Array-RQMC has been proposed as a way to effectively apply randomized quasi-Monte Carlo (RQMC) when simulating a Markov chain over a large number of steps to...

In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa (1987) to evaluate the likelihood fun...

This paper introduces the algorithmic design and implementation of Tulip, an open-source interior-point solver for linear optimization. It implements the ho...

Significant progress has been made in the field of computer vision, due to the development of supervised machine learning algorithms, which efficiently extra...

Les organismes caritatifs jouent un rôle important dans la production de biens et services. L'objectif de cet article est de déterminer la politique optimale...

L’accroissement de la proportion d’énergies renouvelables fluctuantes représente un défi important pour les exploitants de réseaux électriques. Il existe p...

We analyze an endogenous growth model with non-constant discounting and a negative externality of growth on utility. With a decreasing rate of impatience, ...

We consider three colouring problems which are variations of the basic vertex-colouring problem, and are motivated by applications from various domains. We g...

Dans cet article, nous étudions la concurrence entre marques nationales et marques privées (ou marques de distributeurs) en analysant l’effet de la présence ...

Reducing the number of features used in data classification can remove noisy or redundant features, reduce the cost of data collection, and improve the accur...

This paper studies the Dynamic Facility Location Problem with Modular Capacities (DFLPM). We propose a linear relaxation based heuristic (LRH) and an evoluti...

\(K\)-medoids clustering is among the most popular methods for cluster analysis, but it carries several assumptions about the nature of the latent clusters...

Suite aux travaux de Estrin et al. (2019), nous développons une méthode pour l’optimisation avec contraintes générales basée sur une fonction de pénalité d...

In large commercial airlines, the monthly schedule (roster) of the crew members is usually determined by solving two problems sequentially, namely, the crew ...

We consider a class of min-max robust problems in which the functions that need to be robustified can be decomposed as the sum of arbitrary functions. This...

We present a case study of using machine learning classification algorithms to initialize a large scale commercial operations research solver (GENCOL) in the...

Drawing on statistical learning theory, we derive out-of-sample and optimality guarantees about the investment strategy obtained from a regularized portfoli...

This paper considers a dynamic Emergency Medical Services (EMS) network design problem and introduces two novel two-stage stochastic programming formulatio...

Randomized decision making refers to the process of taking decisions randomly according to the outcome of an independent randomization device such as a dic...

We consider several time series and for each of them, we fit an appropriate dynamic parametric model. This produces serially independent error terms for each...