Department of
Automatic Control and Systems Engineering

Română (Romanian)

Professor Ion NECOARĂ

Professor, Ph.D. advisor

Email: ion dot necoara at upb dot ro

Office: ED 204

Interior: 9195

Courses


Courses     Biography     Publications     Projects     Talks     Software
Project ELO-X (EU-H2020-MSCA-ITN)
Project TraDE-Opt (EU-H2020-MSCA-ITN)
Project ELO-Hyp (NO 2014-2021)
Numerical Methods for Big Data
Optimization Techniques for Machine Learning
 
Short Biography (Full CV)
Higher education:
  • July 2021, Habilitation thesis, ISMMA, Stochastic Optimization
  • Nov. 2014, Habilitation thesis, UPB, Coordinate Descent Methods for Sparse Optimization (pdf)
  • 2002 - 2006: PhD in Applied Mathematics (Cum Laude) from Technical University Delft (NL)
  • 2000 - 2002: Master in Optimization and Statistics, Faculty of Mathematics, University Bucharest
  • 1996 - 2000: Diploma in Mathematics, Faculty of Mathematics, University Bucharest
  Academic positions:
  • 2021 - : Senior Researcher I, Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy ( ISMMA)
  • 2021 - : PhD Advisor in Mathematics (see attached some phd thesis proposals)
  • 2015 - : PhD Advisor in Systems Engineering (see attached some phd thesis proposals)
  • 2015 - : Professor of Numerical Methods & Optimization, Fac. Automatic Control & Computers, UPB
  • 2012 - 2015: Associate Professor, Faculty of Automatic Control & Computers, UPB
  • 2009 - 2012: Assistant Professor, Faculty of Automatic Control & Computers, UPB
  • 2007 - 2009: Post-doctoral fellowship at KU Leuven, Belgium
  Research activities:
  • Author of +130 research papers with +2400 citations and h-index 26 on Google Scholar out of which +90 ISI research papers with +800 citations and h-index 17 on Web of Science.
  • Member of editorial board of IEEE Transactions on Automatic Control (IEEE); EURO Journal on Computational Optimization (Elsevier); Mathematics (mdpi); System Theory, Control and Computing Journal.
  • Involved as principal investigator in several EU projects: TraDE-OPT (H2020), ELO-X (H2020), EMBOCON (FP7); national projects (Uefiscdi): ELO-Hyp, ScaleFreeNet, MoCOBiDS, METNet.
  • Head of Optimization, Learning and Control Group (OLC) (see website).
  • Supervise 8 PhD students and several MSc & bachelor students.
  • Invited professor to top universities including: MIT, Cornell Univ., Lehigh Univ., UNC (USA); Univ. Catholique Louvain and KU Leuven (Belgium); Edinburgh Univ. and Cambridge Univ. (UK); TU Delft (Netherlands); ETH Zurich and EPFL (Switzerland); Lund Univ. and Linkoping Univ. (Sweden); SUPELEC (France); NTNU (Norway); IMT Lucca (Italy); OVGU (Germany); KAUST (Saudi Arabia).
  Awards:
  • Romanian representative on General Assembly of European Control Association (EUCA), 2020-
  • National Order Faithful Service from Romanian Presidency, 2017.
  • Fulbright Visiting Professor fellowship at UNC (USA), 2017.
  • Excellence in Research award in Engineering Sciences, awarded by Ad Astra, 2016.
  • Romanian Academy Award in Mathematical Sciences & Information Technology (Gr. Moisil), 2015.
  • Best Paper Award for a paper published in Journal of Global Optimization in 2015.
  • Best Paper Award at International Conference on Systems Theory, Control and Computing 2014.
  • Awarded UEFISCDI Fellowship (Young Independent Research Team Fellowship, 2010-2013 & 2015-2017). University Fellowship (UPB, Excellence in Research Fellowship, 2010-2013).
  • Member of IFAC Committee on Optimal Control and in IPC of various international conferences.
  Main current fields of interest:
  • Theory and methods for Convex/Distributed/Big Data/Stochastic Optimization.
  • Developing optimization algorithms with a focus on structure exploiting (sparsity, convexity, stochasticity, low-rank, parallel and distributed computations).
  • Mathematical guarantees about performance of numerical optimization algorithms.
  • Optimization techniques for Machine Learning problems.
  • Develop new advanced Controller design algorithms for complex systems (Embedded and Distributed Control/MPC).
  • Practical applications include: Big Data Models (Data Analytics, Machine Learning, Weather Forecasts, Smart Electricity Grids, Traffic Networks, Distributed Control, Compressive Sensing, Image/Signal processing), Embedded Control, Control of Robots, Automotive Industry.
  Phd Thesis Proposals:
  • See attached some list of phd thesis proposals.
  • I am always looking for talented and self-motivated phd students that want to perform research within the broad areas of Optimization, Big Data Analytics and Control. You will receive competitive benefits and work at international standards. We support visits to strong research groups from Europe, conference travels and interactions with the best researchers in the field.
  • Students interested in doing some research/practical projects can also contact me.
  • For anyone interested, here are some tips on how read a scientific paper (by Mitzenmacher) (pdf).
  My phd students at UPB:
