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Applied Mathematics

The Applied Mathematics group gathers 8 professors and about twenty researchers who are working on several subfields.

Principal Investigators :

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Research Lab :

INMA (Mathematical Engineering research division)

Research Areas :

Research in the algebra team focuses on various structures whose automorphism groups are linear algebraic groups, notably quadratic forms and algebras over arbitrary fields. These structures are studied using methods from number theory and algebraic geometry, such as valuation theory and Galois cohomology. The current projects aim at developing new cohomological invariants and a noncommutative valuation theory for central simple algebras with involution. This activity is run in cooperation with the group theory team of the IRMP.

Balance laws are hyperbolic partial differential equations that are commonly used to express the fundamental dynamics of open conservative systems. Many physical systems having an engineering interest are described by systems of one-dimensional hyperbolic balance laws. Typical examples are for instance the telegrapher equations for electrical lines, the shallow water (Saint-Venant) equations for open channels, the Euler equations for gas flow in pipelines or the Aw-Rascle equations for road traffic. In this research, our concern is to analyse the exponential stability (in the sense of Lyapunov) of the steady-states of such systems.

This research relies on the use of non-negative convex algebra for solving underdetermined linear systems of equations under positive constraints. Such problems arise in various domains of Systems Biology. We are particularly concerned with the decomposition of complex metabolic networks into elementary pathways and with the metabolic flux analysis which aims at computing the entire intracellular flux distribution from a limited number of flux measurements.

The group works on numerical methods for rational approximation, linear algebra and optimization with applications in systems and control, economy, biology and medicine. In approximation theory we look at approximation problems in the complex plane (orthogonal polynomials, quadrature formulas) and at the solution of functional equations, with applications in science, technology and economy. In linear algebra we study the model reduction problem via interpolation and projection of state-space models. We also look at optimal Hankel-norm approximations and their formulation via convex optimization techniques.  In optimization, we are looking for general schemes with provable global complexity estimates. This extends onto the methods for solving systems of nonlinear equations and optimization on nonlinear manifolds. These techniques are applied to problems in signals and systems.

We study several types of matrix factorization techniques, in particular variants where nonnegative factors are required. We focus on both algorithmic (mehods and computational complexity) and applicative (machine learning, graph problems, polyhedral combinatorics) points of view.

The complex rheological behaviour of non-Newtonian liquids is dictated by the flow induced evolution of their internal microstructure. For example, in homogeneous polymeric fluids, the relevant microstructure is the conformation of the macromolecules. Each macroscopic fluid element contains a large number of polymers with a statistical distribution of conformations. During flow, the polymer conformations evolve along the fluid trajectories. Also, the macroscopic stress carried by each fluid element is itself governed by the distribution of conformations within that element. One thus faces a highly non-linear coupling between rheological behaviour, flow-induced evolution of the microstructure, and flow conditions. The fundamental scientific challenges in rheology and non-Newtonian fluid mechanics are indeed to fully comprehend the nature of this non-linear coupling and to predict its consequences in flow problems of interest. We currently focus on the development of molecular models of kinetic theory and methods of computational rheology.

Most recent publications

Below are listed the 10 most recent journal articles and conference papers produced in this research area. You also can access all publications by following this link : see all applied mathematics publications.


Journal Articles


1. Daglayan Sevim, Hazan; Vary, Simon; Absil, O.; Cantalloube, F.; Christiaens, V.; Gillis, N.; Jacques, Laurent; Leplat, Valentin; Absil, Pierre-Antoine. An alternating minimization algorithm with trajectory for direct exoplanet detection : The AMAT algorithm. In: Astronomy & Astrophysics, Vol. 692, p. A126 (2024). doi:10.1051/0004-6361/202451242.

2. Absil, Pierre-Antoine; Cojuhari, Irina; Fiodorov, Ion; Tits, André L. Strong versions of impulsive controllability and sampled observability. In: Automatica, Vol. 169, p. 111865 (2024). doi:10.1016/j.automatica.2024.111865.

3. Van Brandt, Léopold; Delvenne, Jean-Charles. Predicting State Transitions in Autonomous Nonlinear Bistable Systems With Hidden Stochasticity. In: IEEE Control Systems Letters, Vol. 8, p. 850-855 (2024). doi:10.1109/lcsys.2024.3401586.

4. Delvenne, Jean-Charles; Falasco, Gianmaria. Thermokinetic relations. In: Physical Review E, Vol. 109, no.1 (2024). doi:10.1103/physreve.109.014109.

