All Years Seminars
[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.
[INGI] 2025-11-17 (13:00) : From Cloud to Edge: How to Make AI Inference Accurate, Fast and Resource-Aware ?
At Nyquist Maxwell a.164 room.
Speaker :
Patient NTUMBA WA NTUMBA (CNAM Paris)
Abstract : Edge computing is becoming a critical enabler for real-time Internet of Things (IoT) applications powered by artificial intelligence (AI). These applications often require accurate, low-latency and high-throughput inference while operating on resource-constrained edge servers. In this seminar, we present a holistic framework for optimising AI model provisioning at the edge. Our approach combines HProfiler, a data-driven model profiling tool that generates AI model variants tailored for accuracy-throughput trade-offs and specific edge resources, with AIForwarder, a reinforcement learning-based mechanism that dynamically manages model activation and request-to-model forwarding. By balancing accuracy, energy consumption, and request loss, our solution adapts to dynamic workloads and heterogeneous IoT demands.Short Bio: Patient Ntumba is a postdoctoral researcher in the RoC team at the Conservatoire National des Arts et Métiers (CNAM) in Paris, France. He obtained his PhD from Sorbonne University, conducting his doctoral research at the INRIA Paris research center in the MiMove team. His research interests focus on networks, distributed systems, and optimization. His current work centers on in-network distributed learning and Edge AI inference.
[INGI] 2025-11-10 (11:00) : Double seminars by Hugo Rimlinger (LIP6) & Moritz Müller (université de Twente)
At Shannon room, Maxwell building
Section 1:GeoResolver: An Accurate, Scalable, and Explainable Geolocation Technique Using DNS Redirection
Speaker :
Hugo Rimlinger (LIP6)
Abstract : Obtaining an accurate, explainable and Internet scale IP geolocation dataset has been a longstanding goal of the research community. Despite decades of research on IP geolocation, no current technique can provide such a dataset. In particular, latency-based geolocation techniques do not scale, because, on one hand, we have thousands of available vantage points to perform measurements, but on the other hand, we have no way to select the right ones for each IP address. In this paper, we present GeoResolver, which is a serious step towards our goal, by using the idea that when multiple operators redirect two prefixes to the same servers, these prefixes should be close to each other. With this intuition, we define a methodology to measure and compare the redirection of prefixes to servers using ECS DNS measurements, and select the prefixes with the smallest redirection distance to a target prefix to issue the latency measurements to targets in that prefix. GeoResolver performs nearly as well as a brute force approach, geolocating 94% of the targets that could actually be geolocated at metro level, while using 4.3% of the probing budget compared to the state of the art. On the Internet scale CAIDA ITDK dataset, GeoResolver geolocates 16% of the IP addresses at metro level, 3.4 times more than the state of the art. In addition, GeoResolver is robust to public resolvers or hypergiants stopping supporting ECS.
Section 2:Monitoring highly distributed DNS deployments: challenges and recommendations for the root server system
Speaker :
Moritz Müller (Université de Twente)
Abstract : DNS name servers are crucial for the reachability of domain names. For this reason, name server operators rely on multiple name servers and often replicate and distribute each server across different locations across the world. Operators monitor the name servers to verify that they meet the expected performance requirements. Monitoring can be done from within the system, e.g. with metrics like CPU utilisation, and from the outside, mimicking the experience of the clients. In this talk, we focus on the latter. We take the root server system as a use case and highlight the challenges operators and researchers face when monitoring highly distributed DNS deployments from the outside. We also present recommendations on building a monitoring system that is more reliable and that captures only the relevant metrics.
[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.
[INGI] 2025-10-30 (13:00) : Hybrid artificial intelligence for solving computationally hard problems
At Shannon, Maxwell a.105
Speaker :
Quentin Cappart (ICTEAM)
Abstract : Combinatorial optimization provides methods to make the best possible decisions in complex scenarios, with practical applications in areas such as transportation, logistics, and healthcare. Traditionally, solving methods for combinatorial problems (such as integer programming, constraint programming, or local search) have focused on solving isolated problem instances, often overlooking the fact that these instances frequently originate from related data distributions. In recent years, there has been a growing interest in leveraging machine learning, particularly neural networks, to enhance combinatorial solvers by utilizing historical data. Despite this interest, it remains unclear how to effectively integrate learning into such engines to boost overall performance. In this presentation, I will share my journey in tackling this challenge, from my initial attempts to my current research directions. I will offer personal advice for researchers interested in exploring this fascinating field, highlighting the potential and opportunities for designing efficient hybrid artificial intelligence for solving computationally hard problems.
Seminars
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