Seminars

[INMA] 2026-03-03 (14:00) : Consensus is a myth: Human label variation in Natural Language Inference

At Euler building (room A.002)

Speaker: Marie-Catherine de Marneffe (CENTAL, UCLouvain)

Abstract: Recently, NLP researchers have increasingly begun to acknowledge that humans often diverge in their interpretations of various NLP tasks, and that such variation should be captured if robust language understanding is to be achieved. In this talk, I will focus on analyzing human label variation in the Natural Language Inference (NLI) task, in which, given a premise, one identifies whether a hypothesis sentence is true, false, or undetermined. For instance, if one says: “My friend often travels with a heavy suitcase”, can it be inferred that “My friend often travels with a light suitcase”? I will examine the various sources of NLI label variation and investigate whether or not they can be captured by current LLMs, arguing that, in the presence of variation, labels without explanations are not sufficiently meaningful.

[INMA] 2026-02-24 (14:00) : A passivity-based perspective on distributed optimization and its acceleration

At Euler building (room A.002)

Speaker: Ivano Notarnicola (University of Bologna)

Abstract: This talk revisits the classical gradient method for unconstrained optimization through the lens of control theory. By explicitly interpreting the gradient method as a feedback interconnection between a linear dynamical system and a static nonlinearity associated to the cost function gradient, we uncover an underlying control-theoretic structure. Within this framework, linear convergence is established using arguments from passivity theory. This system-theoretic viewpoint further reveals that the gradient method can also be interpreted as a feedback-controlled static nonlinearity, opening the door to an unconventional interpretation of accelerated schemes. Finally, the same passivity-based tools are also applied to consensus optimization problems, yielding a unified framework for the design and analysis of distributed gradient methods and their acceleration.

[INMA] 2026-02-17 (14:00) : Newcomers Seminar

At Euler building (room A.002)

Section 1: Fleet Rebalancing: Scalable Feedback Optimization for Autonomous Mobility-on-Demand via null-space projection

Speaker: Arthur Mélot (UCLouvain)

Abstract: While state-of-the-art Model Predictive Control (MPC) approaches for Autonomous Mobility-on-Demand (AMoD) achieve scalability through flow-based modeling , they remain computationally burdened by iterative solvers , the need for a pre-computed static equilibrium to track and also a non-negligible prediction horizon to ensure stability. This presentation introduces a horizon-free feedback optimization algorithm that also eliminates the need for offline equilibrium computation. By treating the economic cost minimization as a dynamic feedback process, our approach steers the fleet directly toward the optimal operating point in real-time and does not track a pre-computed reference signal. The preliminary results provide a negligible execution time, outperforming the execution time of standard MPC. This project is still in progress and all the results provided are preliminary results.

Section 2: Mechanical determinants of tactile perception in the human fingertip

Speaker: Viktoriia Kozadaeva (UCLouvain)

Abstract: Human tactile perception enables discrimination of features with microscale precision. This remarkable sensitivity arises from dense innervation of mechanoreceptors within the fingertip that transduce small skin deformations into neural signals. While neural innervation of the fingertip has been widely studied, the mechanical mechanisms enabling the microscale sensations remain underexplored. My research project aims to establish a relationship between psychophysical performance and mechanical determinants underlying microscale tactile perception. Specifically, we examine how fingerpad deformation relates to perceptual detection of small surface features.

Section 3: Reinforcement Learning for Petri-Net based Discrete Event Systems: Application to Aircraft Maintenance Scheduling

Speaker: Margot Devillers (UCLouvain)

Abstract: Efficient aircraft maintenance scheduling is critical to maximizing fleet availability while ensuring regulatory compliance and cost efficiency. The problem involves grouping periodic tasks into maintenance projects and deciding when to ground aircraft under strict deadline and resource constraints. The combinatorial nature of these decisions makes classical optimization approaches challenging at fleet scale. Building on a Colored Petri Net (CPN) digital twin of the aircraft maintenance operations, this work formulates the scheduling process as a discrete-event dynamical system and investigates the use of Reinforcement Learning (RL) to address it. An RL agent is currently being developed to interact with the CPN environment and learn fleet-level policies that balance aircraft utilization and operational feasibility over a finite planning horizon.

[INMA] 2026-02-10 (14:00) : The Ellipsoidal Separation Machine

At Euler building (room A.002)

Speaker: Antonio Frangioni (Università di Pisa)

Abstract: We propose the -- to the best of our knowledge -- first fully functional implementation of the "Separation by a Convex Body" approach first outlined in [Grzybowski et al., Optimization Methods and Software, 2005] for classification, separating two data sets using an ellipsoid. A training problem is defined that is structurally similar to the Support Vector Machine (SVM) one, thus leading to call our method the Ellipsoidal Separation Machine (ESM). Like SVM, the training problem is convex, and can in particular be formulated -- via a set of not entirely obvious reformulation tricks -- as a Semidefinite Program (SDP). However, practical classification tasks produce rather large SDPs, solving which by means of standard SDP approaches (be them IP-or first-order based) does not scale. As an alternative, a nonconvex formulation is proposed that is amenable to a Block-Gauss-Seidel approach alternating between a much smaller SDP and a simple separable Second-Order Cone Program. For the purpose of the classification approach the reduced SDP can even be solved approximately by relaxing it in a Lagrangian way and updating the multipliers by fast subgradient-type approaches. A characteristic of ESM is that it necessarily defines "indeterminate points", i.e., those that cannot be reliably classified as belonging to either one of the two sets. This makes it particularly suitable for Classification with Rejection (CwR) tasks, whereby the system explicitly indicates that classification of some points as belonging to one of the two sets is too doubtful to be reliable. We show that, in many datasets, ESM is competitive with SVM -- with the kernel chosen among the three standard ones and endowed with CwR capabilities using the margin of the classifier -- and in general behaves differently. Thus, ESM provides another arrow in the quiver when designing CwR approaches, although more work would be needed to scale it to really large datasets.

[INMA] 2026-02-03 (14:00) : IRKA Is a Riemannian Gradient Descent Method

At Euler building (room A.002)

Speaker: Petar Mlinarić (University of Zagreb)

Abstract: Large-scale systems frequently arise in applications involving partial differential equations or network dynamics. Model order reduction seeks to replace a large-scale system with a reduced-order model, enabling faster simulations with minimal loss of accuracy. The Iterative Rational Krylov Algorithm (IRKA) is a well-known method for model order reduction of linear time-invariant systems, originally formulated as a fixed-point iteration. In this talk, we show that IRKA can be interpreted as a Riemannian gradient descent method with a fixed step size on the manifold of rational functions of fixed degree. This geometric perspective motivates the application of other Riemannian optimization techniques to the same problem. We illustrate the effectiveness of these approaches through numerical examples.

[INMA] 2026-01-20 (14:00) : Opinion Dynamics with Nonlinear Interaction: From Robust Clustering to Environmental Coupling

At Euler building (room A.002)

Speaker: Anthony Couthures (Université de Lorraine)

Abstract: Social opinion and environmental states are deeply coupled: collective human behavior impacts the environment, while the state of the environment feeds back into public opinion. In this talk, I will present a mathematical framework to analyze these interactions, moving from standard consensus models to coupled socio-environmental dynamics. First, I will introduce a generalized framework for multi-agent opinion dynamics with nonlinear interactions. Unlike classical linear consensus models, nonlinear communication allows for the emergence of rich behaviors beyond simple agreement. We will establish a sharp threshold linking network connectivity (algebraic connectivity) and interaction nonlinearity (Lipschitz constant) that dictates the transition from global synchronization to persistent polarization. Using Input-to-State Stability (ISS) theory, I will also discuss the robustness of these polarized clusters against external influence. In the second part, I will couple this opinion model with an environmental resource variable. By analyzing the system on the synchronization manifold, we identify the role of the "attention parameter" β: representing the weight agents place on environmental feedback versus social influence. Through bifurcation analysis, I will demonstrate how varying this parameter triggers fundamental qualitative changes, specifically Pitchfork bifurcations (leading to bistability and polarization) and Hopf bifurcations (leading to recurrent cycles of environmental collapse and recovery).

[INMA] 2025-12-16 (14:00) : Geometry of low-rank tensors

At EULER (room A.002)

Speaker: Simon Jacobsson (KU Leuven)

Abstract: Morally, a manifold is a set where notions from calculus are well-defined. For example, the set of n-by-n orthogonal matrices and the set of n-by-n symmetric positive definite matrices are manifolds. Knowing that a set of matrices or tensors is a manifold allows us to use a host of calculus tools to do numerical analysis on that set. For example, many constrained optimization problems can be formulated as unconstrained manifold optimization problems. Algorithms for these can then make use of manifold gradients and analogues of straight lines called geodesics. We consider the set of fixed-rank tensors. When the rank is sufficiently low, then (almost) any tensor in this set is related to (almost) any other tensor via a change of basis. We explain how this relation induces a manifold structure, and show how relevant quantities like gradients and geodesics can be computed efficiently. More precisely, we identify the set as a quotient of Lie groups. We also discuss how the manifold perspective can be used to integrate tensor differential equations.

[INMA] 2025-12-09 (14:00) : Matroids are equitable

At EULER (room A.002)

Speaker: László Végh (University of Bonn)

Abstract: We show that if the ground set of a matroid can be partitioned into k≥2 bases, then for any given subset S of the ground set, there is a partition into k bases such that the sizes of the intersections of the bases with S may differ by at most one, settling a conjecture by Fekete and Szabó from 2011. In the talk, I will present the surprisingly simple proof, as well as some extensions and applicaitons in fair division. I will also give an overview of related questions on matroid basis exchanges. This is based on joint work with Hannaneh Akrami, Roshan Raj, and Siyue Liu.

