We consider a structural model to design and evaluate the American call, conversion, and put options embedded in corporate bonds. We use dynamic programmin...

A growing body of recent literature analyzes the reaction of Robinhood (RH) investors to price movements at the daily frequency. As these investors tend to b...

The Network Design Problem with Vulnerability Constraints and Probabilistic Edge Reliability (NDPVC-PER) is an extension of the NDPVC obtained by additionall...

How can retailers incentivize customers to make healthier food choices? Price, convenience, and taste are known to be among the main drivers behind such choi...

Le résumé automatique de document a pour but de créer une version réduite d’un ensemble de textes pour aider des utilisateurs à mieux assimiler l’information...

"L'eau c'est la vie" is a well known french expression for "Water is life", which reflects the fact that water is undoubtedly the most vital resource in the ...

In this paper, we describe a branch-and-price algorithm for the personalized nurse scheduling problem. The variants that appear in the literature involve a ...

Station-based Bike-sharing systems have been implemented in multiple major cities, offering a low-cost and environmentally friendly transportation alternativ...

This paper addresses risk averse constrained optimization problems where the objective and constraint functions can only be computed by a blackbox subject to...

High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks. HTS are mai...

Two-stage stochastic programs are a class of stochastic problems where uncertainty is discretized into scenarios, making them amenable to solution approaches...

Cet article étudie le risque de crédit de contrepartie et l’ajustement réglementaire correspondant (CVA) pour des portefeuilles de produits dérivés avec poss...

This paper investigates a variant of the traveling salesman problem (TSP) with speed optimization for a plug-in hybrid electric vehicle (PHEV), simultaneousl...

Improving neural network optimizer convergence speed is a long-standing priority. Recently, there has been a focus on quasi-Newton optimization methods, whi...

Benders decomposition has been applied significantly to tackle large-scale optimization problems with complicating variables, which, when temporarily fixed, ...

Historically, the training of deep artificial neural networks has relied on parallel computing to achieve practical effectiveness. However, with the increas...

We introduce an iterative solver named MINARES for symmetric linear systems \(Ax \approx b\), where \(A\) is possibly singular. MINARES is based on t...

Operations research specialists at the OCP Group, the Mohammed VI Polytechnic University, and the Polytechnique Montreal operationalized a system optimizing ...

Decision trees are highly interpretable models for solving classification problems in machine learning (ML). The standard ML algorithms for training decision...

We introduce a variant of the proximal gradient method in which the quadratic term is diagonal but may be indefinite, and is safeguarded by a trust region. ...