5. Nabou, Yassine; Glineur, François; Necoara, Ion. Proximal gradient methods with inexact oracle of degree q for composite optimization. In: Optimization Letters, (2024). doi:10.1007/s11590-024-02118-9.

6. Tignol, Jean-Pierre. Invariants de Witt des involutions de bas degré en caractéristique 2. In: Comptes Rendus Mathématiques / Académie des Sciences, Vol. 362, no.1, p. 1261-1271 (2024). doi:10.5802/crmath.640.

7. Van Dessel, Guillaume; Glineur, François. Optimal inexactness schedules for tunable oracle-based methods. In: Optimization Methods and Software, Vol. 39, no.3, p. 664-698 (2024). doi:10.1080/10556788.2023.2296982.

8. Zamani, Moslem; Glineur, François; Hendrickx, Julien. On the Set of Possible Minimizers of a Sum of Convex Functions. In: IEEE Control Systems Letters, Vol. 8, p. 1871-1876 (2024). doi:10.1109/lcsys.2024.3414378.

9. Goujaud, Baptiste; Moucer, Céline; Glineur, François; Hendrickx, Julien; Taylor, Adrien B.; Dieuleveut, Aymeric. PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python. In: Mathematical Programming Computation, Vol. 16, no.3, p. 337-367 (2024). doi:10.1007/s12532-024-00259-7.

10. Bousselmi, Nizar; Hendrickx, Julien; Glineur, François. Interpolation Conditions for Linear Operators and Applications to Performance Estimation Problems. In: SIAM Journal on Optimization, Vol. 34, no.3, p. 3033-3063 (2024). doi:10.1137/23m1575391.


Conference Papers


1. Van Brandt, Léopold; Delvenne, Jean-Charles; Flandre, Denis. Variability-Aware Noise-Induced Dynamic Instability of Ultra-Low-Voltage SRAM Bitcells. In: IEEE Xplore, 2024, 979-8-3503-8122-1 xxx. doi:10.1109/lascas60203.2024.10506179.

2. Van Brandt, Léopold; Flandre, Denis; Delvenne, Jean-Charles. Stochastic Nonlinear Dynamical Modelling of SRAM Bitcells in Retention Mode. In: IEEE Xplore, 2024, 979-8-3503-7152-9 xxx. doi:10.1109/edtm58488.2024.10512067.

3. Loucheur, Benoît; Absil, Pierre-Antoine; Journée, Michel. Weather Data Imputation Using Graph-Based Low-Rank Matrix Completion with Variable Projection. In: Proceedings of BNAIC/BeNeLearn 2024, 2024, n/A, p. 1-16 xxx.

4. Bianchin, Gianluca; Delvenne, Jean-Charles. Cycle families and Resilience of Dynamical Networks. In: IEEE Xplore, 2024, 979-8-3503-8265-5 xxx. doi:10.23919/acc60939.2024.10644385.

5. Bousselmi, Nizar; Pustelnik, Nelly; Hendrickx, Julien; Glineur, François. Comparison of Proximal First-Order Primal and Primal-Dual Algorithms via Performance Estimation. In: Eusipco 2024, 2024, 978-9-4645-9361-7, p. 2647-2651 xxx. doi:10.23919/EUSIPCO63174.2024.10715388.

6. Vernimmen, Pierre; Glineur, François. Convergence analysis of an inexact gradient method on smooth convex functions. In: ESANN 2024 proceedings, 2024, 978-2-87587-090-2 xxx. doi:10.14428/esann/2024.es2024-171.

7. Sechaud, Victor; Jacques, Laurent; Tachella, Julián. Equivariance-based self-supervised learning for audio signal recovery from clipped measurements. In: Proc of 32nd European Signal Processing Conference (EUSIPCO). Vol. 1, no.1, p. 852 (2024). 2024 xxx.

8. Vary, Simon; Ablin, Pierre; Gao, Bin; Absil, Pierre-Antoine. Optimization without Retraction on the Random Generalized Stiefel Manifold. In: Proceedings of Machine Learning Research. Vol. 235, p. 49226-49248 (2024). MLResearchPress, 2024 xxx.

9. Jungers, Raphaël M.. Statistical comparison of Path-Complete Lyapunov Functions: a Discrete-Event Systems perspective. In: In Proc. of IFAC-WODES'24. (2024). 2024 xxx.

10. Delogne, Rémi; Jacques, Laurent. Quadratic polynomial kernel approximation with asymmetric embeddings. In: International Workshop on Deep Learning and Kernel Machines (2024). 2024 xxx.