[INMA] 2025-12-02 (14:00) : Expanding BGP Data Horizons

At EULER (room A.002)

Speaker: Cristel Pelsser (INGI/ICTEAM/UCL)

Abstract: BGP data collection platforms as currently architected face fundamental challenges that threaten their long-term sustainability: their data comes with enormous redundancy and yet dangerous visibility gaps. GILL is a new BGP routes collection platform that can collect routes from at least an order of magnitude more routers compared to existing platforms while limiting the increase in human effort and data volume. GILL’s key principle is an overshoot-and-discard collection scheme: Any AS can easily peer with GILL and export their routes. GILL offers a lossy compression algorithm that only stores the nonredundant routes as well as lossless compression leveraging redundancy in BGP attributes, in our new bgproutes.io platform. Our new mode of data selection and delivery enables to improve BGP data analysis such as topology mapping, AS ranking, and forged origin hijack detection. We have built such a detector. DFOH is a system designed to detect forged-origin hijacks across the entire Internet. Forged-origin hijacks are typically malicious BGP hijacks where attackers manipulate the AS path of BGP messages to make them appear as legitimate routing updates. DFOH is particularly useful because the proposed BGP extensions for cryptographically verifying the validity of AS paths (e.g., BGPSec or ASPA) are challenging to deploy widely. With DFOH, operators can quickly and confidently determine when their traffic is being hijacked.

[INMA] 2025-11-25 (14:00) : A constrained Lie group approach to the modeling of dynamic mechanical systems

At EULER (room A.002)

Speaker: Olivier Bruls (University of Liège)

Abstract: This talk addresses general-purpose geometric modeling methods for a wide class of mechanical systems which includes robotic systems, biomechanical systems, deployable space structures, automotive vehicles, or industrial machines. These systems are represented as a set of rigid and flexible bodies whose dynamics is restricted due to the presence of kinematic joints and contact conditions. It is well-known that the motion of an isolated rigid body can be conveniently represented on the special Euclidean group SE(3). In the first part of the talk, I will show that this SE(3) representation can be extended to model deformable structures, such as rods, shells or more complex 3D flexible bodies. The Lie group framework can then be exploited for the construction of geometrically-consistent spatial discretization schemes and offers to the possibility to write the equations of motion in local frames (and not in an inertial frame). In the second part of the talk, I will address the treatment of bilateral constraints, which model kinematic joints, leading to a formulation of the equations of motion as a differential-algebraic equation (DAE) on a Lie group. Geometric time discretization methods for such DAE will then be discussed. Notice that unilateral constraints, which model contact conditions, can also be considered by adapting the formulation of measure differential inclusions and nonsmooth time integration schemes to the Lie group settings. Finally, a few numerical examples will be presented to illustrate the generality of the proposed framework.

[INMA] 2025-11-18 (14:20) : Data-Driven Methods for Formal Verification and Synthesis of Dynamical Systems

At Euler building (room A.002)

Speaker: Sadegh Soudjani (Max Planck Institute)

Abstract:  Ensuring the safe and reliable behavior of dynamical systems under uncertainty is a fundamental challenge in control and verification. In this talk, I will discuss recent advances in data-driven and certificate-based approaches for the formal verification and synthesis of such systems. I will first present results on necessary and sufficient certificates for reachability in stochastic systems, which provide exact characterizations of when a target set can be reached with probability one. With this, we have closed the long-standing question of characterizing certificates that are both necessary and sufficient. I will then show that the common practice of fixing a template for computing such certificates results in losing completeness: there are polynomial systems that do not admit polynomial certificates. Building on this foundation, I will discuss one of the results from our EIC SymAware project on data-driven approaches for distributionally robust control in multi-agent systems subject to logical and temporal constraints. By leveraging samples from uncertain environments, these methods enable the synthesis of controllers that are robust to distributional uncertainty while satisfying high-level behavioral specifications utilizing knowledge of the behavior of other agents in the system. Credit for the works being discussed in the talk also goes to my collaborators and hard-working students.

[INMA] 2025-11-17 (14:00) : Regularized block coordinate descent methods: Complexity and applications

At a.002

Speaker: Ernesto Birgin (Universidade de São Paulo, Brazil)

Abstract: In this work, we propose block coordinate descent methods for bound-constrained and nonconvex constrained minimization problems. Our approach relies on solving regularized models. For bound-constrained problems, we introduce methods based on models of order $p$, which exhibit asymptotic convergence to $p$th-order stationary points. Moreover, first-order stationarity with precision $\epsilon$ with respect to the variables of each block is achieved in $O(\epsilon^{-(p+1)/p})$; while first-order stationarity with precision $\epsilon$ with respect to all the variables is achieved in $O(\epsilon^{-(p+1)})$. For nonconvex constrained minimization, we consider models with quadratic regularization. Given feasibility/complementarity and optimality tolerances $\delta>0$ and $\epsilon>0$ for feasibility/complementarity and optimality, respectively, it is shown that a measure of $(\delta,0)$-criticality tends to zero; and the number of iterations and functional evaluations required to achieve $(\delta,\epsilon)$-criticality is $O(\epsilon^{-2})$. Numerical experiments illustrate the effectiveness of our methods. We apply the first method to solve the Molecular Distance Geometry Problem, while the second method is used to enhance heuristic approaches for the Traveling Salesman Problem (TSP) with neighbors, a variant of the classical TSP problem where regions in the plane must be visited instead of cities. The case where regions are described by arbitrary (nonconvex) polygons is considered.

[INMA] 2025-11-04 (14:00) : Automated algorithm analysis for time-varying optimization: tracking and regret bounds

At Euler building (room A.002)

Speaker: Fabian Jakob (University of Stuttgart)

Abstract:  Time-varying optimization problems arise across many disciplines from engineering to online learning. The development of efficient algorithms can be quite impactful, as application domains include e.g. power grid systems, mobile robotics, and portfolio optimization. The performance of such algorithms is typically assessed in two ways: (1) how well they track time-varying minima, and (2) how large their accumulated suboptimality is when the objective functions are not known in advance, also known as regret. However, deriving tracking or regret guarantees is often tedious and highly problem-specific, requiring involved and ad-hoc analyses. This talk addresses this issue. We present a novel framework for computer-aided analysis of first-order optimization algorithms for strongly convex and smooth objectives. The framework builds on casting first-order algorithms as dynamical systems and using Integral Quadratic Constraints (IQCs) for their analysis. We recap the concept of IQCs and present an extension to the time-varying setting, which allows us to model temporal variations as disturbances acting on the algorithm dynamics. Based on this, we show how tracking and regret certificates of an algorithm can be obtained as the solution of a semidefinite program and demonstrate numerically how the choice of algorithm affects the performance and sensitivity to time-variations.

[INMA] 2025-10-29 (14:00) : Internal Optimization Seminar

At Euler building (room A.002)

Speaker: No author specified

Abstract: This is a weekly seminar which explores cutting-edge research and applications in mathematical optimization, spanning theory, algorithms, and real-world problem-solving. Each session features talks from leading researchers, practitioners, or graduate students, covering various topics. Attendees include mathematicians, engineers, and data scientists — all united by a passion for optimization. Open to all. No registration required.

[INMA] 2025-10-21 (14:00) : Over-Approximation Methods for Safe Control: Lifting and Preview Information

At Euler building (room A.002)

Speaker: Aspeel, Antoine

Abstract:  Ensuring safety in the control of nonlinear systems is a central challenge in control theory. Over-approximations address this by replacing a deterministic nonlinear system with a nondeterministic (piecewise) linear one, enabling the use of control synthesis techniques with formal guarantees for the original dynamics. The presentation will cover two recent approaches that reduce conservatism in over-approximations. Lifted over-approximations represent the system in higher dimension, providing additional degrees of freedom. Over-approximations with preview reinterpret the approximation error as input-dependent preview information, leading to policies that depend jointly on the state and the error. The resulting concretization problem—recovering a valid input for the true system from such a policy—is formulated as a fixed-point equation, enabling efficient computation.

[INMA] 2025-10-16 (13:00) : Malware Detection with Machine Learning: Challenges and Perspectives

At Shannon room, Maxwell building

Speaker: Serena Lucca (ICTEAM) , and Samy Bettaied (ICTEAM)

Abstract: This presentation explores some challenges and perspectives of applying machine learning to malware detection, drawing insights from two complementary studies. The first investigates the surprising effectiveness of simple models—such as One-Rule and AdaBoost—on state-of-the-art malware detection datasets, revealing that a small subset of dominant features often drives classification performance, leading to minimal differences between simple and deep learning approaches. The second study provides a systematic comparison of tabular and graph-based feature representations under unified conditions, evaluating their trade-offs in computational cost, detection accuracy, and robustness to adversarial attacks. Together, these works question the common assumption that complex models or sophisticated feature types always yield superior results, and instead highlight the importance of understanding feature dominance, dataset biases, and practical constraints in real-world malware detection.