  • drd. Flavia Chorobura (2020-): Scalable optimization algorithms for huge-scale problems.
  • drd. Yassine Nabou (2020-): Efficient optimization algorithms for complex systems.
  • drd. Nitesh Kumar Singh (2020-): Efficient stochastic optimization algorithms.
  • drd. Daniela Lupu (2019-): Higher-order stochastic optimization methods.
  • drd. Liliana Ghinea (2019-, in cotutela): Advanced control techniques for complex systems.
  • dr. Andrei Patrascu (2012-2015): Efficient first order methods for sparse convex optimization (phd thesis). prof. Univ. Bucharest.
  • dr. Valentin Nedelcu (2010-2013): Rate analysis of dual gradient methods (phd thesis). Researcher.
  Books:
Recent publications
2021
  • I. Necoara, N.K. Singh, Stochastic subgradient projection methods for composite optimization with functional constraints, August 2021 (arxiv).
  • I. Necoara, F. Chorobura, Efficiency of stochastic coordinate proximal gradient methods on nonseparable composite optimization, April 2021 (arxiv).
2020
  • D. Lupu, I. Necoara, Convergence analysis of stochastic higher-order majorization-minimization algorithms, December 2020 (arxiv).
  • I. Necoara, D. Lupu, General higher-order majorization-minimization algorithms for (non)convex optimization, October 2020 (arxiv).
  • T. Ionescu, O. Iftime, I. Necoara, Model reduction with pole-zero placement and matching of derivatives, March 2020, (arxiv).
  • I. Necoara, General convergence analysis of stochastic first order methods for composite optimization, Journal of Optimization Theory and Applications, doi: 10.1007/s10957-021-01821-2, 2021, (arxiv).
2019
  • I. Necoara, O. Ferqoc, Linear convergence of dual coordinate descent on non-polyhedral convex problems, November 2019, (arxiv).
  • I. Necoara, A. Nedich, Minibatch stochastic subgradient-based projection algorithms for feasibility problems with convex inequalities, Computational Optimization and Applications, doi: 10.1007/s10589-021-00294-3, 2021, (September 2019), (arxiv).
  • I. Necoara, Random minibatch projection methods with extrapolation for convex feasibility problems, June 2019, (arxiv)
  • I. Mezghani, Q. Tran-Dinh, I. Necoara, A. Papavasiliou, A globally convergent Gauss-Newton algorithm for AC optimal power flow, May 2019, (arxiv).
  • A. Nedich, I. Necoara, Random minibatch projection algorithms for convex problems with functional constraints, March 2019 (Applied Mathematics and Optimization, 8(3): 801--833, 2019), (arxiv).
  • I. Necoara, Faster randomized block Kaczmarz algorithms, March 2019 (Siam Journal on Matrix Analysis and Applications, 40(4), 1425--1452, 2019). (arxiv).
  • I. Necoara, T. Ionescu, H2 model reduction of linear network systems by moment matching and optimization, February 2019 (IEEE Transactions on Automatic Control, 65(12): 5328-5335, 2020), (arxiv).
  • O. Fercoq, A. Alacaoglu, I. Necoara, V. Cevher, Almost surely constrained convex optimization, January 2019, International Conference on Machine Learning (ICML). (arxiv).
2018
  • I. Necoara, T. Ionescu, Optimal H2 moment matching-based model reduction for linear systems by (non)convex optimization, November 2018 (arxiv).
  • I. Necoara, M. Takac, Randomized sketch descent methods for non-separable linearly constrained optimization, July 2018 (IMA Journal of Numerical Analysis, 41(2): 1056-1092, 2021) (arxiv).
  • T. Sun, I. Necoara, Q. Tran-Dinh, Composite Convex Optimization with Global and Local Inexact Oracles, July 2018 (Computational Optimization and Applications, 76(1): 69-124, 2020) (arxiv).
  • I. Necoara, P. Richtarik, A. Patrascu, Randomized projection methods for convex feasibility problems: conditioning and convergence rates, Siam Journal on Optimization, 29(4): 2814-2852, 2019 (arxiv).
  • A. Patrascu, I. Necoara, Nonasymptotic convergence of stochastic proximal point algorithms for constrained convex optimization, Journal of Machine Learning Research, 18(198): 1−42, 2018. (pdf).
  • A. Patrascu, I. Necoara, On the convergence of inexact projection first order methods for convex minimization , IEEE Transactions on Automatic Control, 63(10): 3317--3329, 2018. (pdf).
  2017
  • I. Necoara, Coordinate gradient descent methods, chapter in book: Big Data and Computational Intelligence in Networking, Y. Wu et al. (Eds.), Taylor & Francis LLC - CRC Press, 2017 (pdf).
  • I. Necoara, Yu. Nesterov, F. Glineur, Random block coordinate descent methods for linearly constrained optimization over networks, Journal Optimization Theory Applications, 173(1): 227--254, 2017, (pdf arxiv).
  • A. Patrascu, I. Necoara, Q. Tran-Dinh, Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization , Optimization Letters, 11(3): 609–-626, 2017, (pdf arxiv).
  • N.A. Nguyen, S. Olaru, P. Rodriguez-Ayerbe, M. Hovd, I. Necoara, Constructive solution of inverse parametric linear/quadratic programming problems, Journal Optimization Theory Applications, 172(2): 623–648, 2017 (pdf).
  2016
  • I. Necoara, D. Clipici, Parallel random coordinate descent methods for composite minimization: convergence analysis and error bounds, SIAM Journal on Optimization, 26(1): 197-226, 2016 (pdf arxiv).
