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2020 – today
- 2024
[j42]Yassine Nabou
, Ion Necoara:
Efficiency of higher-order algorithms for minimizing composite functions. Comput. Optim. Appl. 87(2): 441-473 (2024)- [j41]Flávia Chorobura, Ion Necoara:
Coordinate descent methods beyond smoothness and separability. Comput. Optim. Appl. 88(1): 107-149 (2024) - [j40]Daniela Lupu, Ion Necoara:
Convergence analysis of stochastic higher-order majorization-minimization algorithms. Optim. Methods Softw. 39(2): 384-413 (2024) - [j39]Daniela Lupu, Ion Necoara:
Exact representation and efficient approximations of linear model predictive control laws via HardTanh type deep neural networks. Syst. Control. Lett. 186: 105742 (2024) - [c59]Nitesh Kumar Singh, Ion Necoara:
Unified Analysis of Stochastic Gradient Projection Methods for Convex Optimization with Functional Constraints. ECC 2024: 3600-3605 - [c58]Tudor C. Ionescu, Orest V. Iftime, Ion Necoara:
Data-Driven Loewner Matrices-Based Modeling and Model Predictive Control of a Single Machine Infinite Bus Model. MED 2024: 610-615 - [i2]Jon Álvarez Justo, Daniela Lupu, Milica Orlandic, Ion Necoara, Tor Arne Johansen:
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction. CoRR abs/2401.14762 (2024) - 2023
- [j38]Flávia Chorobura, Ion Necoara:
Random Coordinate Descent Methods for Nonseparable Composite Optimization. SIAM J. Optim. 33(3): 2160-2190 (2023) - [c57]Lahcen El Bourkhissi, Ion Necoara, Panagiotis Patrinos:
Linearized ADMM for Nonsmooth Nonconvex Optimization with Nonlinear Equality Constraints. CDC 2023: 7312-7317 - [c56]Liliana Maria Ghinea, Daniela Lupu, Marian Barbu, Ion Necoara:
Control of a wastewater treatment process using linear and nonlinear model predictive control. ETFA 2023: 1-6 - [c55]Flávia Chorobura, François Glineur, Ion Necoara:
Can random proximal coordinate descent be accelerated on nonseparable convex composite minimization problems? ECC 2023: 1-6 - [c54]Daniela Lupu, Ion Necoara:
Deep unfolding projected first order methods-based architectures: application to linear model predictive control. ECC 2023: 1-6 - [c53]Daniela Lupu, Ion Necoara, Tudor C. Ionescu, Liliana Maria Ghinea, Joseph L. Garrett, Tor Arne Johansen:
Dimensionality reduction of hyperspectral images using an ICA-based stochastic second-order optimization algorithm. ECC 2023: 1-6 - [c52]Yassine Nabou, Lucian Toma, Ion Necoara:
Modified projected Gauss-Newton method for constrained nonlinear least-squares: application to power flow analysis. ECC 2023: 1-6 - [c51]Lionel Tondji, Dirk A. Lorenz, Ion Necoara:
An accelerated randomized Bregman-Kaczmarz method for strongly convex linearly constraint optimization. ECC 2023: 1-6 - [i1]Lionel Tondji, Ion Necoara, Dirk A. Lorenz:
Acceleration and restart for the randomized Bregman-Kaczmarz method. CoRR abs/2310.17338 (2023) - 2022
- [j37]Tudor C. Ionescu, Orest V. Iftime, Ion Necoara:
Model reduction with pole-zero placement and high order moment matching. Autom. 138: 110140 (2022) - [j36]Ion Necoara, Nitesh Kumar Singh:
Stochastic subgradient for composite convex optimization with functional constraints. J. Mach. Learn. Res. 23: 265:1-265:35 (2022) - [j35]Ion Necoara, Olivier Fercoq:
Linear Convergence of Random Dual Coordinate Descent on Nonpolyhedral Convex Problems. Math. Oper. Res. 47(4): 2641-2666 (2022) - [j34]Ion Necoara:
Stochastic block projection algorithms with extrapolation for convex feasibility problems. Optim. Methods Softw. 37(5): 1845-1875 (2022) - [j33]Daniela Lupu, Ion Necoara, Joseph L. Garrett, Tor Arne Johansen:
Stochastic Higher-Order Independent Component Analysis for Hyperspectral Dimensionality Reduction. IEEE Trans. Computational Imaging 8: 1184-1194 (2022) - [c50]Flávia Chorobura, Daniela Lupu, Ion Necoara:
Coordinate projected gradient descent minimization and its application to orthogonal nonnegative matrix factorization. CDC 2022: 6929-6934 - [c49]Tudor C. Ionescu, Lahcen El Bourkhissi, Ion Necoara:
Least squares moment matching-based model reduction using convex optimization. ICSTCC 2022: 325-330 - [c48]Jon Álvarez Justo, Daniela Lupu, Milica Orlandic, Ion Necoara, Tor Arne Johansen:
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction. IVMSP 2022: 1-5 - [c47]Joseph L. Garrett, Nitesh Kumar Singh, Tor Arne Johansen, Ion Necoara:
Accelerating Support Vector Machines For Remote Platforms By Increasing Sparsity. WHISPERS 2022: 1-5 - 2021
- [j32]Ion Necoara, Angelia Nedic:
Minibatch stochastic subgradient-based projection algorithms for feasibility problems with convex inequalities. Comput. Optim. Appl. 80(1): 121-152 (2021) - [j31]Ion Necoara:
General Convergence Analysis of Stochastic First-Order Methods for Composite Optimization. J. Optim. Theory Appl. 189(1): 66-95 (2021) - [c46]Daniela Lupu, Ion Necoara:
Local linear convergence of stochastic higher-order methods for convex optimization. ECC 2021: 2219-2224 - [c45]Liliana Maria Ghinea, Ion Necoara, Marian Barbu:
Random coordinate descent methods for non-separable composite optimization. ECC 2021: 2225-2230 - 2020
- [j30]Tianxiao Sun, Ion Necoara, Quoc Tran-Dinh:
Composite convex optimization with global and local inexact oracles. Comput. Optim. Appl. 76(1): 69-124 (2020) - [j29]Ion Necoara, Tudor C. Ionescu:
$H_2$ Model Reduction of Linear Network Systems by Moment Matching and Optimization. IEEE Trans. Autom. Control. 65(12): 5328-5335 (2020) - [c44]Tudor C. Ionescu, Orest V. Iftime, Ion Necoara:
Optimal time-domain moment matching with partial placement of poles and zeros. ECC 2020: 1769-1774 - [c43]Xiaodong Cheng, Ion Necoara:
A suboptimal H2 clustering-based model reduction approach for linear network systems. ECC 2020: 1961-1966
2010 – 2019
- 2019
- [j28]Ion Necoara, Yurii E. Nesterov, François Glineur:
Linear convergence of first order methods for non-strongly convex optimization. Math. Program. 175(1-2): 69-107 (2019) - [j27]Ion Necoara, Andrei Patrascu, François Glineur:
Complexity of first-order inexact Lagrangian and penalty methods for conic convex programming. Optim. Methods Softw. 34(2): 305-335 (2019) - [j26]Ion Necoara, Peter Richtárik, Andrei Patrascu:
Randomized Projection Methods for Convex Feasibility: Conditioning and Convergence Rates. SIAM J. Optim. 29(4): 2814-2852 (2019) - [j25]Ion Necoara:
Faster Randomized Block Kaczmarz Algorithms. SIAM J. Matrix Anal. Appl. 40(4): 1425-1452 (2019) - [c42]Angelia Nedic, Ion Necoara:
Random minibatch projection algorithms for convex feasibility problems. CDC 2019: 1507-1512 - [c41]Ion Necoara:
Random gradient algorithms for convex minimization over intersection of simple sets. ECC 2019: 993-998 - [c40]Ion Necoara, Tudor C. Ionescu:
Parameter selection for best H2 moment matching-based model approximation through gradient optimization. ECC 2019: 2301-2306 - [c39]Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher:
Almost surely constrained convex optimization. ICML 2019: 1910-1919 - 2018
- [j24]Andrei Patrascu, Ion Necoara:
On the Convergence of Inexact Projection Primal First-Order Methods for Convex Minimization. IEEE Trans. Autom. Control. 63(10): 3317-3329 (2018) - [c38]Ion Necoara, Andrei Patrascu:
OR-SAGA: Over-relaxed stochastic average gradient mapping algorithms for finite sum minimization. ECC 2018: 489-494 - 2017
- [j23]Andrei Patrascu, Ion Necoara:
Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization. J. Mach. Learn. Res. 18: 198:1-198:42 (2017) - [j22]Ngoc Anh Nguyen, Sorin Olaru, Pedro Rodríguez-Ayerbe, Morten Hovd, Ion Necoara:
Constructive Solution of Inverse Parametric Linear/Quadratic Programming Problems. J. Optim. Theory Appl. 172(2): 623-648 (2017) - [j21]Ion Necoara, Yurii E. Nesterov, François Glineur:
Random Block Coordinate Descent Methods for Linearly Constrained Optimization over Networks. J. Optim. Theory Appl. 173(1): 227-254 (2017) - [j20]Andrei Patrascu, Ion Necoara, Quoc Tran-Dinh:
Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization. Optim. Lett. 11(3): 609-626 (2017) - 2016
- [j19]Ion Necoara, Andrei Patrascu:
Iteration complexity analysis of dual first-order methods for conic convex programming. Optim. Methods Softw. 31(3): 645-678 (2016) - [j18]Ion Necoara, Dragos N. Clipici:
Parallel Random Coordinate Descent Method for Composite Minimization: Convergence Analysis and Error Bounds. SIAM J. Optim. 26(1): 197-226 (2016) - [c37]Ion Necoara, Valentin Nedelcu, Dragos N. Clipici, Lucian Toma, Constantin Bulac:
Optimal voltage control for loss minimization based on sequential convex programming. ISGT Europe 2016: 1-6 - [c36]Andrei Patrascu, Ion Necoara:
Complexity certifications of inexact projection primal gradient method for convex problems: Application to embedded MPC. MED 2016: 125-130 - 2015
- [j17]Ion Necoara, Valentin Nedelcu:
On linear convergence of a distributed dual gradient algorithm for linearly constrained separable convex problems. Autom. 55: 209-216 (2015) - [j16]Andrei Patrascu, Ion Necoara:
Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization. J. Glob. Optim. 61(1): 19-46 (2015) - [j15]Ion Necoara:
Computational complexity certification for dual gradient method: Application to embedded MPC. Syst. Control. Lett. 81: 49-56 (2015) - [j14]Andrei Patrascu, Ion Necoara:
Random Coordinate Descent Methods for ℓ0 Regularized Convex Optimization. IEEE Trans. Autom. Control. 60(7): 1811-1824 (2015) - [c35]Ion Necoara, Rolf Findeisen:
Parallel and distributed random coordinate descent method for convex error bound minimization. ACC 2015: 527-532 - [c34]Ion Necoara:
Distributed and parallel random coordinate descent methods for huge convex programming over networks. CDC 2015: 425-430 - [c33]Ion Necoara, Andrei Patrascu:
On the behavior of first-order penalty methods for conic constrained convex programming when Lagrange multipliers do not exist. CDC 2015: 1378-1383 - [c32]Ion Necoara, Sverre Kvamme:
DuQuad: A toolbox for solving convex quadratic programs using dual (augmented) first order algorithms. CDC 2015: 2043-2048 - [c31]Andrei Patrascu, Ion Necoara, Rolf Findeisen:
Rate of convergence analysis of a dual fast gradient method for general convex optimization. CDC 2015: 3311-3316 - [c30]Andrei Patrascu, Ion Necoara:
Random Coordinate Descent Methods for Sparse Optimization: Application to Sparse Control. CSCS 2015: 909-914 - [c29]Ion Necoara, Angelia Nedich:
A fully distributed dual gradient method with linear convergence for large-scale separable convex problems. ECC 2015: 304-309 - 2014
- [j13]Ion Necoara, Andrei Patrascu:
A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints. Comput. Optim. Appl. 57(2): 307-337 (2014) - [j12]Dinh Quoc Tran, Ion Necoara, Moritz Diehl:
Path-following gradient-based decomposition algorithms for separable convex optimization. J. Glob. Optim. 59(1): 59-80 (2014) - [j11]Valentin Nedelcu, Ion Necoara, Quoc Tran-Dinh:
Computational Complexity of Inexact Gradient Augmented Lagrangian Methods: Application to Constrained MPC. SIAM J. Control. Optim. 52(5): 3109-3134 (2014) - [j10]Ion Necoara, Valentin Nedelcu:
Rate Analysis of Inexact Dual First-Order Methods Application to Dual Decomposition. IEEE Trans. Autom. Control. 59(5): 1232-1243 (2014) - [c28]Andrei Patrascu, Ion Necoara, Panagiotis Patrinos:
A proximal alternating minimization method for ℓ0-regularized nonlinear optimization problems: application to state estimation. CDC 2014: 4254-4259 - [c27]Ngoc Anh Nguyen, Sorin Olaru, Pedro Rodríguez-Ayerbe, Morten Hovd, Ion Necoara:
On the lifting problems and their connections with piecewise affine control law design. ECC 2014: 2164-2169 - 2013
- [j9]Dinh Quoc Tran, Ion Necoara, Carlo Savorgnan, Moritz Diehl:
An Inexact Perturbed Path-Following Method for Lagrangian Decomposition in Large-Scale Separable Convex Optimization. SIAM J. Optim. 23(1): 95-125 (2013) - [j8]Ion Necoara:
Random Coordinate Descent Algorithms for Multi-Agent Convex Optimization Over Networks. IEEE Trans. Autom. Control. 58(8): 2001-2012 (2013) - [c26]Ion Necoara, Dragos N. Clipici, Sorin Olaru:
Distributed model predictive control of leader-follower systems using an interior point method with efficient computations. ACC 2013: 1697-1702 - [c25]Ion Necoara, Valentin Nedelcu, Dragos N. Clipici:
Feasible distributed MPC scheme for network systems based on an inexact dual gradient method. ASCC 2013: 1-6 - [c24]Dinh Quoc Tran, Ion Necoara, Moritz Diehl:
A dual decomposition algorithm for separable nonconvex optimization using the penalty function framework. CDC 2013: 2372-2377 - [c23]Ion Necoara, Valentin Nedelcu, Tamás Keviczky, Minh Dang Doan, Bart De Schutter:
Linear model predictive control based on approximate optimal control inputs and constraint tightening. CDC 2013: 7728-7733 - [c22]Andrei Patrascu, Ion Necoara:
A random coordinate descent algorithm for large-scale sparse nonconvex optimization. ECC 2013: 2789-2794 - [c21]Ion Necoara, Dragos N. Clipici:
A computationally efficient parallel coordinate descent algorithm for MPC: Implementation on a PLC. ECC 2013: 3596-3601 - 2012
- [c20]Adrian-Bogdan Hanchevici, Ion Necoara:
Networked control strategies for a 3 dimensional crane. CCA 2012: 891-896 - [c19]Mihai Cornoiu, Ion Necoara:
An adaptive approximation method for Hammerstein systems identification. CCA 2012: 1002-1007 - [c18]Valentin Nedelcu, Ion Necoara:
Iteration complexity of an inexact augmented Lagrangian method for constrained MPC. CDC 2012: 650-655 - [c17]Ion Necoara:
A random coordinate descent method for large-scale resource allocation problems. CDC 2012: 4474-4479 - [c16]Ion Necoara:
Suboptimal distributed MPC based on a block-coordinate descent method with feasibility and stability guarantees. CDC 2012: 4480-4485 - 2010
- [j7]Paschalis Tsiaflakis, Ion Necoara, Johan A. K. Suykens, Marc Moonen:
Improved dual decomposition based optimization for DSL dynamic spectrum management. IEEE Trans. Signal Process. 58(4): 2230-2245 (2010) - [c15]Ion Necoara, Ioan Dumitrache, Johan A. K. Suykens:
Fast primal-dual projected linear iterations for distributed consensus in constrained convex optimization. CDC 2010: 1366-1371
2000 – 2009
- 2009