[INMA] 2025-10-15 (14:00) : Internal Optimization Seminar

At Euler building (room A.002)

Speaker: No author specified

Abstract: This is a weekly seminar which explores cutting-edge research and applications in mathematical optimization, spanning theory, algorithms, and real-world problem-solving. Each session features talks from leading researchers, practitioners, or graduate students, covering various topics. Attendees include mathematicians, engineers, and data scientists — all united by a passion for optimization. Open to all. No registration required.

[INMA] 2025-10-14 (14:00) : Newcomers seminars (PhDs)

At Euler building (room a.002)

Section 1: Low-rank PSD matrix completion

Speaker: Sophie Lequeu (PhD UCLouvain/INMA)

Abstract: While convexity is traditionally seen as essential for solving optimization problems, many nonconvex ones pose no significant issues in practice. Moreover, simpler nonconvex formulations are generally more compact and amenable to parallel solving, even when an equivalent convex formulation exists. This justifies the interest in studying the global landscape of selected optimization problems, in order to determine the characteristics that distinguish problems with benign nonconvexity from those with non-benign nonconvexity. In this research project, we will study the Burer–Monteiro nonconvex reformulation of the low-rank PSD matrix completion problem. For sparsity patterns corresponding to chordal graphs, a classical result guarantees the existence of a low-rank solution, encouraging the use of this reformulation. The goal is to determine conditions under which this formulation has no spurious local minima, by studying characteristics of the sparsity pattern.

Section 2: Novel deep learning architectures for the detection of the stochastic gravitational wave background.

Speaker: Antonin Oswald (PhD UCLouvain/INMA)

Abstract: We propose to advance the understanding of the stochastic gravitational wave background by proposing new machine learning architectures specifically dedicated to processing correlation matrices signal representations. These architectures will heavily rely on the geometry of the manifold of positive-definite matrices, to which the correlation matrices representing classically the signal rely. We will explore several directions, aiming to account for time- and frequency dependency in the correlation representations. We will then use our proposed novel architectures for denoising correlation matrices in view of subsequent SGWB detection.

Section 3: Path-complete reinforcement learning

Speaker: Lea Ninite (PhD UCLouvain/INMA)

Abstract: Reinforcement Learning (RL) has achieved remarkable success in complex control tasks, yet its lack of theoretical guarantees limits its use in safety-critical systems. In RL, the Q-function satisfies a decrease condition similar to that of a Lyapunov function, but this relation is typically enforced through heuristic, data-driven updates, which hinders robustness and interpretability. In contrast, Path-Complete Lyapunov Functions (PCLFs) offer a systematic and combinatorial framework for encoding stability through sets of local decrease conditions on a graph structure. This PhD project aims to bridge these two paradigms by developing a Path-Complete Reinforcement Learning (PCRL) framework, introducing a path-complete relaxation of the Bellman equation. As a first step, we focus on computing an upper bound of the value function for arbitrarily switched linear systems using path-complete graphs, where each node encodes a quadratic function satisfying Bellman-like inequalities. Preliminary results show that this construction can yield tight upper bounds on the true value function, highlighting the potential of path-complete methods to bring theoretical structure to learning-based control.

[INMA] 2025-10-08 (14:00) : Internal Optimization Seminar

At Euler building (room A.002)

Speaker: No author specified

Abstract: This is a weekly seminar which explores cutting-edge research and applications in mathematical optimization, spanning theory, algorithms, and real-world problem-solving. Each session features talks from leading researchers, practitioners, or graduate students, covering various topics. Attendees include mathematicians, engineers, and data scientists — all united by a passion for optimization. Open to all. No registration required.

[INMA] 2025-10-07 (14:00) : Communication-efficient distributed optimization algorithms

At Euler building (room A.002)

Speaker: Laurent Condat (King Abdullah University of Science and Technology (KAUST))

Abstract:  In distributed optimization and machine learning, a large number of machines perform computations in parallel and communicate back and forth with a server. In particular, in federated learning, the distributed training process is run on personal devices such as mobile phones. In this context, communication, that can be slow, costly and unreliable, forms the main bottleneck. To reduce it, two strategies are popular: 1) local training, which consists in communicating less frequently; 2) compression. Also, a robust algorithm should allow for partial participation. I will present several randomized algorithms we developed recently, with proved convergence guarantees and accelerated complexity. Our most recent paper “LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression” has been presented as a Spotlight at the ICLR conference in April 2025.

[INMA] 2025-09-30 (14:00) : Toward Resilient Operation of Large-Scale Cyber-Physical Human Systems

At Euler building (room A.002)

Speaker: Ahmad Al-Dabbagh (University of British Columbia)

Abstract:  Examples of large-scale cyber-physical human systems are many in society, such as in manufacturing and energy applications. The systems rely on a high degree of coupling between their cyber, physical, and human components, where operational information is exchanged between the components using communication networks. The involved coupling and the communication networks introduce vulnerabilities which jeopardize the reliability and security of the systems. This seminar provides an overview of decision-making methods for control and monitoring of large-scale cyber-physical human systems, using control theory and artificial intelligence, while focusing on practical challenges related to cybersecurity, fault diagnosis, and alarm management.

[INMA] 2025-09-23 (14:00) : Newcomers seminars (PhDs)

At Euler building (room a.002)

Section 1: Random Embeddings for Deep Learning: Improving Scalability and Generalization

Speaker: Roy Makhlouf (PhD UCLouvain/INMA)

Abstract: Increasingly powerful processing units have led to a dramatic surge in the number of parameters of deep neural networks (DNNs), for which training comes with heavy computational costs. As a consequence, it is essential to develop more scalable algorithms for training DNNs. This work aims to explore the possible benefits of low-dimensional embeddings as a dimensionality reduction technique for DNN training. Instead of considering the entire parameter space, the idea is to restrict training to a lower-dimensional subspace, thereby significantly reducing computational cost. This approach is motivated by prior numerical results, which suggest that training overparameterized neural networks within a subspace of very small dimension still allows to achieve a high test accuracy. Building on my Master's thesis results, we will first conduct a deeper investigation of random Gaussian embeddings for DNN training under the assumption that the training loss exhibits anisotropic variability. That is, when it varies very slowly along some directions and possibly much faster along others. This setting often occurs in overparameterized neural networks training, where not all parameters influence the training loss equally. We will then consider more structured embeddings known as sparse embeddings, which are closer to techniques already used in deep learning. Finally, we will look at how random embeddings can help avoid sharp spurious minima, a class of minima expected to harm model generalization.

Section 2: Analysis of Hidden Convexity in Neural Networks and Transformers: Toward More Efficient and Robust Deep Learning.

Speaker: Adeline Colson (PhD UCLouvain/INMA)

Abstract: While nonconvexity is traditionally viewed as a challenge in optimization, many machine learning models exhibit a phenomenon known as benign nonconvexity, where nonconvex formulations are surprisingly tractable and often more scalable than their convex counterparts. The research project aims to understand and leverage this phenomenon to identify models that are both expressive and efficient to train, develop strategies to escape spurious local minima, and propose new formulations with benign nonconvexity across domains. Hidden convexity refers to a convex structure that is not apparent in the original nonconvex problem. By reformulating the problem using local optimality conditions (first and second order), one can analyze it via a convex program. This allows global properties, like optimality of local minima, to be inferred from local conditions.

Section 3: Scalable Control Design for Networked Systems: Coordination through Local Cooperation.

Speaker: Jonas Hansson (Lund University,Sweden)

Abstract: In this talk, I will present a compositional framework for consensus and coordination, with applications to vehicular formations. The approach, called serial consensus, constructs high-order protocols by cascading simpler first-order dynamics, which makes stability transparent and enables scalable performance guarantees such as string stability. I will also discuss extensions to nonlinear settings, where the framework accommodates constraints such as saturation and time-varying topologies. Altogether, the results show how distributed controllers based only on local relative measurements can ensure robust and scalable coordination in large-scale networks.

[INMA] 2025-09-16 (14:00) : Safety in the Face of Uncertainty: When is a Scenario Decision-Making Algorithm Safe?

At Euler building (room A.002)

Speaker: Guillaume Berger (UCLouvain)

Abstract: Making risk-aware decisions in the face of uncertainty is a central problem in many applications of engineering such as autonomous transportation, energy planning, medical devices, etc. Indeed, in these applications, failures or errors come at a high cost, so that it is important to bound the probability of such events. Nevertheless, this problem is often very challenging in practice because the probability distribution of the uncertainty is often unknown to the decision maker, which must thus make decisions in a black-box way. Scenario decision-making is a powerful data-driven approach to risk-aware decision-making, consisting in drawing samples (called scenarios) of the uncertainty and making a decision based on these samples. A key question is when such scenario-based decisions have a low risk. In this talk, I will review the main techniques from the literature for providing such bounds on the risk, and will show that they are incomparable, in that none is more general (i.e., non-vacuous on a larger class of problems) or less conservative than the other. I will then present a more general bound, inspired by the connection between scenario decision-making algorithms, set operators and VC theory. Finally, I will demonstrate the usefulness of the new bound on problems from scenario optimization.

[INMA] 2025-08-20 (14h) : Efficient Distance-Adaptive Subgradient Methods

At Euler building (room A.002)

Speaker: Anton Rodomanov (CISPA, Germany)

Abstract: Subgradient methods based on the idea of gradient normalization are an appealing class of algorithms for convex optimization because they automatically adapt to the local growth rate of the objective and require no problem-specific parameters---except for a reasonably accurate estimate of the initial distance to the solution. Overestimating or underestimating this distance can, however, substantially slow convergence. We address this limitation by incorporating into normalization-based methods a recently proposed technique that dynamically estimates the distance via the displacement of iterates from the starting point. This removes the need for any problem-specific information, while preserving the adaptability of the base methods and ensuring rigorous convergence guarantees. Illustrative examples include nonsmooth Lipschitz functions, Lipschitz- or Hölder-smooth functions, functions with high-order Lipschitz derivatives, quasi-self-concordant functions, and $(L_0, L_1)$-smooth functions. We further extend the approach to problems with functional constraints using a simple switching strategy.