  • I. Necoara, A. Patrascu, Iteration complexity analysis of dual first order methods for conic convex programming, Optimization Methods and Software, 31(3):645-678, 2016, (pdf arxiv).
  • Q. Tran Dinh, I. Necoara, M. Diehl, Fast Inexact Decomposition Algorithms For Large-Scale Separable Convex Optimization, Optimization, 65(2): 325–356, 2016, (pdf arxiv).
  2015
  • I. Necoara, Yu. Nesterov, F. Glineur, Linear convergence of first order methods for non-strongly convex optimization, Mathematical Programming, 175(1): 69--107, 2019 (updated version here: pdf), (or on arxiv).
  • I. Necoara, A. Patrascu, F. Glineur, Complexity certifications of first order inexact Lagrangian and penalty methods for conic convex programming, Optimization Methods and Software, 2015, (arxiv).
  • I. Necoara, A. Patrascu, A. Nedich, Complexity certifications of first order inexact Lagrangian methods for general convex programming, chapter in Developments in Model-Based Optimization and Control, Springer, 2015, (arxiv).
  • A. Patrascu, I. Necoara, Random coordinate descent methods for l0 regularized convex optimization, IEEE Transactions on Automatic Control, 60(7):1811–-1824, 2015, (arxiv).
  • A. Patrascu, I. Necoara, Efficient random coordinate descent algorithms for large-scale structured nonconvex optimization, Journal of Global Optimization, 61(1):19--46, 2015 (received Best Paper Award for a paper published in Journal of Global Optimization in 2015), (arxiv).
  • I. Necoara, V. Nedelcu, On linear convergence of a distributed dual gradient algorithm for linearly constrained separable convex problems, Automatica J., 55(5):209–-216, 2015, (arxiv).
  • I. Necoara, L. Ferranti, T. Keviczky, An adaptive constraint tightening approach to linear MPC based on approximation algorithms for optimization, Optimal Control Appl. & Met., 36(5):648–-666, 2015, (pdf).
  • I. Necoara, Computational complexity certification for dual gradient method: application to embedded MPC, Systems and Control Letters, 81(7):49–56, 2015 (pdf).
  • I. Necoara, A. Patrascu, DuQuad: an inexact (augmented) dual first order algorithm for quadratic programming, Tech. Rep., UPB, 2015, (arxiv).
  2014
  • I. Necoara, V. Nedelcu, Rate analysis of inexact dual first order methods: application to dual decomposition, IEEE Transactions on Automatic Control, 59(5): 1232 - 1243, 2014, (arxiv).
  • I. Necoara, A. Patrascu, A random coordinate descent algorithm for optimization problems with composite objective function and linear coupled constraints, Computational Optim. & Applications, 57(2): 307-337, 2014, (arxiv).
  • Q. Tran Dinh, I. Necoara, M. Diehl, Path-Following Gradient-Based Decomposition Algorithms For Separable Convex Optimization, Journal of Global Optimization, 59(1): 59-80, 2014, (arxiv).
  • V. Nedelcu, I. Necoara, Q. Tran Dinh, Computational Complexity of Inexact Gradient Augmented Lagrangian Methods: Application to Constrained MPC, SIAM J. Control and Optimization, 52(5): 3109-3134, 2014, (pdf).
  2013
  • I. Necoara, Yu. Nesterov, F. Glineur, A random coordinate descent method on large-scale optimization problems with linear constraints, Tech. rep, UPB, 2011 (ICCOPT 2013, Lisbon), (pdf).
  • I. Necoara, Random coordinate descent algorithms for multi-agent convex optimization over networks, IEEE Transactions on Automatic Control, 58(8): 2001-2012, 2013, (pdf).
  • Q. Tran Dinh, I. Necoara, C. Savorgnan, M. Diehl, An inexact Perturbed Path-Following Method for Lagrangian Decomposition in Large-Scale Separable Convex Optimization, SIAM J. Optimization, 23(1): 95-125, 2013, (pdf).
  • I. Necoara, D. Clipici, Efficient parallel coordinate descent algorithm for convex optimization problems with separable constraints: application to distributed MPC, Journal of Process Control, 23(3): 243--253, 2013, (pdf).
  • I. Necoara, V. Nedelcu, Distributed dual gradient methods and error bound conditions, Tech. rep., 2013, (pdf).
  Some old papers
  • I. Necoara, V. Nedelcu, I. Dumitrache, Parallel and distributed optimization methods for estimation and control in networks, Journal of Process Control, 21(5): 756 – 766, 2011, (pdf).
  • P. Tsiaflakis, I. Necoara, J.A.K. Suykens, M. Moonen, Improved dual decomposition based optimization for DSL dynamic spectrum management, IEEE Trans. Signal Processing, 58(4): 2230--2245, 2010.
  • I. Necoara, J. Suykens, An interior-point Lagrangian decomposition method for separable convex optimization, Journal of Optimization Theory and Applications, 143(3): 567–588, 2009, (pdf).
  • I. Necoara, J. Suykens, Application of a smoothing technique to decomposition in convex optimization, IEEE Transactions on Automatic Control, 53(11): 2674--2679, 2008, (pdf).
  • M. Baes, M. Diehl, I. Necoara, Every nonlinear control system can be obtained by parametric convex programming, IEEE Transactions on Automatic Control, 53(8): 1963--1967, 2008.
Research Projects (principal investigator)
 
