Email: ion dot necoara at upb dot ro
Office: ED 204
I am recruiting 1 PhD candidate supported by EU-H2020-MSCA-ITN project:
- Project ELO-X (2021-2024) (specifically for ESR 9), to work on combining Optimization and Learning techniques for Model Predictive Control (press here for complete description).
This is a full position and the salary will be according to European Commission funding rate. You can get more information about salary and ITNs in Information Note for ITN Fellows.
I am looking for candidates with strong mathematical and computations skills (optimization, linear algebra, control) to work on theoretical and algorithmic projects at the interface between optimization, machine learning and control. The position is for 3 years and it is available immediately (before July 2021). Interested candidates are invited to send a CV, short abstract of their master thesis and the name of two references by email to me at: firstname.lastname@example.org.
Eligibility criteria: Candidates must – at the date of recruitment – (i) have obtained the MSc degree entitling you to embark on a doctorate; (ii) be within the first four years of your research career and not have a doctoral degree; (iii) candidates must not have resided or carried out your main activity (work, studies, etc.) in Romania for more than 12 months in the 3 years immediately prior to the recruitment date.
Numerical Methods for Big Data
- 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
- 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
- Author of +130 research papers with +2300 citations and h-index 26 on Google Scholar out of which +90 ISI research papers with +800 citations and h-index 16 on Web of Science.
- Member of editorial board of 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).
- 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.
- 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.
- 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).
- 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.
- I. Necoara, Decomposition Methods for Large Scale Mathematical Optimization, in progress, 2019.
- I. Necoara, Model predictive control for hybrid systems: piecewise affine and max-plus-linear systems, VDM, 2008.
- D. Lupu, I. Necoara, Stochastic higher-order majorization-minimization algorithms, work in progress, 2020.
- 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, 2021, (arxiv).
- 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 solving convex inequalities, 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), 1--8, 2020), (arxiv).
- O. Fercoq, A. Alacaoglu, I. Necoara, V. Cevher, Almost surely constrained convex optimization, January 2019, International Conference on Machine Learning (ICML). (arxiv).
- 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 (to appear in IMA Journal of Numerical Analysis, 2020) (arxiv).
- T. Sun, I. Necoara, Q. Tran-Dinh, Composite Convex Optimization with Global and Local Inexact Oracles, July 2018 (to appear in Computational Optimization and Applications, 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).
- 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).
- 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).
- 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).
- 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).
- 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).
- 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.
TRAINING DATA-DRIVEN EXPERTS IN OPTIMIZATION (TRADE-OPT)
EU H2020/Marie-Curie ITN-ETN, Grant Agreement nr. 861137
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).
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.
- Toolbox for Support Vector Machine (PD-SVM): Python code optimization toolbox for solving large-scale SVM problems download