[INMA] 2025-06-17 (14h) : Matrix Factorization and Approximation of Nonnegative Rank Two

At Euler building (room A.002)

Speaker: Van Dooren, Paul

Abstract: We consider the problem of finding the best nonnegative rank two approxi- mation of an arbitrary nonnegative matrix. We first provide a parametrization of all possible nonnegative factorizations of a nonnegative matrix of rank 2. We then use this result to construct a suboptimal, but cheaply computable, solution of the nonnegative rank 2 approximation problem for an arbitrary nonnegative matrix input; this can then be used as a starting point for the Alternating Least Squares method, resulting in both an improved computational complexity and an enhanced output quality. We provide numerical experiments to support these claims. We also look at some variants of the problem, including symmetry con- straints and three-way factorizations. This is joint work with Etna Lindy and Vanni Noferini (Aalto University).

[INMA] 2025-05-20 (14h) : Model-Based and Data-Driven Interventions in Network Games

At Euler building (room A.002)

Speaker: Nima Monshizadeh (University of Groningen)

Abstract: In modern cyber-physical-human systems like power grids and traffic networks, self-interested decisions by users or firms often lead to inefficiencies such as congestion, blackouts, and systemic risks. These challenges underscore the need for effective intervention mechanisms to coordinate self-interested behavior. However, designing effective interventions with guaranteed performance is challenging, as planners typically lack detailed knowledge of users’ private preferences or behaviors. This informational gap and privacy considerations complicate the prediction of user responses and hinder the development of suitable control strategies. In this talk, we examine the problem of intervention design in network games, where agents’ cost or utility functions are interdependent; as exemplified by applications such as multi-commodity markets. The central question is how the type of information available to the planner — ranging from full to partial knowledge of utility functions, network structure, or desired target profiles — shapes the design of effective interventions. We also give special attention to scenarios in which the planner has no a priori knowledge of agent cost functions and must rely instead on historical observations of agent actions and past interventions. Given these diverse informational settings, we discuss a range of static, dynamic, adaptive, and data-driven intervention strategies aimed at steering the system toward socially desirable outcomes. The results illustrate how combining control-theoretic, game-theoretic, and data-driven insights enables the design of interventions that are effective under various informational constraints.

[INMA] 2025-05-06 (14h) : Axisymmetric magnetic control in ST40

At Euler building (room A.002)

Speaker: Benjamin Vincent (UCLouvain)

Abstract: Tokamaks magnetically confine plasmas (i.e. ionised gas) up to temperatures where the nuclear fusion reaction is sustainable. The real-time operation of a tokamak relies on a Plasma Control System, which is responsible for data acquisition, pulse supervision and feedback control. The plasma current, position, and shape can be actuated by the poloidal field coils. The talk will introduce the concept of tokamaks and magnetically confined plasmas. The axisymmetric magnetic control problems will be presented and illustrated in the context of the spherical tokamak ST40.

[INMA] 2025-04-16 (15h) : Gradient-based optimization in CFD: from nuclear fusion to small modular fission reactors

At Euler building (room A.002)

Speaker: Niels Horsten (IMMC,UCLouvain)

Abstract: Gradient-based optimization is a powerful tool for advancing engineering applications. Its success relies on three key ingredients: accurate simulation models, efficient gradient calculations, and carefully chosen optimization methods. In this seminar, I will demonstrate how I developed fast, physics-based CFD models for nuclear fusion that are now being used for the design of future reactors. A particular focus will be on the challenges and solutions for obtaining gradients from Monte Carlo particle simulations, which are critical for modeling neutral-plasma interactions in fusion and neutron transport in fission systems. Lastly, I will discuss the application of these methods to the design of heat exchangers for lead-cooled small modular reactors (SMRs). The unique properties of liquid lead, particularly its low Prandtl number, introduce additional complexities for the optimization.

[INMA] 2025-04-15 (14h) : Open problems about the simplex method

At Euler building (room A.002)

Speaker: Sophie Huiberts (LIMOS, Clermont Auvergne University)

Abstract: The simplex method is a very efficient algorithm. In this talk we see a few of the state-of-the-art theories for explaining this observation. We will discuss what it takes for a mathematical model to explain an algorithm’s qualities, and whether existing theories meet this bar. Following this, we will question what the simplex method is and if the theoretician's simplex method is the same algorithm as the practitioner's simplex method. Along the way I will share some anecdotes about linear programming history.

[INMA] 2025-04-09 (15h) : Stochastic second-order optimization: global bounds, subspaces, and momentum

At Euler building (room A.002)

Speaker: Doikov, Nikita

Abstract: In this talk, we present stochastic second-order algorithms for solving general non-convex optimization problems. Using the cubic regularization, we prove global convergence rates for our methods. We will discuss two techniques that improve the properties of our algorithms in large-scale cases: stochastic subspaces, to deal with high-dimensional problems, and stochastic methods with momentum. The latter technique provably improves the variance of stochastic estimates and allows the method to converge for any noise level. This is in stark contrast to all existing stochastic second-order methods for non-convex problems, which typically require large batches.

[INMA] 2025-04-08 (14h) : Multi-product Supply Function Equilibria

At Euler building (room A.002)

Speaker: Bert Willems (UCLouvain-LIDAM)

Abstract: We solve for Nash equilibria in a procurement auction with multiple heterogeneous divisible goods. There are (dis)economies of scope in production and goods could be substitutes or complements for the procurer. Before demand is realized, each firm offers a vector of supply functions where supply of a good depends on the prices of all goods. This is related to the organization of the product-mix auction and electricity markets with complex bids. We show that outcomes are not influenced by bundling of the goods. For quadratic costs and linear demand, we can use this property to transform the multi-product problem into an equivalent set of separated markets, which can be analyzed independently. We show that Lerner and pass-through tensors can be used to characterize mark-ups and welfare losses in a multi-product market. Eigenvalues of the tensors are fundamental properties, as they do not depend on bundling.

[INMA] 2025-03-31 (11h) : ICTEAM Colloquium series

At Maxwell building (room Shannon A.105)

Speaker: Sepulchre, Rodolphe

Abstract: Regulation theory is grounded in the internal model principle, which states that exact regulation requires an exact internal model of the external signals to be regulated. How to reconcile this calibration principle with systems made of uncertain and variable components ? How do animals achieve regulation in changing and complex environments? In this talk, Professor Sepulchre will propose that reliable regulation is possible in uncertain machines that regulate events rather than trajectories. He will highlight the role of excitability and synaptic coupling in a theory of event regulation .

[INMA] 2025-03-25 (14h) : Recent trends in Combinatorial Optimization Augmented Machine Learning

At Euler building (room A.002)

Speaker: Axel Parmentier (Ecole Nationale des Ponts et Chaussées)

Abstract: Combinatorial optimization augmented machine learning (COAML) is a novel and rapidly growing field that integrates methods from machine learning and operations research to tackle data-driven problems that involve both uncertainty and combinatorics. These problems arise frequently in industrial processes, where firms seek to leverage large and noisy data sets to better optimize their operations. COAML typically involves embedding combinatorial optimization layers into neural networks and training them with decision-aware learning techniques. This talk provides an overview of the field, covering its main applications, algorithms, and theoretical foundations. We will notably emphasize recent contributions on empirical risk minimization and the resulting theoretical guarantees. We also demonstrate the effectiveness of COAML on contextual and dynamic stochastic optimization problems, as evidenced by its winning performance on the 2022 EURO-NeurIPS challenge on dynamic vehicle routing.

[INMA] 2025-03-18 (14h) : Practical computation of the diffusion MRI signal of realistic neurons based on Laplace eigenfunctions

At Euler building (room A.002)

Speaker: Jing-Rebecca Li (INRIA-ENSTA)

Abstract: The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium such as brain tissue can be modeled by the Bloch-Torrey partial differential equation. The spatial integral of the solution of this equation in realistic geometry provides a gold-standard reference model for the diffusion MRI signal arising from different tissue micro-structures of interest. A closed form representation of this reference diffusion MRI signal called matrix formalism, which makes explicit the link between the Laplace eigenvalues and eigenfunctions of the biological cell and its diffusion MRI signal, was derived 20 years ago. In addition, once the Laplace eigendecomposition has been computed and saved, the diffusion MRI signal can be calculated for arbitrary diffusion-encoding sequences and b-values at negligible additional cost. Up to now, this representation, though mathematically elegant, has not been often used as a practical model of the diffusion MRI signal, due to the difficulties of calculating the Laplace eigendecomposition in complicated geometries. We present a simulation framework that we have implemented inside the MATLAB-based diffusion MRI simulator SpinDoctor that efficiently computes the matrix formalism representation for realistic neurons using the finite element method. We show that the matrix formalism representation requires a few hundred eigenmodes to match the reference signal computed by solving the Bloch-Torrey equation when the cell geometry originates from realistic neurons. As expected, the number of eigenmodes required to match the reference signal increases with smaller diffusion time and higher b-values. We also convert the eigenvalues to a length scale and illustrate the link between the length scale and the oscillation frequency of the eigenmode in the cell geometry.