  • 7. EU-H2020, Marie Curie - Innovative Training Networks: TRADE-OPT, 2020-2024, see website for this ongoing project.
  • 6. UEFISCDI, PCE: ScaleFreeNet (Scale-free modeling and optimization techniques for control of complex networks), 2017-2019.
  • 5. UEFISCDI, Human Resources: MoCOBiDS (Modelling, Control and Optimization for Big Data Systems), 2015-2017.
  • 4. WBI Belgium-Romanian Academy: Programme de cooperation scientifique entre L’Academie roumaine, WBI et le FRS/FNRS, 2016-2018.
  • 3. EU, FP7, ICT-STREP: EMBOCON (Embedded Optimization for Resource Constrained Platforms), 2010 - 2013.
  • 2. ANCS, PNII: EMBOCON (Embedded Optimization for Resource Constrained Platforms), 2010-2012.
  • 1. UEFISCDI, Human Resources: METNET (Mathematical Tools for Network Systems), 2010-2013.
    • Some talks
  • 8. ICSTCC 2018- International Conference on System Theory, Control and Computing, Sinaia, October 2018, Optimization in control: recent advances and challenges.
  • 7. EMBOPT 2014 - Workshop on embedded optimization, Lucca, September 2014, Iteration complexity analysis of dual first order methods, (pdf).
  • 6. HYCON2 Workshop on Distributed Optimization in Large Networks and its Applications, Zurich, July 2013, Coordinate descent methods for huge scale problems, (pdf).
  • 5. IMT Lucca, 2013, Rate analysis of inexact dual gradient methods: application to embedded and distributed MPC, (pdf).
  • 4. ACSE - Univ. Politehnica Bucharest, December 2012, Decomposition methods for large-scale convex problems: applications in engineering, (pdf).
  • 3. ETH Zurich, Oct. 2010, Distributed optimization methods for estimation and control in networks, (pdf).
  • 2. Lund University, May 2010, Smoothing Techniques for Distributed Control over Networks, (pdf).
  • 1. Supelec, ETH Zurich, 2008, Robust control of a class of hybrid systems, (pdf).
    • Software
  • proxDykstra-SOCP: Python code optimization toolbox for solving SOCP problems using proximal point and Dykstra algorithms (see Github).
  • 2-RCD: Python code optimization toolbox for structured quadratic programs using 2 random coordinates descent method and applications to SVM (see Github).
  • QP solver (DuQuad): C code toolbox for solving convex QPs with dual first order methods (see Github).
  • Primal-Dual Toolbox for SVM (PD-SVM): Matlab code toolbox for solving large-scale SVM problems - download
  • Parallel Optimization Toolbox (POPT): C code toolbox for solving large-scale structured QPs - download

