LOC

Learning, Optimization and Control Group

Abstract

Next generation of smart control systems (SCS) are expected to learn models from data and take optimal decisions in real-time, leading to increased performance, safety, energy efficiency, and ultimately value creation. Since, SCS produce large amounts of data, machine learning (ML) aims at extracting information from them. The key step in any ML technique is training, where an optimization problem is solved to tune the parameters of the ML model. In this project we reverse this paradigm, i.e. we use ML for devising efficient optimization algorithms. Our key observation is that the modelling and control approaches for SCS yield optimization problems that are extremely challenging due to their large dimension, stochasticity, nonconvexity, etc. Optimization algorithms addressing such problems usually involve many parameters that need to be hand-tuned after time-consuming experimentation and are prone to ill-conditioning and slow convergence. To address these challenges, our group will develop learning-based techniques for devising efficient tuning-free optimization algorithms. A novel universal framework will be developed, which will serve as a solid theoretical ground for the design of new learning paradigms to train optimization methods with mathematical guarantees for convergence. Modelling and control problems for SCS, e.g. those arising in power networks, will provide the datasets for the training.

Team

Prof. Ion Necoara

Leader

CSI Gabriela Marinoschi

Prof. Tudor C. Ionescu

Prof. Lucian Toma

Phd. Daniela Lupu

Phd.
Nitesh Kumar Singh

Publications

Papers accepted/submitted in ISI journals:

[J1] I. Necoara, N.K. Singh, Stochastic subgradient projection methods for composite optimization with functional constraints, Journal of Machine Learning Research (Q1), 23(265), 1-35, 2022 (https://www.jmlr.org/papers/volume23/21-1062/21-1062.pdf).
[J2] I. Necoara, D. Lupu, General higher-order majorization-minimization algorithms for (non)convex optimization, provisionally accepted in Siam Journal on Optimization, 2022.
[J3] L. El Bourkhissi, I. Necoara, Complexity of linearized augmented Lagrangian for optimization with nonlinear equality constraints, Journal of Global Optimization, doi: 10.1007/s10898-024-01456-3, 2024.
[J4] D. Lupu, I. Necoara, Convergence analysis of stochastic higher-order majorization-minimization algorithms, Optimization Methods and Software (Q2), doi: 10.1080/10556788.2023.2256447, 2023.
[J5] F. Chorobura, I. Necoara, Random coordinate descent methods for nonseparable composite optimization, Siam Journal on Optimization (Q1), 33(3), doi: 10.1137/22M148700X, 2023.
[J6] N.K. Singh, I. Necoara, V. Kungurtsev, Mini-batch stochastic subgradient for functional constrained optimization, Optimization (Q2), doi: 10.1080/02331934.2023.2189015, 2023.
[J7] Y. Nabou, I. Necoara, Efficiency of higher-order algorithms for minimizing general composite optimization, Computational Optimization and Applications (Q1), doi: 10.1007/s10589-023-00533-9, 2023.
[J8] F. Chorobura, I. Necoara, Coordinate descent methods beyond smoothness and separability, Computational Optimization and Applications (Q1), 88, 107–149, doi: 10.1007/s10589-024-00556-w, 2024.
[J9] I. Necoara, F. Chorobura, Efficiency of stochastic coordinate proximal gradient methods on nonseparable composite optimization, Mathematics of Operations Research (Q1), doi: 10.1287/moor.2023.0044, 2024.
[J10] N. K. Singh, I. Necoara, A stochastic moving ball approximations method for smooth convex constrained minimization, Computational Optimization and Applications (Q1), 89, 659–689, doi: 10.1007/s10589-024-00612-5, 2024.
[J11] L. Tondji, I. Necoara, D. A. Lorenz, Acceleration and restart for the randomized Bregman-Kaczmarz method, Linear Algebra and Its Applications (Q2), 699, 508–538, doi: 10.1016/j.laa.2024.07.009, 2024.
[J12] N. K. Singh, I. Necoara, Stochastic halfspace approximation method for convex optimization with nonsmooth functional constraints, IEEE Transactions on Automatic Control (Q1), 70(1), doi: 10.1109/TAC.2024.3426888, 2025.
[J13] D. Lupu, I. Necoara, Exact representation and efficient approximations of linear model predictive control laws via HardTanh type deep neural networks, Systems and Control Letters (Q1), 186, doi: 10.1016/j.sysconle.2024.105742, 2024.
[J14] L. El Bourkhissi and I. Necoara, Convergence rates for an inexact linearized ADMM for nonsmooth optimization with nonlinear equality constraints, provisionally accepted in Computational Optimization and Applications (Q1), 2024.
[J15] T. C. Ionescu, O. V. Iftime and R. Stefan, “Loewner matrices-based data-driven loopshaping design with closed-loop pole placement”, submitted to IEEE Transactions on Automatic Control (Q1), 2024.
[J16] D. Lupu, J. Garrett, T. A. Johansen, M. Orlandic, I. Necoara, Quick unsupervised hyperspectral dimensionality reduction for earth observation: a comparison, submitted to IEEE Transactions on Computational Imaging (Q2), 20 June 2024.

