Research
 Theory and methods for Convex/Distributed/Big Data/Stochastic Optimization.
 Developing optimization algorithms with a focus on structure exploiting (sparsity, convexity, stochasticity, lowrank, 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.
I am recruiting 3 PhD candidates supported by two EUH2020MSCAITN projects:
 Project TraDEOpt (20202024) (specifically for ESR 9 and 10), to work on Optimization Methods for Big Data, Machine Learning or Complex Systems.
 Project ELOX (20202024) (specifically for ESR 9), to work on Control and Optimizationbased Learning techniques.
These are full positions 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 positions are for 3 years and they are available immediately (before July 2020). Interested candidates are invited to send a CV and the name of two references by email to me at: ion.necoara@acse.pub.ro.
Team
Members of the OLC group are affilieted with University Politehnica Bucharest and also with "Gheorghe MihocCaius Iacob" Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy
Prof. dr. Ion Necoara
ion.necoara@upb.ro Download CV
Bio: Ion Necoara received the B.Sc. degree in mathematics and the M.Sc. degree in optimization and statistics from the University of Bucharest, in 2000 and 2002. After graduating he worked as a Ph.D. student at the Delft Center for Systems and Control, Delft University of Technology, The Netherlands. For the period 20062009, he completed a Postdoctoral Fellowship at the Katholieke Universiteit Leuven, Belgium. Since 2009 he is a staff member of the Faculty of Automatic Control and Computers, University Politehnica Bucharest, where he is now a Professor of Numerical Methods and Optimization. He is also a senior researcher with "Gheorghe MihocCaius Iacob" Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy. His main research interests cover various topics in developing new optimization algorithms with a focus on structure exploiting and applying optimization techniques for developing new advanced controller design algorithms for complex systems.


Senior Researcher Dr. Gabriela Marinoschi
gabriela.marinoschi@acad.ro
Bio: Gabriela Marinoschi received the B.Sc. degree in mathematics from the University of Bucharest in 1979 and a Ph.D. in mathematics from the same university in 1989. Since 1998 she is with "Gheorghe MihocCaius Iacob" Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, where she is now a senior researcher and from 2020 director of the institute. Her research interests cover various topics such as optimal control, inverse problems, dynamical systems modeling and partial differential equations.


Assoc. prof. dr. Tudor Ionescu
tudor.ionescu@upb.ro
Bio: Tudor Ionescu received an M.Sc. from the Politehnica University of Bucharest (2004) and Ph.D. from University of Groningen, The Netherlands (2009). He was a Research Associate at Imperial College London, UK (20092013) and at the University of Sheffield, UK (20132015). Currently, he is an Associate Professor at Politehnica University of Bucharest and a senior researcher with "Gheorghe MihocCaius Iacob" Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy. His research interests include modelling and control of nonlinear systems with focus on modelling and model order reduction with preservation of physical structure.


Assoc. prof. dr. Lucian Toma
lucian_toma_ro@yahoo.com
Bio: Lucian Toma received an M.Sc. from the Politehnica University of Bucharest (2003) and Ph.D. from Politehnica University of Bucharest on Power Systems Control (2010). Currently, he is an Associate Professor at Politehnica University of Bucharest. His research interests include modelling and control of power systems, smart grids and smart cities.


Phd. eng. Lupu Daniela
daniela.lupu@upb.ro
Bio: Lupu Daniela received her B.Sc. and M.Sc. degree in automatic control from the University Politehnica of Bucharest in 2017 and 2019, and she is currently a Ph.D. student at the same university. Her main research interests include stochastic optimization, machine learning, modeling complex systems and predictive control.


Phd. Liliana Ghinea
Liliana.Ghinea@ugal.ro
Bio: Liliana Ghinea received her B.Sc and M.Sc. degree in mathematics and computer science from the "Dunarea de Jos" University of Galati in 2017 and 2019, and she is currently a Ph.D. student at the "Dunarea de Jos" University of Galati (Professor Marian Barbu) in cosupervision with Professor Ion Necoara from University Politehnica of Bucharest. Her main research interests
include optimization, control, modeling complex systems and big data optimization.


Phd. Nitesh Kumar Singh
ns103213@gmail.com
Bio: Nitesh Kumar Singh received his B.Sc. degree in Physics, Chemistry, Mathematics from CSJM University and M.Sc. degree in Applied Mathematics from South Asian University, New Delhi, India in 2017 and 2020, respectively. He is currently a Ph.D. Student at University Politehnica of Bucharest, Romania. His main research interests include Stochastic Optimization, Machine Learning and Deep Neural Networks.


