Ion Necoara

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 62

Top Venues

ECC

CDC

Comput. Optim. Appl.

IEEE Trans. Autom. Control.

Publications

Flávia Chorobura, Ion Necoara
Coordinate descent methods beyond smoothness and separability. Comput. Optim. Appl. 88 (1) (2024)
Yassine Nabou, Ion Necoara
Efficiency of higher-order algorithms for minimizing composite functions. Comput. Optim. Appl. 87 (2) (2024)
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)
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 (2024)
Nitesh Kumar Singh, Ion Necoara
Unified Analysis of Stochastic Gradient Projection Methods for Convex Optimization with Functional Constraints. ECC (2024)
Daniela Lupu, Ion Necoara
Convergence analysis of stochastic higher-order majorization-minimization algorithms. Optim. Methods Softw. 39 (2) (2024)
Jon Álvarez Justo, Daniela Lupu, Milica Orlandic, Ion Necoara, Tor Arne Johansen
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction. CoRR (2024)
Flávia Chorobura, François Glineur, Ion Necoara
Can random proximal coordinate descent be accelerated on nonseparable convex composite minimization problems? ECC (2023)
Lahcen El Bourkhissi, Ion Necoara, Panagiotis Patrinos
Linearized ADMM for Nonsmooth Nonconvex Optimization with Nonlinear Equality Constraints. CDC (2023)
Daniela Lupu, Ion Necoara
Deep unfolding projected first order methods-based architectures: application to linear model predictive control. ECC (2023)
Yassine Nabou, Lucian Toma, Ion Necoara
Modified projected Gauss-Newton method for constrained nonlinear least-squares: application to power flow analysis. ECC (2023)
Liliana Maria Ghinea, Daniela Lupu, Marian Barbu, Ion Necoara
Control of a wastewater treatment process using linear and nonlinear model predictive control. ETFA (2023)
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)
Lionel Tondji, Dirk A. Lorenz, Ion Necoara
An accelerated randomized Bregman-Kaczmarz method for strongly convex linearly constraint optimization. ECC (2023)
Lionel Tondji, Ion Necoara, Dirk A. Lorenz
Acceleration and restart for the randomized Bregman-Kaczmarz method. CoRR (2023)
Flávia Chorobura, Ion Necoara
Random Coordinate Descent Methods for Nonseparable Composite Optimization. SIAM J. Optim. 33 (3) (2023)
Flávia Chorobura, Daniela Lupu, Ion Necoara
Coordinate projected gradient descent minimization and its application to orthogonal nonnegative matrix factorization. CDC (2022)
Ion Necoara, Olivier Fercoq
Linear Convergence of Random Dual Coordinate Descent on Nonpolyhedral Convex Problems. Math. Oper. Res. 47 (4) (2022)
Tudor C. Ionescu, Orest V. Iftime, Ion Necoara
Model reduction with pole-zero placement and high order moment matching. Autom. 138 (2022)
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 (2022)
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)
Ion Necoara, Nitesh Kumar Singh
Stochastic subgradient for composite convex optimization with functional constraints. J. Mach. Learn. Res. 23 (2022)
Joseph L. Garrett, N. K. Singh, Tor Arne Johansen, Ion Necoara
Accelerating Support Vector Machines For Remote Platforms By Increasing Sparsity. WHISPERS (2022)
Ion Necoara
Stochastic block projection algorithms with extrapolation for convex feasibility problems. Optim. Methods Softw. 37 (5) (2022)
Tudor C. Ionescu, Lahcen El Bourkhissi, Ion Necoara
Least squares moment matching-based model reduction using convex optimization. ICSTCC (2022)
Ion Necoara
General Convergence Analysis of Stochastic First-Order Methods for Composite Optimization. J. Optim. Theory Appl. 189 (1) (2021)
Daniela Lupu, Ion Necoara
Local linear convergence of stochastic higher-order methods for convex optimization. ECC (2021)
Ion Necoara, Angelia Nedic
Minibatch stochastic subgradient-based projection algorithms for feasibility problems with convex inequalities. Comput. Optim. Appl. 80 (1) (2021)
Liliana Maria Ghinea, Ion Necoara, Marian Barbu
Random coordinate descent methods for non-separable composite optimization. ECC (2021)
Tianxiao Sun, Ion Necoara, Quoc Tran-Dinh
Composite convex optimization with global and local inexact oracles. Comput. Optim. Appl. 76 (1) (2020)