[INMA] 2025-03-11 (14h) : Optimizing the Present and Future of Smart Electric Power Grids

At Euler building (room A.002)

Speaker: Miguel Anjos (University of Edinburgh)

Abstract: A smart grid is the combination of a traditional electrical power system with information and energy both flowing back and forth between suppliers and consumers. This new paradigm introduces major challenges such as the integration of decentralized energy generation, the increase of electric transportation, and the need for electricity consumers to play an active role in the operations of the grid. This presentation will overview the changes in progress in several countries, present some recent research on mathematical optimization models to support these changes, and conclude with a summary of the opportunities for optimization to contribute to the future success of smart grids.

[INMA] 2025-03-04 (14h) : Correctness guarantees for the Burer-Monteiro heuristic on MaxCut-type problems

At Euler building (room A.002)

Speaker: Irene Waldspurger (Université Paris-Dauphine)

Abstract: We consider semidefinite optimization problems, that is, where one has to minimize a convex function on the set of semidefinite positive matrices. Sometimes, we know a priori that the solution will be of low rank (for instance, rank 1). This information can be used to make numerical solvers faster. This is the goal of the Burer-Monteiro factorization: we represent the unknown matrix as a product of "thin" matrices (i.e. with few columns); then, we optimize the factors instead of the whole square matrix. Since it reduces the dimensionality of the problem, this method allows for significant speedups. However, it makes the problem non-convex, thereby possibly introducing non-optimal critical points which can cause the solver to fail. With Faniriana Rakoto Endor, we have considered the specific category of so-called "MaxCut-type" semidefinite problems. We have exhibited a simple property which guarantees that the Burer-Monteiro factorization associated with one of these problems has no non-optimal critical point.

[INMA] 2025-03-03 (14h) : Systematic Design of Control Barrier Function

At Euler building (room A.002)

Speaker: Marco Nicotra

Abstract: Control Barrier Functions (CBFs) have become an increasingly popular tool for constrained control due to their simplicity and performance. Despite promising results in many applications, the widespread adoption of CBFs has been limited by the absence of a systematic method for designing CBFs for nonlinear systems with arbitrary state and input constraints. This talk will provide a brief overview of CBFs and show the dangers of using improper CBFs to enforce constraints. Then, it will draw parallels between CBFs and a different Safety Filter framework known as Reference Governors (RGs). Using existing tools from the RG literature, the talk will introduce a simple and systematic approach for CBF design.

[INMA] 2025-02-25 (14h) : Noise modelling and analysis of nonlinear circuits and systems: From statistical mechanics to nonlinear dynamics

At Maxwell building (Shannon room)

Speaker: Michele Bonnin (Polytechnico di Torino)

Abstract: The theory of random fluctuations in linear systems does not extend to the internal noise in nonlinear systems. In this talk, we will review the standard methods for modeling internal noise in linear circuits and systems. After examining the thermodynamic requirements for a consistent model, it will be demonstrated that applying linear-system methods to nonlinear systems leads to circuit behaviors that violate thermodynamic principles. A series of tests will be introduced to assess whether a given noise model for nonlinear devices aligns with accepted thermodynamic principles. We will explore the validity of Gaussian and shot-noise models for nonlinear devices, as well as the limitations and shortcomings of approaches based on Langevin and Fokker-Planck equations. Additionally, a stochastic description using the master equation will be presented, which relates to the one-time probability density of Markov processes governing the fluctuating electrical quantities. Finally, we will discuss the implications of these findings for emerging computational paradigms such as neuromorphic and reservoir computing.

[INMA] 2025-02-18 (14h) : Deep Learning Mass Mapping with Conformal Predictions

At Euler building (room A.002)

Speaker: Hubert Leterme (Ensicaen, CEA Paris-Saclay)

Abstract: In this talk, I will introduce a plug-and-play (PnP) approach for mapping the distribution of dark matter in the sky using weak gravitational lensing and noisy shear measurements. This method aims to deliver accurate and efficient mass estimates without the need to retrain deep learning models for each new galaxy survey or sky region. Instead, a single model is trained on simulated convergence maps with Gaussian white noise corruption. To enhance reliability, we incorporate a distribution-free uncertainty quantification (UQ) method, conformalized quantile regression (CQR), into the mass mapping framework. Leveraging a simulation-derived calibration set, CQR provides rigorous coverage guarantees, independent of any specific prior data distribution. We benchmark this PnP approach against existing mass mapping methods, including Kaiser-Squires, Wiener filtering, MCALens, and DeepMass. Our results demonstrate that while miscoverage rates, after calibration with CQR, remain consistent across methods, the choice of mass mapping technique significantly impacts the size of the resulting error bars.

[INMA] 2025-02-11 (14h) : Climate science : a goldmine for applied mathematicians

At Euler building (room A.002)

Speaker: Francois Massonnet (UCLouvain - PHYS/ELIC)

Abstract: Climate science presents a wealth of mathematical challenges, many of which are unknown to the applied mathematics community. From linear feedback theory to nonlinear dynamical systems, from optimization in decision-making to inverse problems in detection and attribution, applied mathematics is fundamental to our understanding of the climate system. Numerical methods form the backbone of climate models, while machine learning is transforming predictions, weather regime classification, and the tracking of extreme events such as hurricanes and ocean eddies. In this talk, I will present concrete examples where applied mathematics plays a crucial role in climate science, highlighting opportunities for deeper collaboration between our institutes. Everyone is welcome!

[INMA] 2025-02-04 (14h) : Newcomers seminars (PhDs)

At Euler building (room a.002)

Section 1: Large Language Models for Safety-Critical Control

Speaker: Amir Bayat (PhD UCLouvain/INMA)

Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in recent years. Their proficiency in tasks such as question answering, text summarization, and code generation has revolutionized various fields, including education, healthcare, and more. LLMs are user-friendly, offering intuitive interfaces that make interaction seamless. However, despite these strengths, they fall short in engineering applications like robotic task planning and execution. In their current state, LLMs are neither reliable nor safe for performing such actions. On the other hand, symbolic control, also known as abstraction-based control, is a powerful method for managing complex cyber-physical systems. This approach involves designing a controller for an abstracted version of the system and then refining it to control the original system. While effective, this method requires formal language specifications, which demand significant training and expertise to create. Our long-term goal is to integrate the strengths of both LLMs and symbolic control for cyber-physical systems. By leveraging the user-friendly interaction capabilities of LLMs alongside the safety and reliability of symbolic control, we aim to develop systems that ensure both usability and robustness

Section 2: Data-driven Event-triggered Control for Discrete-time LTI Systems

Speaker: Vijayanand Digge (PhD UCLouvain/INMA)

Abstract: Inspired by recent work on data-driven control, this work presents data-driven event-triggered control strategies for discrete-time linear time-invariant (LTI) systems. The results presented do not require explicit identification of the system parameters and are based on the input and state data collected from the system during an open-loop experiment. The design of event-triggered control consists of two stages: finding a state feedback controller that exponentially stabilizes the system and designing an event-triggered policy that determines the instances at which the control law needs to be updated. The proposed designs in both stages involve solving semi-definite programs with data-dependent linear matrix inequalities (LMIs) as constraints. For the event-triggered implementation, we employ a relative thresholding mechanism, and the range of the thresholding parameter is derived using S-procedure. Further, conditions on the thresholding parameter are derived that ensure both pre-specified exponential convergence and non-trivial event-triggering.

Section 3: Impact of Fibre Assignments on Fourier Space Galaxy Clustering Statistics

Speaker: Jana Jovcheva (PhD UCLouvain/INMA)

Abstract: Due to instrumental limitations, spectroscopic redshift surveys cannot measure the redshifts for all targets. The physical size of the spectroscopic fibres sets a limit on the angular separation between two galaxies at which their redshifts can be measured simultaneously, known as the fibre collision scale. Fibre allocation algorithms attempt to maximise the number of observed targets and minimise the effects of collisions, but it remains impossible to efficiently measure the redshift of every target. The resulting incompleteness in the data biases galaxy clustering statistics such as the power spectrum and bispectrum, which can prevent robust inference of cosmological parameters. I quantify the impact of fibre assignments on the power spectrum and bispectrum for the Dark Energy Spectroscopic Instrument (DESI) first generation mock catalogues, assess the effectiveness of a simple nearest neighbour correction at recovering the true statistics, and consider two alternative schemes to improve the accuracy of clustering measurements. This analysis provides insights for mitigating fibre collision effects and enhancing the cosmological utility of DESI data and future surveys. .