Eng. Mirel Puchianu
puchianu.m@gmail.com
Bio: Mirel Puchianu received his B.Sc in automatic control from the University Politehnica of Bucharest in 2019. His bachelor thesis was on efficient optimization methods for ACOPF problem arising in power systems. He is currently pursuing a master at the same university. His main research interests include optimization, control, machine learning and applications in power systems.
 
Former members
Dr. eng. Andrei Patrascu
andrei.patrascu@acse.pub.ro
Bio: Andrei Patrascu received the M.Sc. and Ph.D. degrees in automatic control from the University Politehnica
of Bucharest, Romania, in 2012 and 2015, respectively. His research interests include
numerical algorithms for large scale sparse optimization problems and their application in systems and control theory.


Dr. eng. Valentin Nedelcu
valentin.nedelcu@acse.pub.ro
Bio: Valentin Nedelcu received the M.Sc. and Ph.D. degrees in automatic control from the University Politehnica
of Bucharest, Romania, in 2011 and 2013, respectively. His main research interests
include distributed optimization and networked control systems.


Drd. eng. Dragos Clipici
dragos.clipici@acse.pub.ro
Bio: Dragos Clipici received his M.Sc. degree in automatic control from the "Politehnica" University of Bucharest in 2010, and is currently a Ph.D. student at the "Politehnica" University of Bucharest. His main research interests
include distributed optimization, robust and stochastic optimization and predictive control.
 
