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

Project TE MoCOBiDS (UEFISCDI)



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.
  • 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).

Awards:
  • Awarded UEFISCDI Fellowship (Young Independent Research Team Fellowship, 2010-2013 & 2015-2017) and University Fellowship (UPB, Excellence in Research Fellowship, 2010-2013).
  • Best Paper Award at International Conference on Systems Theory, Control and Computing 2014.
  • Best Paper Award for a paper published in Journal of Global Optimization in 2015.
  • Romanian Academy Award in Mathematical Sciences & Information Technology: Grigore Moisil award, 2015 (for the papers published in 2013).
  • Excellence in Research Award in Engineering Sciences, top prize awarded every two years by Ad Astra, 2016.

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.

  • Books:



Recent publications  

2016
  • I. Necoara, A. Patrascu, P. Richtarik, Randomized projection methods for convex feasibility problems, Working paper, July 2016 (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, 2016.
  • 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 (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. & 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


Project TE MoCOBiDS

Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI, Human Resources, 2015-2017)

Modelling, Control and Optimization for Big Data Systems

(MoCOBiDS)

Contract nr. 176/2015

Abstract: Experiments, observations and numerical simulations in many areas of science and business are currently generating terabytes of data. Analyses of the information contained in these data sets have already led to major breakthroughs in fields ranging from genomics to power grids and process industry. The availability of these massive data sets is transforming society and the way we think about information storage, retrieval and data processing. Not only because our team has already acquired expertise on Big Data Systems, but also because of the potential for future applications, we have identified modeling, control and optimization for big data systems as the common theme for this research proposal. The central objective of this proposal is the analysis, design and implementation of data-driven mathematical methods and numerical algorithms for the analysis and optimization of Big Data Systems, as well as modeling and control challenges. While inspired by concrete cases from application ranging from data access networks, power grids to process industry, the real focus in this project will be on tackling generic problems starting from quantitative measured data collected from Big Data Systems and developing efficient numerical algorithms for solving them. We will develop novel algorithms for modeling, control and optimization of Big Data Systems, implement the new algorithms in a programming language, test them in a wide variety of applications and include them in a toolbox.

Project's team: Prof. Ion Necoara, Dr. Andrei Patrascu, Dr. Valentin Nedelcu, PhD Dragos Clipici.

Expected results:
  • 2015 - Literature review on methods for Big Data Systems (report on a book manuscript): Activity Report 2015
  • 2016 - Modeling and control for Big Data Systems (2 ISI journal articles, 4 conference articles, book manuscript): Activity Report 2016
  • 2017 - Optimization algorithms for large scale problems (2 ISI journal articles, 4 conference articles): Activity Report 2017

Publications

Papers published in ISI Journals

  • 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.
  • I. Necoara, A. Patrascu, Iteration complexity analysis of dual first order methods for conic convex programming, Optimization Methods & Software, 31(3): 645-678, 2016.
  • 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 & Applications, DOI 10.1007/s10957-016-0968-0, 2016.
  • A. Patrascu, I. Necoara, Q. Tran-Dinh, Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization, Optimization Letters, DOI:10.1007/s11590-016-1024-6, 2016.
  • I. Necoara, Yu. Nesterov, F. Glineur, Random block coordinate descent for linearly-constrained optimization over networks, Journal Optimization Theory & Applications, to appear: 1-26, 2016.

Papers under review/in progress

  • A. Patrascu, I. Necoara, On the convergence of inexact projection first order methods for convex minimization, submitted to IEEE Transactions on Automatic Control, November 2016.
  • I. Necoara, Yu. Nesterov and F. Glineur, Linear convergence of first order methods for non-strongly convex optimization, submitted to Mathematical Programming, July 2016.
  • I. Necoara, A. Patrascu and F. Glineur, Complexity of first order Lagrangian and penalty methods for conic convex programming, submitted to Optimization Methods & Software, September 2016.
  • I. Necoara, A. Patrascu, P. Richtarik, Randomized projection methods for convex feasibility problems, in preparation, 2016.
  • A. Patrascu, I. Necoara, Randomized proximal methods for convex minimization problems, in preparation, 2016.
  • I. Necoara, A. Patrascu, DuQuad: a dual first order algorithm for quadratic programming, in preparation, 2016.
  • I. Necoara, A. Patrascu, Iteration complexity analysis of coordinate descent methods for l0 regularized convex problems, in preparation, 2016.

Book in progress

  • I. Necoara, A. Patrascu, Decomposition Methods for Large Scale Mathematical Optimization, to appear in John Wiley & Sons, 2017.

Book chapters

  • 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, pp. 1-30, 2016.
  • I. Necoara, A. Patrascu, A. Nedich, Complexity certifications of first order inexact Lagrangian methods for general convex programming, chapter in book: Developments in Model-Based Optimization and Control, S. Olaru et al. (EDs.), Springer, pp. 1–22, 2015.

Papers accepted/submitted in conferences

  • I. Necoara, A. Patrascu, P. Richtarik, Randomized projection methods for convex feasibility problems, submitted to SIAM Conference on Optimization 2017.
  • I. Necoara, V. Nedelcu, D. Clipici, L. Toma, On fully distributed dual first order methods for convex network optimization, submitted to IFAC World Congress, 2017.
  • T. Ionescu, I. Necoara, A scale-free moment matching-based model reduction technique of linear networks, submitted to IFAC World Congress, 2017.
  • A. Patrascu, I. Necoara, Inexact projection primal first order methods for strongly convex minimization, submitted to IFAC World Congress, 2017.
  • A. Patrascu, I. Necoara, Complexity certifications of inexact projection primal gradient method for convex problems: application to embedded MPC, Proceedings of IEEE Mediterranean Conference on Control and Automation, 2016.
  • I. Necoara, V. Nedelcu, D. Clipici, L. Toma, C. Bulac, Optimal voltage control for loss minimization based on sequential convex programming, Proceedings of IEEE Conference Innovative Smart Grid Technologies Europe, 2016.
  • I. Necoara, Yu. Nesterov, F. Glineur, Linear convergence of first order methods for nonstrongly convex optimization, invited paper in session: Recent advances on convergence rates of first-order methods, International Conference on Continuous Optimization, 2016.
  • I. Necoara, A. Patrascu, F. Glineur, Complexity of first order inexact Lagrangian and penalty methods for conic convex programming, invited paper in session: First order methods for convex optimization problems, European Conference on Operational Research, 2016.
  • I. Necoara, Linear convergence of gradient type methods for non-strongly convex optimization, invited paper in session: Analyse non-lisse et optimisation, Colloque Franco- Roumain de Mathematiques Appliquees, 2016.


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