Papers in conferences proceedings:

[C1] Tudor C. Ionescu, Lahcen El Bourkhissi, Ion Necoara, "Least squares moment matching-based model reduction using convex optimization", 2022 26th International Conference on System Theory, Control and Computing (ICSTCC), DOI: 10.1109/ICSTCC55426.2022.9931837. [See pdf here]
[C 2] Lahcen El Bourkhissi, Ion Necoara, Panagiotis Patrinos, "Linearized ADMM for Nonsmooth Nonconvex Optimization with Nonlinear Equality Constraints", In 2023 62nd IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC49753.2023.10384166. [See pdf here ]
[C3]. Y. Nabou, L. Toma, I. Necoara, Modified projected Gauss-Newton method for constrained nonlinear least-squares: application to power flow analysis, Proceedings of IEEE European Control Conference, doi: 10.23919/ECC57647.2023.10178179, 2023.
[C4]. T.C. Ionescu, L. El Bourkhissi, I. Necoara, Least-squares moment matching-based model reduction using convex optimization, Proceedings of IEEE International Conference on System Theory, Control and Computing, doi: 10.1109/ICSTCC55426.2022.9931837, 2022.
[C5]. F. Chorobura, D. Lupu, I. Necoara, Coordinate projected gradient descent minimization and its application to orthogonal nonnegative matrix factorization, Proceedings of IEEE Conference on Decision and Control, doi: 10.1109/CDC51059.2022.9992996, 2022.
[C6] D. Lupu, I. Necoara, Deep unfolding projected first order methods-based architectures: application to linear model predictive control, , Proceedings of IEEE European Control Conference, doi: 10.23919/ECC57647.2023.10178167, 2023.
[C7] L. El Bourkhissi, I. Necoara, P. Patrinos, Linearized ADMM for nonsmooth nonconvex optimization with nonlinear equality constraints, Proceedings of IEEE Conference on Decision and Control, doi: 10.1109/CDC49753.2023.10384166, 2023.
[C8] L.N. Tondji, D. Lorenz, I. Necoara, An accelerated randomized Bregman-Kaczmarz method for strongly convex linearly constraint optimization, Proceedings of IEEE European Control Conference, doi: 10.23919/ECC57647.2023.10178390, 2023.
[C9] F. Chorobura, F. Glineur, I. Necoara, Can random proximal coordinate descent be accelerated on nonseparable convex composite minimization problems?, Proceedings of IEEE European Control Conference, doi: 10.23919/ECC57647.2023.10178217, 2023.
[C10] T. C. Ionescu, O. V. Iftime and R. Stefan, A moment matching-based loop shaping design with closed-loop pole placement, Proceedings of IEEE European Control Conference, doi: 10.23919/ECC57647.2023.10178150, 2023.
[C11] Y. Kawano, T. C. Ionescu and O. V. Iftime, Gramian Preserving Moment Matching for Linear Systems, Proceedings of IEEE European Control Conference, doi: 10.23919/ECC57647.2023.10178382, 2023.
[C12] X. Cheng, T.C. Ionescu, O.V. Iftime and I. Necoara, Moment matching for second order systems with pole-zero placement, Proceedings of IEEE Conference on Decision and Control, December 2024.
[C13] N.K. Singh, I. Necoara, Unified analysis of stochastic gradient projection methods for convex optimization with functional constraints, Proceedings of IEEE European Control Conference, doi: 10.23919/ECC64448.2024.10591311, 2024.
[C14] D. Lupu, I. Necoara, L. Toma, Deep unfolding primal-dual architectures: application to linear model predictive control, submitted to European Control Conference, Greece, 2025.
[C15] T.C. Ionescu, O. Iftime and I. Necoara, Data-driven Loewner matrices-based modeling and model predictive control of a single machine infinite bus model, Proceedings of IEEE Meditaranean Control Conference, doi: 10.1109/MED61351.2024.10566187, 2024.
[C16] N. K. Singh, I. Necoara, Model predictive control problem with ellipsoidal state constraints and its application to a multi-machine power system, to be submitted to Meditaranean Control Conference, 2024.

Software package

Unfolded DeepNN MPC: open-source, Python code, on Github. It contains an interface, where the user can introduce easily the data for the dynamical system and the associated finite horizon optimal control problem. Once the control problem has been introduced, it solves it either with optimization algorithms or it generates a set of training data, trains a deep neural network inspired from these optimization methods and thus learning the MPC law. For testing, we consider a power system test case widely used in the literature, called CIGRE.

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