Project  ScaleFreeNet
Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI, Exploratory Research Project, PNIIIP4IDPCE20160731, 20172019)
Scalefree modeling and optimization techniques for control of complex networks
(ScaleFreeNet)
CONTRACT NR. 39/2017
Project's research team: Prof. I. Necoara, Assoc. Prof. L. Toma,Assoc. Prof. T. Ionesc, Dr. A. Patrascu, Phd. D. Lupu
Publications
Papers in ISI Journals
 A. Patrascu, I. Necoara, Nonasymptotic convergence of stochastic proximal point methods for constrained convex optimization, Journal of Machine Learning Research, 18(198): 1–42, 2018
 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
 I. Necoara, Yu. Nesterov, F. Glineur, Linear convergence of first order methods for nonstrongly convex optimization, Mathematical Programming, 175(1): 69–107, 2019
 A. Nedich, I. Necoara, Random minibatch subgradient algorithms for convex problems with functional constraints, Applied Mathematics and Optimization, 80(3): 801–833, 2019
 I. Necoara, P. Richtarik, A. Patrascu, Randomized projection methods for convex fea sibility problems: conditioning and convergence rates, Siam Journal on Optimization, 29(4): 2814–2852, 2019
 I. Necoara, Faster randomized block Kaczmarz algorithms, Siam Journal on Matrix Analysis and Applications, 40(4), 14251452, 2019
 T. Sun, I. Necoara, Q. TranDinh, Composite Convex Optimization with Global and Local Inexact Oracles, to appear in Computational Optimization and Applications, 2020
 I. Necoara, M. Takac, Randomized sketch descent methods for nonseparable linearly constrained optimization, to appear in IMA Journal of Numerical Analysis, 2020
 I. Necoara, T. Ionescu, H2 model reduction of linear network systems by moment matching and optimization, IEEE Transactions on Automatic Control, 65(12), 18, 2020
 I. Necoara, T. Ionescu, Optimal H2 moment matchingbased model reduction for linear systems by (non)convex optimization, partially accepted in Siam Journal of Control and Optimization, 2018
 I. Necoara, Random block projection algorithms with extrapolation for convex feasibility problems, submitted to Applied Mathematics and Optimization, 2019
 I. Necoara, A. Nedich, Minibatch stochastic subgradientbased projection algorithms for solving convex inequalities, submitted to Computational Optimization and Applications, 2019
 I. Mezghani, Q. TranDinh, I. Necoara, A. Papavasiliou, A globally convergent GaussNewton algorithm for AC optimal power flow, submitted to European Journal of Operational Research, 2019.
Papers in conferences
 D. Lupu, I. Necoara, Primal and dual first order methods for SVM: applications to driver fatigue monitoring, International Conference on System Theory, Control and Computing, Sinaia, 2018.
 I. Necoara, M. Takac, Random coordinate descent methods for linearly constrained convex optimization, International Symposium on Mathematical Programming, Bordeaux, 2018.
 I. Necoara, A. Patrascu, ORSAGA: Overrelaxed stochastic average gradient mapping algorithms for finite sum minimization, European Control Conference, Limassol, 2018
 I. Necoara, Random gradient algorithms for convex minimization over intersection of simple sets, European Control Conference, Napoli, 2019
 I. Necoara, T. Ionescu, Parameter selection for best H2 moment matchingbased model approximation through gradient optimization, European Control Conference, Napoli, 2019
 T. Ionescu, O. Iftime, Q. Zhong, Model reduction by moment matching: case study of a FIR system, European Control Conference, Napoli, 2019.
 O. Fercoq, A. Alacaoglu, I. Necoara, V. Cevher, Almost surely constrained convex optimization, International Conference on Machine Learning (A* conference), Long Beach, 2019.
 A. Nedich, I. Necoara, Random minibatch projection algorithms for convex feasibility problems, Conference on Decision and Control, Nice, 2019
 A. Radu, M. Eremia, L. Toma, Optimal charging coordination of electric vehicles considering distributed energy resources, IEEE PES PowerTech Conference, Milano, 2019.
 D. Sidea, L. Toma, M. Sanduleac, I. Picioroaga, V. Boicea, Optimal BESS Scheduling Strategy in Microgrids Based on Genetic Algorithms, IEEE PES PowerTech Conference, Milano,2019
 X. Cheng, I. Necoara, A suboptimal H2 clusteringbased model reduction approach for linear network systems, European Control Conference, 2020
Project  MoCOBiDS
Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI, Human Resources, 20152017)
Modelling, Control and Optimization for Big Data Systems
(MoCOBiDS)
CONTRACT NR. 176/2015
Project's research 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 in ISI Journals
 I. Necoara, Yu. Nesterov, F. Glineur, Random block coordinate descent for linearlyconstrained optimization over networks, Journal of Optimization Theory and Applications, to appear, pp. 126, 2016
 I. Necoara, A. Patrascu, Iteration complexity analysis of dual first order methods for conic convex programming, Optimization Methods & Software, 31(3):645678, 2016.
 N.A. Nguyen, S. Olaru, P. RodriguezAyerbe, M. Hovd, I. Necoara, Constructive solution of inverse parametric linear/quadratic programming problems, Journal of Optimization Theory & Applications, DOI 10.1007/s1095701609680, 2016.
 A. Patrascu, I. Necoara, Q. TranDinh, Adaptive inexact fast augmented Lagrangian methods for constrained convex optimization, Optimization Letters, DOI:10.1007/s11590 01610246: 118, 2016.
 I. Necoara, D. Clipici, Parallel random coordinate descent methods for composite minimization: convergence analysis and error bounds, SIAM Journal on Optimization, vol. 26, no. 1, pp. 197226, 2016.
 A. Patrascu, I. Necoara, On the convergence of inexact projection first order methods for convex minimization, IEEE Transactions on Automatic Control, 2017.
 I. Necoara, A. Patrascu and F. Glineur, Complexity of first order Lagrangian and penalty methods for conic convex programming, Optimization Methods and Software, 2017.
Book chapters
 I. Necoara, Coordinate gradient descent methods, chapter in Taylor & Francis LLC  CRC Press, pp. 130, 2016.
 I. Necoara, A. Patrascu, A. Nedich, Complexity certifications of first order inexact Lagrangian methods for general convex programming, chapter in Springer, pp. 1–22, 2015.
Papers 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 scalefree moment matchingbased 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, Mediterranean Conference on Control and Automation, 2016.
 I. Necoara, L. Toma, V. Nedelcu, Optimal voltage control for loss minimization based on sequential convex programming, IEEE Conference Innovative Smart Grid Technologies, 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 firstorder methods, International Conference Continuous Optimization, 2016.
 I. Necoara, Linear convergence of gradient type methods for nonstrongly convex optimization, invited paper in session: Analyse nonlisse et optimisation, Colloque Franco Roumain de Mathematiques Appliquees, 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.
Applications
In our Optimization, Learning and Control Lab, we have developed a number of applications, from which we mention the following: DJI Phantom Drone: DJI Phantom is remarkably intuitive and easy to fly drone, endowed with a highquality camera for image/video recording, which can be used in as an implementation platform for highly complex applications.
 Epuck robots: The epuck robot is educational desktop robot and open hardware, and its onboard software is open source. It has proven to be a versatile tool for embedded programming in automatic control and it is of interest in developing embedded/distributed control algorithms for networked systems.
 Four tank process: The 4 tank application consists of four identical interconnected water tanks with inflows provided from a base tank via two pumps. Each individual tank is fitted with controllable valves, level transducers, flow transducers and overflow and minimum level sensors. The main purpose of this application is controlling the water levels in each of the four tanks. Given the fact that each plant is fitted with a flow valve, control for different operations scenarios can be tested. The plant is fitted with a Siemens S71200 programmable logic controller.
In the picture below we can view the result from a control experiment, in which the epuck robot has to follow a sinusoidal trajectory.
We implemented an MPC control algorithm directly onto this programmable logic controller, with the control results in figures below.
Software
Pagina creata la data de 15/11/2015. Ultima modificare:4/6/2020.