
Journal of Global Optimization 61 (1), 19-46, 2015
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
Interior-point lagrangian decomposition method for separable convex optimization
I Necoara, JAK Suykens
Journal of Optimization Theory and Applications 143 (3), 567, 2009
On linear convergence of a distributed dual gradient algorithm for linearly constrained separable convex problems
I Necoara, V Nedelcu
Automatica 55, 209-216, 2015
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
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
Faster randomized block Kaczmarz algorithms
I Necoara
SIAM Journal on Matrix Analysis and Applications 40 (4), 1425-1452, 2019
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
Random block coordinate descent methods for linearly constrained optimization over networks
I Necoara, Y Nesterov, F Glineur
Journal of Optimization Theory and Applications 173, 227-254, 2017
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
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
Every continuous nonlinear control system can be obtained by parametric convex programming
M Baes, M Diehl, I Necoara
IEEE Transactions on Automatic Control 53 (8), 1963-1967, 2008
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