Ion Necoara, Tudor C. Ionescu
$H_2$ Model Reduction of Linear Network Systems by Moment Matching and Optimization. IEEE Trans. Autom. Control. 65 (12) (2020)
Tudor C. Ionescu, Orest V. Iftime, Ion Necoara
Optimal time-domain moment matching with partial placement of poles and zeros. ECC (2020)
Xiaodong Cheng, Ion Necoara
A suboptimal H2 clustering-based model reduction approach for linear network systems. ECC (2020)
Ion Necoara, Tudor C. Ionescu
moment matching-based model approximation through gradient optimization. ECC (2019)
Angelia Nedic, Ion Necoara
Random minibatch projection algorithms for convex feasibility problems. CDC (2019)
Olivier Fercoq, Ahmet Alacaoglu, Ion Necoara, Volkan Cevher
Almost surely constrained convex optimization. ICML (2019)
Ion Necoara
Random gradient algorithms for convex minimization over intersection of simple sets. ECC (2019)
Ion Necoara, Andrei Patrascu, François Glineur
Complexity of first-order inexact Lagrangian and penalty methods for conic convex programming. Optim. Methods Softw. 34 (2) (2019)
Ion Necoara, Peter Richtárik, Andrei Patrascu
Randomized Projection Methods for Convex Feasibility: Conditioning and Convergence Rates. SIAM J. Optim. 29 (4) (2019)
Ion Necoara, Yurii E. Nesterov, François Glineur
Linear convergence of first order methods for non-strongly convex optimization. Math. Program. 175 (1-2) (2019)
Ion Necoara
Faster Randomized Block Kaczmarz Algorithms. SIAM J. Matrix Anal. Appl. 40 (4) (2019)
Andrei Patrascu, Ion Necoara
On the Convergence of Inexact Projection Primal First-Order Methods for Convex Minimization. IEEE Trans. Autom. Control. 63 (10) (2018)
Ion Necoara, Andrei Patrascu
OR-SAGA: Over-relaxed stochastic average gradient mapping algorithms for finite sum minimization. ECC (2018)
Andrei Patrascu, Ion Necoara
Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization. J. Mach. Learn. Res. 18 (2017)
Ion Necoara, Yurii E. Nesterov, François Glineur
Random Block Coordinate Descent Methods for Linearly Constrained Optimization over Networks. J. Optim. Theory Appl. 173 (1) (2017)
Andrei Patrascu, Ion Necoara, Quoc Tran-Dinh
Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization. Optim. Lett. 11 (3) (2017)
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) (2017)
Ion Necoara, Dragos N. Clipici
Parallel Random Coordinate Descent Method for Composite Minimization: Convergence Analysis and Error Bounds. SIAM J. Optim. 26 (1) (2016)
Andrei Patrascu, Ion Necoara
Complexity certifications of inexact projection primal gradient method for convex problems: Application to embedded MPC. MED (2016)
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)
Ion Necoara, Andrei Patrascu
Iteration complexity analysis of dual first-order methods for conic convex programming. Optim. Methods Softw. 31 (3) (2016)
Ion Necoara, Angelia Nedich
A fully distributed dual gradient method with linear convergence for large-scale separable convex problems. ECC (2015)
Andrei Patrascu, Ion Necoara
Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization. J. Glob. Optim. 61 (1) (2015)
Ion Necoara, Valentin Nedelcu
On linear convergence of a distributed dual gradient algorithm for linearly constrained separable convex problems. Autom. 55 (2015)
Andrei Patrascu, Ion Necoara
Random Coordinate Descent Methods for Sparse Optimization: Application to Sparse Control. CSCS (2015)
Ion Necoara
Computational complexity certification for dual gradient method: Application to embedded MPC. Syst. Control. Lett. 81 (2015)
Andrei Patrascu, Ion Necoara
Regularized Convex Optimization. IEEE Trans. Autom. Control. 60 (7) (2015)
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)
Andrei Patrascu, Ion Necoara, Rolf Findeisen
Rate of convergence analysis of a dual fast gradient method for general convex optimization. CDC (2015)
Ion Necoara, Sverre Kvamme
DuQuad: A toolbox for solving convex quadratic programs using dual (augmented) first order algorithms. CDC (2015)
Ion Necoara, Rolf Findeisen
Parallel and distributed random coordinate descent method for convex error bound minimization. ACC (2015)
Ion Necoara
Distributed and parallel random coordinate descent methods for huge convex programming over networks. CDC (2015)
