activity
20222026
most citedTight convergence rates of the gradient method on smooth hypoconvex functions

2 citations · 2 across the 12 of their papers we have counts for

collaborators

13 papers

cs.CV2026

From Elevation Maps To Contour Lines: SVM and Decision Trees to Detect Violin Width Reduction

Philémon Beghin, Anne-Emmanuelle Ceulemans, François Glineur

We explore the automatic detection of violin width reduction using 3D photogrammetric meshes. We compare SVM and Decision Trees applied to a geometry-based raw representation built…

math.OC2025

Empirical and computer-aided robustness analysis of long-step and accelerated methods in smooth convex optimization

Pierre Vernimmen, François Glineur

This work assesses both empirically and theoretically, using the performance estimation methodology, how robust different first-order optimization methods are when subject to relat…

math.OC2025

Performance Estimation of second-order optimization methods on classes of univariate functions

Anne Rubbens, Nizar Bousselmi, Julien M. Hendrickx +1

We develop a principled approach to obtain exact computer-aided worst-case guarantees on the performance of second-order optimization methods on classes of univariate functions. We…

math.OC2025

Worst-case convergence analysis of relatively inexact gradient descent on smooth convex functions

Pierre Vernimmen, François Glineur

We consider the classical gradient descent algorithm with constant stepsizes, where some error is introduced in the computation of each gradient. More specifically, we assume some…

math.OC2025

Presolve techniques for quasi-convex chance constraints with finite-support low-dimensional uncertainty

Guillaume Van Dessel, François Glineur

Chance-constrained programs (CCP) represent a trade-off between conservatism and robustness in optimization. In many CCPs, one optimizes an objective under a probabilistic constrai…

math.OC2025

Dual first-order methods for efficient computation of convex hull prices

Sofiane Tanji, Yassine Kamri, François Glineur +1

Convex Hull (CH) pricing, used in US electricity markets and raising interest in Europe, is a pricing rule designed to handle markets with non-convexities such as startup costs and…