Department of
Automatic Control and Systems Engineering

Română (Romanian)

Professor, PhD
Ion NECOARĂ

Professor, Ph.D. advisor

Email:ion dot necoara at acse dot pub dot ro

Office:ED 204

Interior:9195

Courses




Courses         Biography         Publications         Projects         Talks         Software


Numerical Methods course support
Optimization Techniques course support









Short Biography (Full CV)

Higher education:
  • Habilitation thesis defense, Coordinate Descent Methods for Sparse Optimization, November 2014
  • PhD in Applied Mathematics (Cum Laude) from Delft Center for Systems and Control, Technical University Delft, Netherlands (2002 - 2006)
  • Master in Optimization and Statistics, Faculty of Mathematics, University Bucharest (2000 - 2002)
  • Bachelor in Mathematics, Faculty of Mathematics, University Bucharest (1996 - 2000)

Academic positions:
  • 2015 - present: PhD Advisor in Systems Engineering field
  • 2015 - present: Professor of Numerical Methods and Optimization, Faculty of Automatic Control and Computers, University Politehnica Bucharest (UPB)
  • 2012 - 2015: Associate Professor, Faculty of Automatic Control and Computers, UPB
  • 2009 - 2012: Assistant Professor, Faculty of Automatic Control and Computers, UPB
  • 2007 - 2009: Post-doctoral fellowship at KU Leuven, Belgium

Research activities:
  • Head of Distributed Optimization and Control Group (DOC) (website).
  • Supervised 4 PhD students and several MSc students.
  • Visiting lecturer or invited professor to a number of universities including Univ. Catholique Louvain (Belgium), TU Delft (Netherlands), ETH Zurich (Switzerland), Lund University (Sweden), SUPELEC (France), KU Leuven (Belgium), University Cambridge (UK), IMT Lucca (Italy).
  • Author of about 100 research papers: 2 books, 30 papers in ISI journals, 7 book chapters, and about 60 papers in refereed international conferences.
  • Book: Model predictive control for hybrid systems: piecewise affine and max-plus-linear systems, VDM.
  • Member of IFAC Committee on Optimal Control and in IPC of various international conferences.
  • Involved in several EU-FP7 projects: EMBOCON (principal investigator), HD-MPC (member), HYCON (member); National projects: METNET, MoCOBiDS (principal investigator).
  • Awarded The National Authority for Scientific Research Fellowship (Young Independent Research Team Fellowship, 2010-2013 & 2015-2017) and University Fellowship (UPB, Excellence in Research Fellowship, 2010-2013).
  • Best Paper Award at the Int. Conference on Systems Theory, Control and Computing 2014.
  • Romanian Academy Award in Mathematical Sciences & Information Technology: Grigore Moisil award, 2015 (for the papers published in 2013).

Main current fields of interest:
  • Theory and methods for Convex/Distributed/Big Data Optimization.
  • Developing optimization algorithms with a focus on structure exploiting.
  • Mathematical guarantees about performance of numerical optimization algorithms.
  • Applying optimization techniques for developing new advanced controller design algorithms for complex systems (Embedded and Distributed Control/MPC).
  • Practical applications include: Big Data Models (smart electricity grids, traffic networks, weather forecasts, distributed control, compressive sensing, image/signal processing, machine learning), Embedded Control, Control of Robots, Automotive Industry.

My Phd students at UPB:
  • I am always looking for talented and self-motivated students that want to perform research within the broad areas of optimization, decision and information systems, 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. Dragos Clipici (2012-...): Distributed gradient methods over graphs.
  • dr. Andrei Patrascu (2012-2015): Efficient first order methods for sparse convex optimization (phd thesis). Postdoctoral Researcher at UPB, Romania.
  • dr. Valentin Nedelcu (2010-2013): Rate analysis of dual gradient methods (phd thesis). Research Scientist at Assystem, Romania.




Recent publications  

2016
  • I. Necoara, A. Patrascu, P. Richtarik, Randomized projection methods for convex feasibility problems, Working paper, July 2016 (pdf).
  • I. Necoara, Yu. Nesterov, F. Glineur, Random block coordinate descent methods for linearly constrained optimization over networks , Journal of Optimization Theory and Applications, 2016, (arxiv).
  • A. Patrascu, I. Necoara, Q. Tran-Dinh, Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization , Optimization Letters, 2016, (arxiv).
  • N.A. Nguyen, S. Olaru, P. Rodriguez-Ayerbe, M. Hovd, I. Necoara, Constructive solution of inverse parametric linear/quadratic programming problems, Journal of Optimization Theory and Applications, 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 (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, (arxiv).
  • Q. Tran Dinh, I. Necoara, M. Diehl, Fast Inexact Decomposition Algorithms For Large-Scale Separable Convex Optimization, Optimization, 65(2): 325–356, 2016, (arxiv).

2015
  • I. Necoara, Yu. Nesterov, F. Glineur, Linear convergence of first order methods for non-strongly convex optimization, submitted, 2015, (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, submitted, 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, (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. & Methods, 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.
  • 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 Transactions on 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)

5. UEFISCDI, Human Resources: MoCOBiDS (Modelling, Control and Optimization for Big Data Systems), 2015-2017, see website for this ongoing project.
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, website.
2. ANCS, PNII - Capacitati: EMBOCON (Embedded Optimization for Resource Constrained Platforms), 2010-2012
1. UEFISCDI, Human Resources: METNET (Mathematical Tools for Network Systems), 2010-2013.



Some talks

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

QP solver (DuQuad): C code toolbox for solving convex QPs with dual first order methods - download

Parallel Optimization Toolbox (POPT): C code toolbox for solving large-scale structured QPs - download


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