[INMA] 2024-12-03 (14h) : MadNLP: a GPU-ready interior-point solver

At Euler building (room A.002)

Speaker: François Pacaud ((Mines Paris - PSL))

Abstract: The interior-point method (IPM) has become a standard algorithm to solve large-scale optimization problems. Traditionally, IPM solves a sequence of symmetric indefinite linear systems, or Karush-Kuhn-Tucker (KKT) systems, that are increasingly ill-conditioned as we approach the solution. As such, solving a KKT system with traditional sparse factorization methods requires numerical pivoting, making parallelization difficult. We present two novel interior-point methods that circumvent this issue. The first method intervenes at the level of the linear algebra: it condenses IPM's KKT system into a symmetric positive-definite matrix and solve it with a Cholesky factorization, stable without pivoting. Although condensed KKT systems are more prone to ill-conditioning than the original ones, they exhibit structured ill-conditioning that mitigates the loss of accuracy. The second method mixes IPM with an Augmented Lagrangian method (Auglag-IPM). The Augmented Lagrangian term adds an implicit dual regularization to the problem; as a result the KKT systems write as symmetric quasi-definite (SQD) matrices, also factorizable without pivoting. Aside, the Auglag-IPM is able to solve degenerate optimization problems, in particular nonlinear programs with complementarity constraints. Both methods have been implemented on the GPU using MadNLP.jl, an optimization solver interfaced with the NVIDIA sparse linear solver cuDSS and with the GPU-accelerated modeler ExaModels.jl. Our experiments on large-scale OPF instances reveal that GPUs can attain up to a tenfold speed increase compared to CPUs. In addition, Auglag-IPM is able to solve complicated optimization problems that are not solvable by a classical IPM algorithm.

[INMA] 2024-11-26 (14h) : Forward electricity market with nonconvexities

At Euler building (room A.002)

Speaker: Quentin Lété ((UCLouvain))

Abstract: Thermal generators must decide whether to produce electricity well in advance, as starting up or shutting down operations require lead time. By participating in forward auctions, like day-ahead or intraday auctions, they can lock in prices, ensuring that they will not make a loss when committing to produce power in the future. Their participation leads to the auction turning nonconvex as thermal generators must communicate indivisible fixed costs associated with start-up and shut-down operations. How to price electricity in such a nonconvex auction is a long-standing debate, but a consensus seems to be emerging towards convex hull pricing. However, most existing literature studies the nonconvex forward market in isolation, ignoring the relationship with the convex real-time market. In this talk, we propose a model to study nonconvex pricing in two-settlement markets, accounting for both day-ahead and real-time markets with financial participants. We show on an illustrative example that, in this case, most of the theoretical properties of convex hull pricing (efficiency and loss opportunity costs minimization) cease to hold. We then present simulation results on a case study calibrated on the PJM market.

[INMA] 2024-11-19 (14h) : Large-scale Stochastic Optimization: Approximations and Distributed Methods

At Euler building (room A.002)

Speaker: Ashish Cherukuri (University of Groningen)

Abstract: This talk focuses on stochastic optimization problems defined over a network and explores data-driven distributionally robust (DR) solution methods to solve it. Specifically, we will look at chance-constrained optimization that finds application in generation planning problem and expectation minimization problem that is motivated by distributed optimization and federated learning. The DR formulations of the problem have attractive statistical guarantees but pose computational difficulties. The talk will provide algorithms to handle these challenges, paying special attention to the large-scale nature of the problem and the fact that the data about the uncertainty cannot be aggregated at one single location in the network. We will end the talk with future challenges and research directions.

[INMA] 2024-11-18 (10h) : Scalable high-order consensus for multi-agent coordination

At Euler building (room A.207)

Speaker: Jonas Hansson (Lund University,Sweden)

Abstract: In this talk, I will present a novel control design for vehicular formations, introducing an alternative approach to conventional consensus protocols for second and high-order systems. The design is motivated by the closed-loop system, which we construct to match the dynamics of first-order systems connected in series, and is therefore called serial consensus. Due to the design, the stability of the closed-loop can be easily proven. Perhaps more interestingly, the control design can also be adapted to achieve both scalable and robust stability and performance (string stability), which is particularly interesting for controlling large vehicular platoons. Noteworthy, this is achieved with a distributed controller that only uses local relative measurements. The theoretical findings will be illustrated through examples.

[INMA] 2024-11-12 (14h) : Null space gradient flows and applications to topology optimization

At Euler building (room A.002)

Speaker: Florian Feppon ((KU Leuven))

Abstract: In this seminar, I will present the "Null Space Optimizer" [1,2] which is an algorithm developed for solving nonlinear optimization programs with differentiable equality and inequality constraints. The main principle of the algorithm is to discretize a dynamical system whose trajectories simultaneously and gradually correct the violation of the constraints while minimizing the objective function. As a result, one of its appealing aspects comes from its relative independence to the need for tuning unintuitive algorithm parameters. I will show some applications of the algorithm to topology optimization and discuss a recent extension that enables the optimizer to solve problems with both a large design spaces and many constraints with sparse jacobian matrix. [1] Feppon, F., Allaire, G. and Dapogny, C. Null space gradient flows for constrained optimization with applications to shape optimization (2020). ESAIM: COCV, 26 9. [2] Feppon F. Density based topology optimization with the Null Space Optimizer: a tutorial and a comparison (2024). Structural and Multidisciplinary Optimization, 67(4), 1-34.

[INMA] 2024-11-06 (15h) : Subsampled cubic regularization method for finite-sum minimization

At Euler building (room A.002)

Speaker: Max.L.N. Gonçalves ((Universidade Federal de Goiás, Brazil))

Abstract: In this talk, we propose and analyse a subsampled Cubic Regularization Method (CRM) for solving finite-sum optimization problems. The new method uses random subsampling techniques to approximate the functions, gradients and Hessians in order to reduce the overall computational cost of the CRM. Under suitable hypotheses, first- and second-order iteration-complexity bounds and global convergence analyses are presented. We also discuss the local convergence properties of the method. Numerical experiments are presented to illustrate the performance of the proposed scheme.

[INMA] 2024-11-05 (14h) : Addressing data analysis challenges in next-generation gravitational-wave detectors

At Euler building (room A.002)

Speaker: Justin Janquart (UCLouvain,IRMP)

Abstract: The first detection of a gravitational wave signal in 2015 led to the opening of a new observational window on the Universe. Since then, several detector upgrades have been made, leading to more routine detections with a rate of one to a few events a week currently. To continue increasing the number of detected signals and the observational accuracy, the next generation of gravitational wave detectors is planned with Einstein Telescope in Europe and Cosmic Explorer in the United States. These detectors will see tens to hundreds of thousands of gravitational wave signals, and some will be extremely loud. This will lead to interesting new scientific avenues such as, for example, unprecedented tests of general relativity, the possibility to probe the Universe at unprecedented cosmic scales, and look for potential dark matter candidates. However, these improvements come with a cost: the data analysis systems will be challenged due to overlapping signals, strong astrophysical stochastic backgrounds, and the lack of time to characterize the noise, amongst other things. Here, I explain how these problems arise, what could be their effect if not correctly accounted for, and some of the avenues that have been explored in recent years.

[INMA] 2024-10-24 (14h) : Alternating projections and Convex infeasibility: theory and applications

At Euler building (room A.002)

Speaker: Luiz-Rafael dos Santos (Universidade Federal de Santa Catarina, Brazil)

Abstract: In this talk, we first demonstrate that inconsistency arising from the infeasibility of closed convex sets can be leveraged to enhance the performance of alternating projections and their corresponding convergence rates, surprisingly leading to finite convergence under suitable error bounds. In the second part, we apply this concept to develop a new and numerically competitive method for solving the basis pursuit problem. Basis pursuit (BP) seeks the vector with the smallest l1-norm among the solutions to a given linear system, and it is a well-known convex relaxation of the sparse affine feasibility (SAF) problem. SAF aims to find sparse solutions to underdetermined systems, a key issue in compressed sensing, a technique used to recover sparse signals from incomplete measurements. Although SAF is NP-hard, there are instances where its solution coincides with that of BP. The importance of basis pursuit led to a great deal of research into efficient methods for solving it, particularly in large-scale settings, often via linear programming reformulations. However, our approach tackles basis pursuit in its original form, employing a scheme that uses alternating projections within subproblems. These subproblems are purposefully inconsistent, involving two disjoint sets. Numerical experiments show that the proposed algorithm is competitive.

[INMA] 2024-10-22 (14h) : Hidden convexity in linear neural networks

At Euler building (room A.002)

Speaker: Legat, Benoît

Abstract: Training neural networks involves minimising a loss function that is nonconvex with respect to the network’s weights. Despite this nonconvexity, when the optimization converges to a local minimum, it is often close to globally optimal. This transfer from local properties to global properties is often achieved through convexity in optimization which neural networks seem to lack, or is it hidden ? There are two sources of nonconvexity in neural networks : 1) the nonlinear activation functions and 2) the multilinear product of the weight matrices. Interestingly, recent research has demonstrated that the second source does not, on its own, lead to local minima that are not global when paired with a mean squared error loss. Although this result is promising, the complexity of the proof limits its generalization to more complex models, such as those with nonlinear activation functions or other loss structures. In this talk, we reveal the convexity hidden in the problem and show how it allows for a simpler and more insightful proof. By exposing this underlying structure, we aim to open the door to recognizing which types of models are more likely to train well and to extend this understanding to other machine learning architectures.

[INMA] 2024-10-17 (10h) : Recent developments in Direct Multisearch

At Euler building (room A.002)

Speaker: Ana Luisa Custódio (Universidade Nova de Lisboa)

Abstract: Direct Multisearch (DMS) is a well-established class of Multiobjective Derivative-free Optimization methods, widely used by the optimization community in practical applications and as benchmark for new solvers. In this talk, the key features of DMS will be described, convergence results and worst-case complexity bounds will be provided, as well as recent developments covering the definition of a search step based in quadratic polynomial interpolation and strategies to address nonlinear constraints.

