Mars 2026

Heure:	10:30 - 12:00
Lieu:	Salle G205, Université de Villetaneuse
Résumé:	Positive spanning sets and their connections to polyhedra
Description:	Clément Royer Positive spanning sets (PSSs), that span a given space through nonnegative linear combinations, have been successfully employed to design and analyze derivative-free optimization algorithms. Although linear algebra is a natural framework for studying PSSs, polyhedral geometry can provide additional insights on the structure of PSSs. In this talk, I will first introduce the concept of positive spanning sets, together with its use in derivative-free optimization. I will then focus on the specific case of polyhedral constrained problems, and explain how to generate positive spanning sets that conform to the geometry of those constraints. Finally, I will turn to a perhaps unexpected construction of PSSs of smallest cardinality through polytopes, and discuss several associated open questions. This talk is based on joint works with Denis Cornaz, Sébastien Kerleau and Lindon Roberts.

Heure:	14:00 - 16:00
Lieu:	Salle G202, Université de Villetaneuse
Résumé:	Warm-Starting QAOA for Combinatorial Optimization via Difference-of-Convex Optimization - A Case Study on Max-Cut
Description:	Viet Hung Nguyen The Quantum Approximate Optimization Algorithm (QAOA) has recently been proposed as a heuristic framework for solving combinatorial optimization problems through a hybrid classical–quantum optimization procedure. The algorithm alternates parameterized quantum transformations with a classical optimization step that adjusts the circuit parameters in order to increase the probability of sampling high-quality solutions. A key factor influencing the performance of QAOA is the choice of the initial state. In standard implementations, the algorithm starts from a uniform superposition over all candidate solutions, which does not exploit structural information about the original optimization problem and may lead to inefficient parameter optimization and lower-quality solutions. In this talk, we propose a warm-start strategy based on continuous optimization, using the Difference-of-Convex Algorithm (DCA). The idea is to exploit a continuous relaxation of the original optimization problem in order to construct an informed initialization that biases the search toward promising regions of the solution space. We illustrate the approach on instances of the Max-Cut problem and show that this strategy can significantly improve the approximation ratios obtained by QAOA. This is a joint work with HA Huy Phuc Nguyen et TA Anh Son.