Ion Necoara

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	Toate	Din 2020
Referințe bibliografice	3548	1561
h-index	30	20
i10-index	61	35

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Bart De Schutterfull professor & head of department, Delft Center for Systems and ControlAdresă de e-mail confirmată pe tudelft.nl
Valentin NedelcuPhD.Adresă de e-mail confirmată pe acse.pub.ro
Johan SuykensProfessor, KU LeuvenAdresă de e-mail confirmată pe esat.kuleuven.be
Ton van den boomAssociate professor, Delft University of TechnologyAdresă de e-mail confirmată pe tudelft.nl
François GlineurProfessor, Université catholique de Louvain (UCLouvain) - ICTEAM / INMA & CORE - EPLAdresă de e-mail confirmată pe uclouvain.be
Quoc Tran-DinhDepartment of Statistics and Operations Research, UNCAdresă de e-mail confirmată pe email.unc.edu
Moritz DiehlProfessor of Systems, Control and Optimization at the University of FreiburgAdresă de e-mail confirmată pe imtek.uni-freiburg.de
Yurii NesterovResearch Professor at Corvinus University of Budapest, Emeritus Profesor at UCLouvainAdresă de e-mail confirmată pe uclouvain.be
Tudor C IonescuPolitehnica Univ. of Bucharest & "Gh. Mihoc-C. Iacob" Institute for Mathematical Statistics andAdresă de e-mail confirmată pe acse.pub.ro
Sorin OlaruProfessor - Control Engineering, RTE Chair at CentraleSupélec, L2S, FranceAdresă de e-mail confirmată pe supelec.fr
Tamas KeviczkyDelft Center for Sytems and Control, Delft University of TechnologyAdresă de e-mail confirmată pe tudelft.nl
Minh Dang DoanUniversity of FreiburgAdresă de e-mail confirmată pe doan.vn
Angelia NedichProfessor, Arizona State UniversityAdresă de e-mail confirmată pe asu.edu

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Ion Necoara

Professor in optimization, University Politehnica Bucharest

Adresă de e-mail confirmată pe upb.ro - Pagina de pornire

Optimization theory optimization algorithms big data machine learning distributed optimization and control


Titlu Sortați după descrierea bibliografică Sortați după an Sortați după titlu	Citat de Citat de	Anul
Linear convergence of first order methods for non-strongly convex optimization I Necoara, Y Nesterov, F Glineur Mathematical Programming 175, 69-107, 2019	430	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	207	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	157	2011
Faster randomized block Kaczmarz algorithms I Necoara SIAM Journal on Matrix Analysis and Applications 40 (4), 1425-1452, 2019	125	2019
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	125	2013
Random coordinate descent algorithms for multi-agent convex optimization over networks I Necoara IEEE Transactions on Automatic Control 58 (8), 2001-2012, 2013	122	2013
Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization A Patrascu, I Necoara Journal of Global Optimization 61 (1), 19-46, 2015	112	2015
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	111	2014
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	110	2014
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	102	2013
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	101	2016
Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization A Patrascu, I Necoara Journal of Machine Learning Research 18 (198), 1-42, 2018	100	2018
Interior-point lagrangian decomposition method for separable convex optimization I Necoara, JAK Suykens Journal of Optimization Theory and Applications 143 (3), 567-588, 2009	86	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	79	2015
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	62	2017
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	60	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	59	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	57	2019
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	55	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	53	2013

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