[INMA] 2024-10-15 (14h) : Newcomers seminars (PhDs)

At Euler building (room a.002)

Section 1: Generalized low-rank plus sparse models in angular and spectral differential imaging for exoplanets detection with regularized implicit neural representations

Speaker: Nicolas Mil-Homens Cavaco (PhD UCLouvain/INMA)

Abstract: Differential imaging is a widespread technique that involves post-processing images captured by ground-based telescopes during an observing campaign in order to make exoplanets in a distant planetary system directly visible. This technique is based on introducing diversity into the observation process, for example by taking advantage of the Earth's rotation in angular differential imaging (ADI) or by recording many wavelengths in spectral differential imaging (SDI), or a combination of both (ASDI). The effect is to increase the signal-to-noise ratio of exoplanet image features compared to unstructured and non-physical data corruption. Direct imaging of exoplanets with ASDI is nevertheless particularly challenging since an exoplanet is faint compared to its host star and the surrounding data corruption noise. In this context, we propose to develop novel signal representations and inverse problem-solving techniques by incorporating regularized implicit neural representations (INRs), defined as continuous parametric models based on neural network architectures, into dedicated low-rank plus sparse models to address the specific geometric transformations experienced by exoplanets in ASDI and to reduce the interpolation error induced by these transformations. More generally, this work aims at offering innovative solutions for employing INRs in continuous or high-dimensional signal representations for various inverse problems, especially when low-rank, sparse or low-rank plus sparse models are typically employed.

Section 2: Bridging Minds and Movements : Nonlinear Control Models for Human Reaching Movements

Speaker: Alexandre Thyrion (PhD UCLouvain/INMA)

Abstract: A large majority of currents research aiming at improving the understanding of the cerebral mechanisms underlying human reaching movements are based on linear approximation of the biomechanics of the body, neglecting completely the impact of the inherent nonlinearities of the system, but allowing the use of linear control models. However, evidence has shown that this simplification, although extremely common, could in many cases be inadequate. This study develops nonlinear control models allowing to study directly the behavior of a more realistic nonlinear model of biomechanics. We also aim to study the underlying hypotheses brought by these new models and their implication on the a priori functioning of the brain. Finally, we will compare the movements produced by the model with experimental observations and give some insights about future research.

Section 3: Computer-assisted analysis of inexact and stochastic first-order optimization methods.

Speaker: Vernimmen, Pierre

Abstract: The increasing complexity of large-scale optimization challenges, particularly in the field of machine learning, requires the development of more efficient algorithms. First-order methods have emerged as a preferred choice due to their simplicity and minimal computational requirements; however, their effectiveness can decrease when information is inexact, or if they are subject to stochastic influences. This study aims to improve the Performance Estimation Methodology (PEP) - a robust framework that automates the evaluation of optimization algorithms - to solve these problems in inexact and stochastic environments. Using PEP, we will examine traditional and new first-order optimization algorithms in scenarios where gradient information is inexact or where randomness affects the decision-making process, a situation frequently encountered in data-driven applications such as machine learning. The main objective is to deepen the theoretical understanding of these algorithms, refine their worst-case performance guarantees and develop improved methods that demonstrate greater reliability and efficiency in real-world applications. .

[INMA] 2024-10-08 (14h) : Expanding Inductive Functional Proofs: Beyond Barrier Certificates

At Euler building (room A.002)

Speaker: Majid Zamani (University of Colorado Boulder, USA)

Abstract: A prominent approach to ensuring the safety of cyber-physical systems is through the use of barrier certificates—real-valued functions that serve as inductive proofs of safety. In this talk, we explore generalizations of barrier certificates, inspired by alternative notions of induction, to verify discrete-time dynamical systems against a wide range of specifications, from safety to more complex logic-based and security specifications. We introduce several new concepts, including k-inductive barrier certificates, closure certificates, and augmented barrier certificates, to verify systems against safety, ω-regular, and hyperproperty specifications. Drawing from k-induction in software verification, we propose k-inductive barrier certificates for system safety verification, allowing simpler functions to serve as proofs of safety compared to traditional barrier certificates. Building on the concept of transition invariants, we introduce closure certificates for verifying systems against ω-regular properties—temporal specifications described by ω-regular automata, which extend beyond safety to encompass a broader class of system behaviors. Finally, we present augmented barrier certificates for verifying systems against hyperproperties, which describe relationships between system traces and often address security-related concerns.

[INMA] 2024-10-01 (14h) : Newcomers seminars (PhDs)

At Euler building (room a.002)

Section 1: Tools for measuring and quantifying neurodegenerative diseases. Quantitative approach applied to essential tremor and Parkinson disease

Speaker: François Lessage (PhD UCLouvain/INMA)

Abstract: To develop quantitative measures and assess pathologies affecting movement control, we will study populations with essential tremor (ET) and Parkinson disease (PD) in tasks at the forefront of current knowledge on these pathologies. For essential tremor, we will study the mechanism of sensory attenuation in relation to recent laboratory results suggesting delay compensation errors. For Parkinson disease, we will study Long-Range Autocorrelation (LRA) and adaptation to better understand how this pathology affects gait control. In both cases, we hope to expand knowledge and identify complementary measures that could be useful to quantify these deficits.

Section 2: A computational framework to study the integration of mechanical and thermal inputs during tactile interactions

Speaker: Louis Lovat (PhD UCLouvain/INMA)

Abstract: A computational framework to study the integration of mechanical and thermal inputs during tactile interactions" Abstract : "The study of somatosensation has made significant strides, with numerous models developed to simulate either mechanical or thermal responses of the skin. These models have provided valuable insights into the response of the skin under various conditions, such as stress, deformation, and temperature changes. However, most existing models tend to focus on either mechanical or thermal stimuli in isolation, bypassing proven interactions between the different somatosensory submodalities and often overlooking the fine-scale interactions that occur at the level of fingerprint ridges and small topographic features of objects. Here, we aim to develop a comprehensive computational framework capable of simulating the complex interactions between mechanical and thermal stimuli at the fingertip. The core of this work being to create a detailed model of the fingertip accurately predicting the mechanical and thermal responses of the skin at both macro and micro scales. This model will be integrated with artificial neurons representing somatosensory afferents which will allow precise simulation of the sensory system’s responses to various combined stimuli. Understanding how the human somatosensory system captures these intricate dynamics will provide a tool to design, predict and interpret future psychophysical and neurophisiological experiment, and contribute to practical applications in the development of haptic interfaces, neuroprosthetics and virtual reality technologies

Section 3: Tight analysis and design of online optimization algorithms

Speaker: Erwan Meunier (PhD UCLouvain/INMA)

Abstract: Online optimization algorithms aim at minimizing on average over time a function that can change every time it is sampled. Crucially, the value of the function often has to be “paid” each time it is sampled, e.g. in terms of energy, money, prediction error, etc. Preliminary results in my master‘s thesis show that (i) the currently available performance bounds are conservative, which can lead to a suboptimal use and tuning of online methods and higher costs, and (ii) tight performance bounds can be understood by analyzing highly structured low-dimensional functions. In my thesis, I will be to analyze and exploit this structure to develop general worst-case performance bounds, and to use these bounds and the insights gained to design novel more efficient online optimization algorithms. Specifically, I will begin by analyzing online settings with unstructured (arbitrary) changes of functions. I will then move to several contexts where changes are structured, with applications in control and stochastic optimization. Finally, I will generalize my results to distributed online optimization, where a group of interconnected computers each own a part of the functions and collaborate towards the global minimization. Based on recent results in standards optimization, potential gains in the decentralized settings are suspected to be tremendous. A key enabler of my project and my preliminary works, is the recently developed performance estimation problem (PEP) methodology, which allows computing the exact worst-case behavior of a wide class of deterministic optimization algorithms, by formulating this analysis itself as a tractable optimization problem. I will exploit it both as a guide in my exploration, and as a method for theoretical developments .

[INMA] 2024-09-24 (14h) : Newcomer seminar (postdocs)

At Euler building (room a.002)

Section 1: Synchronization Analysis, Control and Verification of Complex Networked Systems

Speaker: Shuyuan Zhang

Abstract: Synchronization is a kind of common collective phenomenon in nature, which has a quite wide range of applications for various subjects, including physics, biology, control science, social science. A typical way of analyzing synchronization of complex networked systems is to establish the synchronization criteria based on quadratic Lyapunov functions. Beyond the challenge of obtaining quadratic Lyapunov functions, the serious challenge is no guarantee of Lyapunov functions of the quadratic form for some systems. However, for these systems, there may exist general Lyapunov functions. Inspired by this fact, we propose several less conservative synchronization criteria by using general Lyapunov functions beyond quadratic ones. Then, the synchronization problem is transformed into a sum-of-squares optimization problem. The resulting sum-of-squares-based optimization algorithm efficiently generates polynomial Lyapunov functions, facilitating automatic synchronization verification. The obtained results are less conservative and are applicable for more systems, not only homogeneous networked systems but also heterogeneous networked systems.

Section 2: A coarse view on dynamical systems, control and optimization

Speaker: Wouter Jongeneel

Abstract: It is not clear if our field would be in this state without early efforts towards understanding stability of the solar system. At that time---as explicit integration turned out to be overly complicated, the key insight was not to approximate, but to move away from the quantitative. The result was the inception of a more qualitative study of dynamical systems, with the overall philosophy being nicely captured by Conley: "... if such rough equations are to be of use it is necessary to study them in rough terms ...". Now, flash-forward to 2024, with the advent of computational power and data storage, the focus shifted again to the quantitative. It is hard to find papers without sample complexities, probabilistic bounds and a statistical analysis of extensive simulations. This viewpoint is evidently very important towards safe and practical algorithms, for instance, regarding a control system. So is there still room for something qualitative? In this talk we discuss open homotopy questions (and partial resolutions) in dynamical systems, topological insights in control systems and we comment on some optimization problems. With these examples we hope to show that there is indeed room---and arguably a need, for more qualitative work in a quantitative age.