- [c14]Ion Necoara, Carlo Savorgnan, Dinh Quoc Tran, Johan A. K. Suykens, Moritz Diehl:
Distributed nonlinear optimal control using sequential convex programming and smoothing techniques. CDC 2009: 543-548 - [c13]Ion Necoara, Johan A. K. Suykens:
A dual interior-point distributed algorithm for large-scale data networks optimization. ECC 2009: 969-974 - [c12]Paschalis Tsiaflakis, Ion Necoara, Johan A. K. Suykens, Marc Moonen:
An improved dual decomposition approach to DSL dynamic spectrum management. EUSIPCO 2009: 2087-2091 - [c11]Ion Necoara:
Distributed Control over Networks Using Smoothing Techniques. ICANN (2) 2009: 835-844 - 2008
- [j6]Ion Necoara, Ton J. J. van den Boom, Bart De Schutter, Hans Hellendoorn:
Stabilization of max-plus-linear systems using model predictive control: The unconstrained case. Autom. 44(4): 971-981 (2008) - [j5]Ion Necoara, Bart De Schutter, Ton J. J. van den Boom, Hans Hellendoorn:
Model predictive control for uncertain max-min-plus-scaling systems. Int. J. Control 81(5): 701-713 (2008) - [j4]Michel Baes, Moritz Diehl, Ion Necoara:
Every Continuous Nonlinear Control System Can be Obtained by Parametric Convex Programming. IEEE Trans. Autom. Control. 53(8): 1963-1967 (2008) - [j3]Ion Necoara, Johan A. K. Suykens:
Application of a Smoothing Technique to Decomposition in Convex Optimization. IEEE Trans. Autom. Control. 53(11): 2674-2679 (2008) - [c10]Ion Necoara, Minh Dang Doan, Johan A. K. Suykens:
Application of the proximal center decomposition method to distributed model predictive control. CDC 2008: 2900-2905 - [c9]Ion Necoara, Johan A. K. Suykens:
A proximal center-based decomposition method for multi-agent convex optimization. CDC 2008: 3077-3082 - 2007
- [j2]Ion Necoara, Bart De Schutter, Ton J. J. van den Boom, Hans Hellendoorn:
Stable Model Predictive Control for Constrained Max-Plus-Linear Systems. Discret. Event Dyn. Syst. 17(3): 329-354 (2007) - [j1]Ion Necoara, Eric C. Kerrigan, Bart De Schutter, Ton J. J. van den Boom:
Finite-Horizon Min-Max Control of Max-Plus-Linear Systems. IEEE Trans. Autom. Control. 52(6): 1088-1093 (2007) - 2006
- [c8]Ion Necoara, Ton J. J. van den Boom, Bart De Schutter, Johannes Hellendoorn:
Stabilization of Max-plus-linear Systems using receding horizon control - the unconstrained Case. ADHS 2006: 148-153 - [c7]Ion Necoara, Bart De Schutter, Ton J. J. van den Boom, Johannes Hellendoorn:
Stable receding horizon control for max-plus-linear systems. ACC 2006: 1-6 - [c6]Daniele Corona, Ion Necoara, Bart De Schutter, Ton J. J. van den Boom:
Robust hybrid MPC applied to the design of an adaptive cruise controller for a road vehicle. CDC 2006: 1721-1726 - [c5]Ion Necoara, Eric C. Kerrigan, Bart De Schutter, Ton J. J. van den Boom:
Worst-case optimal control of uncertain max-plus-linear systems. CDC 2006: 6055-6060 - 2005
- [c4]Ion Necoara, Bart De Schutter, Ton J. J. van den Boom, Johannes Hellendoorn:
Robustly stabilizing MPC for perturbed PWL systems. CDC/ECC 2005: 3759-3764 - [c3]Ton J. J. van den Boom, Bart De Schutter, Ion Necoara:
On MPC for max-plus-linear systems: Analytic solution and stability. CDC/ECC 2005: 7816-7821 - 2004
- [c2]Ion Necoara, Bart De Schutter, Hans Hellendoorn:
On structural properties of Helbing's gas-kinetic traffic flow model. ACC 2004: 5508-5513 - [c1]Ion Necoara, Bart De Schutter, Ton J. J. van den Boom, Johannes Hellendoorn:
Model predictive control for perturbed continuous piecewise affine systems with bounded disturbances. CDC 2004: 1848-1853
Coauthor Index
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