Portrait de Margarida Carvalho

Margarida Carvalho

Membre académique associé
Professeure adjointe, Université de Montréal, Département d'informatique et de recherche opérationnelle
Sujets de recherche
Équité algorithmique
IA et durabilité
Optimisation
Optimisation combinatoire
Théorie de la décision
Théorie des jeux
Site web
Google Scholar

Biographie

Margarida Carvalho est titulaire d'un baccalauréat et d'une maîtrise en mathématiques. Elle a obtenu un doctorat en informatique à l'Université de Porto, pour lequel elle a reçu le prix de la thèse EURO en 2018. La même année, elle est devenue professeure adjointe au Département d'informatique et de recherche opérationnelle de l'Université de Montréal, où elle occupe la Chaire de recherche FRQ-IVADO en science des données pour la théorie des jeux combinatoires.

Elle est une experte en recherche opérationnelle, notamment en optimisation combinatoire et en théorie algorithmique des jeux. Ses recherches sont motivées par des problèmes de prise de décision du monde réel impliquant l'interaction de plusieurs agents, tels que les programmes d'échange de reins, le choix des écoles et les marchés concurrentiels.

Étudiants actuels

Marylou Fauchard
Maîtrise recherche - UdeM
Co-superviseur⋅e :
Golnoosh Farnadi
marylou.fauchard@mila.quebec
William St-Arnaud
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Golnoosh Farnadi
Nagisa Sugishita
Postdoctorat - UdeM

Publications

Integer Programming Games.
Margarida Carvalho
Gabriele Dragotto
Andrea Lodi 0001
Sriram Sankaranarayanan 0002
2025-01-01
Found. Trends Optim. (publié)
doi.org
A stochastic integer programming approach to reserve staff scheduling with preferences
Carl Perreault‐Lafleur
Margarida Carvalho
Guy Desaulniers
2025-01-01
Int. Trans. Oper. Res. (publié)
doi.org
arxiv.org
The Strength of Fuel Refueling Location Problem Formulations
Nagisa Sugishita
Margarida Carvalho
Ribal Atallah
2024-09-06
ArXiv (prépublication)
arxiv.org
arxiv.org
Accelerated Benders Decomposition and Local Branching for Dynamic Maximum Covering Location Problems
Steven Lamontagne
Margarida Carvalho
Ribal Atallah
The maximum covering location problem (MCLP) is a key problem in facility location, with many applications and variants. One such variant is… (voir plus) the dynamic (or multi-period) MCLP, which considers the installation of facilities across multiple time periods. To the best of our knowledge, no exact solution method has been proposed to tackle large-scale instances of this problem. To that end, in this work, we expand upon the current state-of-the-art branch-and-Benders-cut solution method in the static case, by exploring several acceleration techniques. Additionally, we propose a specialised local branching scheme, that uses a novel distance metric in its definition of subproblems and features a new method for efficient and exact solving of the subproblems. These methods are then compared through extensive computational experiments, highlighting the strengths of the proposed methodologies.
2024-07-01
Computers & Operations Research (publié)
doi.org
arxiv.org
Learning to Build Solutions in Stochastic Matching Problems Using Flows (Student Abstract)
William St-Arnaud
Margarida Carvalho
Golnoosh Farnadi
2024-03-24
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
Solving Combinatorial Pricing Problems using Embedded Dynamic Programming Models
Quang Minh Bui
Margarida Carvalho
Jos'e Neto
The combinatorial pricing problem (CPP) is a bilevel problem in which the leader maximizes their revenue by imposing tolls on certain items … (voir plus)that they can control. Based on the tolls set by the leader, the follower selects a subset of items corresponding to an optimal solution of a combinatorial optimization problem. To accomplish the leader's goal, the tolls need to be sufficiently low to discourage the follower from choosing the items offered by the competitors. In this paper, we derive a single-level reformulation for the CPP by rewriting the follower's problem as a longest path problem using a dynamic programming model, and then taking its dual and applying strong duality. We proceed to solve the reformulation in a dynamic fashion with a cutting plane method. We apply this methodology to 2 distinct dynamic programming models, namely, a novel formulation designated as selection diagram and the well-known decision diagram. We also produce numerical results to evaluate their performances across 3 different specializations of the CPP and a closely related problem that is the knapsack interdiction problem. Our results showcase the potential of the 2 proposed reformulations over the natural value function approach, expanding the set of tools to solve combinatorial bilevel programs.
2024-03-19
ArXiv (prépublication)
doi.org
arxiv.org
The effects of nature-based vs. indoor settings on the adaptability, performance and affect of calisthenics exercisers. A registered report.
Henrique Brito
Henrique Lopes
Margarida Carvalho
Daniel Carrilho
Adriano Carvalho
Duarte Araújo
2024-03-01
Psychology of Sport And Exercise (publié)
doi.org
The effects of nature-based vs. indoor settings on the adaptability, performance and affect of calisthenics exercisers. A registered report.
Henrique Brito
Henrique Lopes
Margarida Carvalho
Daniel Carrilho
Adriano Carvalho
Duarte Araújo
2024-03-01
Psychology of Sport And Exercise (publié)
doi.org
Solving Two-Stage Stochastic Programs with Endogenous Uncertainty via Random Variable Transformation
Maria Bazotte
Margarida Carvalho
Thibaut Vidal
2024-02-23
ArXiv (prépublication)
arxiv.org
arxiv.org
The Sample Average Approximation Method for Solving Two-Stage Stochastic Programs with Endogenous Uncertainty
Maria Bazotte
Margarida Carvalho
Thibaut Vidal
Real-world decision-making problems involve Type 1 decision-dependent uncertainty, where the probability distribution of the stochastic proc… (voir plus)ess depends on the model decisions. However, few studies focus on two-stage stochastic programs with this type of endogenous uncertainty, and those that do lack general methodologies. We thus propose herein a general method for solving a class of these programs based on the transformation of random variables, a technique widely employed in probability and statistics. The proposed method is tailored to large-scale problems with discrete or continuous endogenous random variables. The random variable transformation allows the use of the sample average approximation (SAA) method, which provides optimality convergence guarantees under certain conditions. We show that, for some classical distributions, the proposed method reduces to solving mixed-integer linear or convex programs. Finally, we validate this method by applying it to a network design and facility-protection problem, considering distinct decision-dependent distributions for the random variables. Whereas most distributions result in a nonlinear nonconvex deterministic equivalent program, the proposed method solves mixed-integer linear programs in all cases. In addition, it produces attractive performance estimators for the SAA method in a reasonable computational time and outperforms the case in which the endogenous distribution defines a mixed-integer deterministic equivalent.
2024-02-23
ArXiv (prépublication)
arxiv.org
arxiv.org
Computing Approximate Nash Equilibria for Integer Programming Games
Aloïs Duguet
Margarida Carvalho
Gabriele Dragotto
Sandra-ulrich Ngueveu
2024-02-06
ArXiv (prépublication)
arxiv.org
arxiv.org
Asymmetry in the complexity of the multi-commodity network pricing problem
Quang Minh Bui
Margarida Carvalho
Jos'e Neto
2024-01-01
Math. Program. (publié)
doi.org
arxiv.org