[INMA] 2024-09-17 (14h) : Emergence and control of synchronization patterns in systems with higher-order interactions

At Maxwell building (Shannon room)

Speaker: Riccardo Muolo (Tokyo Institute of Technology)

Abstract: Synchronization is a ubiquitous emergent phenomenon in which an ensemble of elementary units behaves in unison due to their interactions [1]. Given the pervasiveness of synchronization, understanding how it is achieved is a fundamental question. In particular, the nature of the interactions among oscillators has strong consequences on the transition to synchronization. To tackle this issue, it is convenient to consider phase models in which each oscillator is described solely in terms of a phase variable. According to phase reduction theory, the phase model captures the dynamics completely when the coupling among the oscillators is sufficiently weak [2]. If one considers only pairwise interactions, the synchronization transition is described by a Kuramoto-type model. Despite the versatility of such an approach, the classical theory of synchronization is solely based on pairwise interactions, while, in many systems, the interactions are intrinsically higher-order (many-body) rather than pairwise [3]. In fact, many examples show that a pairwise description is not sufficient to match the theory with observations and, additionally, higher-order interactions appear naturally when phase reduction is performed up to higher orders [4]. It was also shown that extensions of the Kuramoto model including higher-order interactions exhibit an explosive transition to synchrony and other rich behaviors [5]. I will start by introducing the phase reduction theory and highlight the universality of phase models. Then, after discussing the basics of higher-order interactions, I will present a recent work where we analyzed the collective dynamics of the simplest minimal extension of the Kuramoto-type phase model for identical globally coupled oscillators subject to two- and three-body interactions and showed how the many-body interactions greatly enrich the synchronization patterns of the system [6]. In the last part of the seminar, I will briefly introduce an intriguing synchronization pattern in which coherent and incoherent oscillators coexist, called chimera states [7]. Such patterns are known to be elusive and characterized by a very short life-time when the interactions are pairwise, but are enhanced by the presence higher-order interactions [9]. This fact can be exploited by using a pinning control approach: in fact, controlling the emergence of chimera states in systems with higher-order interactions is much easier and efficient if compared with the classic network framework [10]. This is joint work with Hiroya Nakao (Tokyo Institute of Technology, Japan), Shigefumi Hata (Kagoshima University, Japan), Iván León (University of Cantabria, Spain), Lucia Valentina Gambuzza and Mattia Frasca (University of Catania, Italy) References: [1] Kuramoto Y., Chemical Oscillations, Waves, and Turbulence. Springer-Verlag, 1984. [2] Nakao H., Phase reduction approach to synchronisation of nonlinear oscillators. Cont. Phys., 57(2): 188-214, 2016. [3] Battiston F. et al., Networks beyond pairwise interactions: Structure and dynamics. Phys. Rep., 84: 1-92, 2020. [4] León I. and Pazó D., Phase reduction beyond the first order: The case of the mean-field complex Ginzburg-Landau equation. Phys. Rev. E, 100(1): 012211, 2019. [5] Skardal P.S. and Arenas A., Abrupt Desynchronization and Extensive Multistability in Globally Coupled Oscillator Simplexes. Phys. Rev. Lett., 122(84): 248301, 2019. [6] León I., Muolo R., Hata S. and Nakao H., Higher-order interactions induce anomalous transitions to synchrony. Chaos 34, 013105, 2024. [7] Zakharova A., Chimera Patterns in Networks. Interplay between Dynamics, Structure, Noise, and Delay. Springer, 2020. [8] Kuramoto Y. and Battogtokh D, Coexistence of coherence and incoherence in nonlocally coupled phase oscillators. Nonlinear Phenom. Complex Syst. 5, 2002. [9] Muolo R., Njougouo T., Gambuzza L.V., Carletti T. and Frasca M., Phase chimera states on nonlocal hyperrings. Phys. Rev. E 109, L022201, 2024. [10] Muolo R., Gambuzza L.V., Nakao H. and Frasca M., Pinning control of chimera states on nonlocal hyperrings. In preparation. .

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Seminars

inma |

Seminars to Come

[INMA] 2026-03-10 (14:00) : From Learning to Optimize to Learning Optimization Algorithms

At Euler building (room A.002)

[INMA] 2026-03-24 (14:00) : Alternating low-rank optimization for solving PDEs depending on geometric parameters

At Euler building (room A.002)

Previous Seminars

[INMA] 2026-03-03 (14:00) : Consensus is a myth: Human label variation in Natural Language Inference

At Euler building (room A.002)

[INMA] 2026-02-24 (14:00) : A passivity-based perspective on distributed optimization and its acceleration

At Euler building (room A.002)

[INMA] 2026-02-17 (14:00) : Newcomers Seminar

At Euler building (room A.002)

Section 1: Fleet Rebalancing: Scalable Feedback Optimization for Autonomous Mobility-on-Demand via null-space projection

Section 2: Mechanical determinants of tactile perception in the human fingertip

Section 3: Reinforcement Learning for Petri-Net based Discrete Event Systems: Application to Aircraft Maintenance Scheduling

[INMA] 2026-02-10 (14:00) : The Ellipsoidal Separation Machine

At Euler building (room A.002)

[INMA] 2026-02-03 (14:00) : IRKA Is a Riemannian Gradient Descent Method

At Euler building (room A.002)

[INMA] 2026-01-20 (14:00) : Opinion Dynamics with Nonlinear Interaction: From Robust Clustering to Environmental Coupling

At Euler building (room A.002)

[INMA] 2025-12-16 (14:00) : Geometry of low-rank tensors

At EULER (room A.002)

[INMA] 2025-12-09 (14:00) : Matroids are equitable

At EULER (room A.002)

[INMA] 2025-12-02 (14:00) : Expanding BGP Data Horizons

At EULER (room A.002)

[INMA] 2025-11-25 (14:00) : A constrained Lie group approach to the modeling of dynamic mechanical systems

At EULER (room A.002)

[INMA] 2025-11-18 (14:20) : Data-Driven Methods for Formal Verification and Synthesis of Dynamical Systems

At Euler building (room A.002)

[INMA] 2025-11-17 (14:00) : Regularized block coordinate descent methods: Complexity and applications

At a.002

[INMA] 2025-11-04 (14:00) : Automated algorithm analysis for time-varying optimization: tracking and regret bounds

At Euler building (room A.002)

[INMA] 2025-10-29 (14:00) : Internal Optimization Seminar

At Euler building (room A.002)

[INMA] 2025-10-21 (14:00) : Over-Approximation Methods for Safe Control: Lifting and Preview Information

At Euler building (room A.002)

[INMA] 2025-10-16 (13:00) : Malware Detection with Machine Learning: Challenges and Perspectives

At Shannon room, Maxwell building

[INMA] 2025-10-15 (14:00) : Internal Optimization Seminar

At Euler building (room A.002)

[INMA] 2025-10-14 (14:00) : Newcomers seminars (PhDs)

At Euler building (room a.002)

Section 1: Low-rank PSD matrix completion

Section 2: Novel deep learning architectures for the detection of the stochastic gravitational wave background.

Section 3: Path-complete reinforcement learning

[INMA] 2025-10-08 (14:00) : Internal Optimization Seminar

At Euler building (room A.002)

[INMA] 2025-10-07 (14:00) : Communication-efficient distributed optimization algorithms

At Euler building (room A.002)

[INMA] 2025-09-30 (14:00) : Toward Resilient Operation of Large-Scale Cyber-Physical Human Systems

At Euler building (room A.002)

[INMA] 2025-09-23 (14:00) : Newcomers seminars (PhDs)

At Euler building (room a.002)

Section 1: Random Embeddings for Deep Learning: Improving Scalability and Generalization

Section 2: Analysis of Hidden Convexity in Neural Networks and Transformers: Toward More Efficient and Robust Deep Learning.

Section 3: Scalable Control Design for Networked Systems: Coordination through Local Cooperation.

[INMA] 2025-09-16 (14:00) : Safety in the Face of Uncertainty: When is a Scenario Decision-Making Algorithm Safe?

At Euler building (room A.002)

[INMA] 2025-08-20 (14h) : Efficient Distance-Adaptive Subgradient Methods

At Euler building (room A.002)

[INMA] 2025-06-17 (14h) : Matrix Factorization and Approximation of Nonnegative Rank Two

At Euler building (room A.002)

[INMA] 2025-05-20 (14h) : Model-Based and Data-Driven Interventions in Network Games

At Euler building (room A.002)

[INMA] 2025-05-06 (14h) : Axisymmetric magnetic control in ST40

At Euler building (room A.002)

[INMA] 2025-04-16 (15h) : Gradient-based optimization in CFD: from nuclear fusion to small modular fission reactors

At Euler building (room A.002)

[INMA] 2025-04-15 (14h) : Open problems about the simplex method

At Euler building (room A.002)

[INMA] 2025-04-09 (15h) : Stochastic second-order optimization: global bounds, subspaces, and momentum

At Euler building (room A.002)

[INMA] 2025-04-08 (14h) : Multi-product Supply Function Equilibria

At Euler building (room A.002)