249 citations · 790 across the 59 of their papers we have counts for
10 papers · 1 filter
Computing Nash equilibria for integer programming games
Margarida Carvalho, Andrea Lodi, João Pedro Pedroso
The recently defined class of integer programming games (IPG) models situations where multiple self-interested decision makers interact, with their strategy sets represented by a f…
Stochastic Damped L-BFGS with Controlled Norm of the Hessian Approximation
Sanae Lotfi, Tiphaine Bonniot de Ruisselet, Dominique Orban +1
We propose a new stochastic variance-reduced damped L-BFGS algorithm, where we leverage estimates of bounds on the largest and smallest eigenvalues of the Hessian approximation to…
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers
Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo +3
We present Ecole, a new library to simplify machine learning research for combinatorial optimization. Ecole exposes several key decision tasks arising in general-purpose combinator…
Change Point Detection by Cross-Entropy Maximization
Aurélien Serre, Didier Chételat, Andrea Lodi
Many offline unsupervised change point detection algorithms rely on minimizing a penalized sum of segment-wise costs. We extend this framework by proposing to minimize a sum of dis…
Reinforcement Learning Based Penetration Testing of a Microgrid Control Algorithm
Christopher Neal, Hanane Dagdougui, Andrea Lodi +1
Microgrids (MGs) are small-scale power systems which interconnect distributed energy resources and loads within clearly defined regions. However, the digital infrastructure used in…
Design and implementation of a modular interior-point solver for linear optimization
Miguel F. Anjos, Andrea Lodi, Mathieu Tanneau
This paper introduces the algorithmic design and implementation of Tulip, an open-source interior-point solver for linear optimization. It implements a regularized homogeneous inte…
Hybrid Models for Learning to Branch
Prateek Gupta, Maxime Gasse, Elias B. Khalil +3
A recent Graph Neural Network (GNN) approach for learning to branch has been shown to successfully reduce the running time of branch-and-bound algorithms for Mixed Integer Linear P…
Multi-agent Assortment Optimization in Sequential Matching Markets
Alfredo Torrico, Margarida Carvalho, Andrea Lodi
Motivated by the challenges faced by choice-based matching platforms, in this work, we study a generalization of the foundational model introduced by Ashlagi et al. (2022). Specifi…
The Covering-Assignment Problem for Swarm-powered Ad-hoc Clouds: A Distributed 3D Mapping Use-case
Leandro R. Costa, Daniel Aloise, Luca G. Gianoli +1
The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perfect solution to improve…
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies
Giulia Zarpellon, Jason Jo, Andrea Lodi +1
Branch and Bound (B&B) is the exact tree search method typically used to solve Mixed-Integer Linear Programming problems (MILPs). Learning branching policies for MILP has become an…