From the 1 of 7 linked papers with an AI index.
7 papers
First-Order Methods for Distributionally Robust Constrained Optimization
Hubert Villuendas, Mathieu Besançon, Jérôme Malick
The paper introduces a stochastic algorithm that combines entropic regularization with a stochastic Frank‑Wolfe method to solve Wasserstein distributionally robust optimization pro…
Optimisation models for the design of multiple self-consumption loops in semi-rural areas
Yohann Chasseray, Mathieu Besançon, Xavier Lorca +1
Collective electricity self-consumption gains increasing interest in a context where localised consumption of energy is a lever of sustainable development. While easing energy dist…
Efficient Quadratic Corrections for Frank-Wolfe Algorithms
Jannis Halbey, Seta Rakotomandimby, Mathieu Besançon +2
We develop a Frank-Wolfe algorithm with corrective steps, generalizing previous algorithms including blended conditional gradients, blended pairwise conditional gradients, and full…
A Frank-Wolfe-based primal heuristic for quadratic mixed-integer optimization
Gioni Mexi, Deborah Hendrych, Sébastien Designolle +2
We propose a primal heuristic for quadratic mixed-integer problems. Our method extends the Boscia framework -- originally a mixed-integer convex solver leveraging a Frank-Wolfe-bas…
Knapsack with compactness: a semidefinite approach
Hubert Villuendas, Mathieu Besançon, Jérôme Malick
The min-knapsack problem with compactness constraints extends the classical knapsack problem, in the case of ordered items, by introducing a restriction ensuring that they cannot b…
Network Design for the Traffic Assignment Problem with Mixed-Integer Frank-Wolfe
Kartikey Sharma, Deborah Hendrych, Mathieu Besançon +1
We tackle the network design problem for centralized traffic assignment, which can be cast as a mixed-integer convex optimization (MICO) problem. For this task, we propose differen…