Andrei Patrascu, Ion Necoara, Panagiotis Patrinos
A proximal alternating minimization method for ℓ0-regularized nonlinear optimization problems: application to state estimation. CDC (2014)
Ion Necoara, Valentin Nedelcu
Rate Analysis of Inexact Dual First-Order Methods Application to Dual Decomposition. IEEE Trans. Autom. Control. 59 (5) (2014)
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) (2014)
Dinh Quoc Tran, Ion Necoara, Moritz Diehl
Path-following gradient-based decomposition algorithms for separable convex optimization. J. Glob. Optim. 59 (1) (2014)
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) (2014)
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)
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) (2013)
Dinh Quoc Tran, Ion Necoara, Moritz Diehl
A dual decomposition algorithm for separable nonconvex optimization using the penalty function framework. CDC (2013)
Ion Necoara, Valentin Nedelcu, Dragos N. Clipici
Feasible distributed MPC scheme for network systems based on an inexact dual gradient method. ASCC (2013)
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)
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)
Ion Necoara
Random Coordinate Descent Algorithms for Multi-Agent Convex Optimization Over Networks. IEEE Trans. Autom. Control. 58 (8) (2013)
Andrei Patrascu, Ion Necoara
A random coordinate descent algorithm for large-scale sparse nonconvex optimization. ECC (2013)
Ion Necoara, Dragos N. Clipici
A computationally efficient parallel coordinate descent algorithm for MPC: Implementation on a PLC. ECC (2013)
Ion Necoara
A random coordinate descent method for large-scale resource allocation problems. CDC (2012)
Ion Necoara
Suboptimal distributed MPC based on a block-coordinate descent method with feasibility and stability guarantees. CDC (2012)
Adrian-Bogdan Hanchevici, Ion Necoara
Networked control strategies for a 3 dimensional crane. CCA (2012)
Mihai Cornoiu, Ion Necoara
An adaptive approximation method for Hammerstein systems identification. CCA (2012)
Valentin Nedelcu, Ion Necoara
Iteration complexity of an inexact augmented Lagrangian method for constrained MPC. CDC (2012)
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) (2010)
Ion Necoara, Ioan Dumitrache, Johan A. K. Suykens
Fast primal-dual projected linear iterations for distributed consensus in constrained convex optimization. CDC (2010)
Ion Necoara
Distributed Control over Networks Using Smoothing Techniques. ICANN (2) (2009)
Ion Necoara, Johan A. K. Suykens
A dual interior-point distributed algorithm for large-scale data networks optimization. ECC (2009)
Paschalis Tsiaflakis, Ion Necoara, Johan A. K. Suykens, Marc Moonen
An improved dual decomposition approach to DSL dynamic spectrum management. EUSIPCO (2009)
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)
Ion Necoara, Johan A. K. Suykens
A proximal center-based decomposition method for multi-agent convex optimization. CDC (2008)
Ion Necoara, Minh Dang Doan, Johan A. K. Suykens
Application of the proximal center decomposition method to distributed model predictive control. CDC (2008)
Michel Baes, Moritz Diehl, Ion Necoara
Every Continuous Nonlinear Control System Can be Obtained by Parametric Convex Programming. IEEE Trans. Autom. Control. 53 (8) (2008)
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) (2008)
Ion Necoara, Johan A. K. Suykens
Application of a Smoothing Technique to Decomposition in Convex Optimization. IEEE Trans. Autom. Control. 53 (11) (2008)
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) (2008)
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) (2007)
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) (2007)
Ion Necoara, Bart De Schutter, Ton J. J. van den Boom, Johannes Hellendoorn
Stable receding horizon control for max-plus-linear systems. ACC (2006)
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)
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)
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)
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)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ion Necoara