    • Project TraDE-Opt (2020-2024)

    TRAINING DATA-DRIVEN EXPERTS IN OPTIMIZATION (TRADE-OPT)

    EU H2020/Marie-Curie ITN-ETN, Grant Agreement nr. 861137

    Abstract: TRADE-Opt offers 15 PhD positions (ESRs) within an innovative training program giving a solid mathematical background in (convex) optimization and data driven modeling combined with employability skills: management, fund rising, communication, and carrier planning skills. The training will be based on three modules: learning by research, learning by courses, learning by doing. Embedded in the TraDe-OPT's training environment, each ESR will be developing an individual research project comprising significant advances in designing efficient algorithmic solutions and a related industry backed project.

    Consortium: Università di Genova, CentraleSupélec, Technische Universität Braunschweig, Universität Graz, Universitatea Politehnica Bucuresti, Instytut Badan Systemowych PAN, Université catholique de Louvain, Camelot. See TraDe-Opt website.

    UPB project team:
      Prof. Ion Necoara, 2 PhD students: Flavia Chorobura and Yassine Nabou.

    UPB PhD/ESR positions:
    • ESR 9 - Scalable optimization algorithms for huge-scale optimization problems (details).
    • ESR 10 - Efficient decision-making (modelling and control) for complex network systems (details).
     

    Publications

    Papers accepted/submitted in ISI Journals

    • [J3] I. Necoara, D. Lupu, General higher-order majorization-minimization algorithms for (non)convex optimization, 2020.
    • [J2] I. Necoara, General convergence analysis of stochastic first order methods for composite optimization, 2020.
    • [J1] T. Ionescu, O. Iftime, I. Necoara, Model reduction with pole-zero placement and matching of derivatives, 2020.

    Papers accepted in conferences

    • [C1] I. Necoara, .....

    Papers in progress

    • [P1] X. Cheng, I. Necoara, A suboptimal H2 clustering-based model reduction approach for linear network systems, 2020.

    Software Packages

    • Toolbox for Support Vector Machine (PD-SVM): Python code optimization toolbox for solving large-scale SVM problems download
    Visits: