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

In this paper, we consider both first- and second-order techniques to address continuous optimization problems arising in machine learning. In the first-orde...

A small polygon is a polygon of unit diameter. The maximal area of a small polygon with \(n=2m\) vertices is not known when \(m\ge 7\). Finding the large...

A small polygon is a polygon of unit diameter. The maximal perimeter and the maximal width of a convex small polygon with \(n=2^s\) vertices are not known ...

We introduce iterative methods named TriCG and TriMR for solving symmetric quasi-definite systems based on the orthogonal tridiagonalization process proposed...

We consider computationally expensive blackbox optimization problems and present a method that employs surrogate models and concurrent computing at the searc...

Given a set of predefined duties and groups of drivers, the duty assignment problem with group-based driver preferences (DAPGDP) aims at building rosters tha...

We consider a two-stage game in a differentiated duopoly, where firms can pursue both a financial and an environmental objective. We assume that the maximu...

We represent a closed-loop supply chain formed of one manufacturer and one retailer as a dynamic game played over an event tree, which naturally allows to ...

We present ExaPF.jl, a solver for power flow on GPUs, entirely written in Julia. It implements a highly parallel Newton-Raphson solver for nonlinear equati...

In this paper, we study the time consistency of cooperative agreements in dynamic games with nontransferable utility. An agreement designed at the outset of ...

We consider the problem of a government that wishes to promote replacing old cars with new ones via a vehicle scrappage program. Since these programs incre...

We study a class of deterministic two-player nonzero-sum differential games where one player uses piecewise-continuous controls to affect the continuously e...

GenoGraphiX-Log 2.0 (abbreviation GGXLog) is a keystroke logging software that was developed as a collaboration between <a href="" title="https://www.h...

We consider logistic collaborations where multiple carriers collaborate by consolidating demands, combining delivery routes, and serving new customers. Logis...

The crew pairing problem (CPP) is solved in the first step of the crew scheduling process. It consists of creating a set of pairings (sequence of flights, co...

The personalized crew rostering problem (CRP) consists of assigning pairings (sequences of flights, deadheads, connections, and rests, forming one or several...

Energy generation has always been a major stake in our economy and is all the more so with the increase in energy demand all over the world. In that context,...

This paper presents an algorithm for the identification of parameters for a stochastic hot water end-use process that drives a homogeneous population of ther...

We present LatNet Builder, a software tool to find good parameters for lattice rules, polynomial lattice rules, and digital nets in base 2, for quasi-Monte...

We study a solution approach for a staffing problem in multi-skill call centers. The objective is to find a minimal-cost staffing solution while meeting a ta...

Based on data from real call centers, we develop, test, and compare forecasting methods to predict the waiting time of a call upon its arrival to the center,...

We empirically test the prediction of Pastor, stambaugh, and Taylor (2020) that green firms outperform brown firms when concerns about climate change increas...

Demand response (DR) has been increasingly growing in significance among the solutions to tackle climate change, along with the development of intermittent r...

In this paper, we propose an exact branch-price-and-cut (BPC) algorithm for the two-echelon vehicle routing problem with time windows. This problem arises i...

This paper proposes a way to combine the Mesh Adaptive Direct Search (MADS) algorithm with the Cross-Entropy (CE) method for non smooth constrained optimizat...

This work introduces the StoMADS-PB algorithm for constrained stochastic blackbox optimization, which is an extension of the mesh adaptive direct-search (MAD...

Generation expansion planning (GEP) is a classical problem that determines an optimal investment plan for existing and future electricity generation technolo...

We consider the problem of designing vehicle routes in a distribution system that are at the same time cost-effective and visually attractive. In this pape...

This work investigates lot-sizing with component substitution under demand uncertainty. The integration of component substitution with lot-sizing in an uncer...

Scholars and practitioners have long recognised the importance of data-driven operations and supply chain management (OSCM), which typically centres on produ...

Berth allocation and pilotage planning are the two most important decisions made by a seaport for serving incoming vessels. Traditionally, the berth allocati...

We study a new variant of the well-studied Vehicle Routing Problem with Time Windows (VRPTW), called the fragility-constrained VRPTW, which assumes that ...

This work reviews blackbox optimization applications over the last twenty years, addressed using direct search optimization methods. Emphasis is placed on...

Data acquisition and recording in the form of databases are routine operations. The process of collecting data, however, may experience irregularities, res...

This paper studies disjunctive cutting planes in Mixed-Integer Conic Programming. Building on conic duality, we formulate a cut-generating conic program for...

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 He...

The Ninth Montreal IPSW took place on August 19-23, 2019, and was jointly organized by the CRM and IVADO (Institute for Data Valorization). The workshop welc...

We consider a deterministic two-player linear-state differential game, where Player 1 uses piecewise continuous controls, while Player 2 implements impulse...

Conditional estimation given specific covariate values (i.e., local conditional estimation or functional estimation) is ubiquitously useful with applications...

In this paper, we consider the problem of equal risk pricing and hedging in which the fair price of an option is the price that exposes both sides of the con...

In this paper, we propose a simple oligopoly game model to represent the interactions between coalitions of countries in deploying Carbon Dioxide Removal ...

We study a distributionally robust version of the classical capacitated facility location problem with a distributional ambiguity set defined as a Wasserst...

We study a new variant of the vehicle routing problem, which arises in hospital-wide scheduling of physical therapists. Multiple service locations exist for...

Aggregator is an open-source python package which aims to facilitate the exploitation of relational datasets by automating feature aggregation.

The design of key nonlinear systems often requires the use of expensive blackbox simulations presenting inherent discontinuities whose positions in the varia...

We present a modeling of bundle adjustment problems in Julia, as well as a solver for non-linear least square problems (including bundle adjustment problems)...

In several countries, electricity systems are under strong decarbonization pressure. In particular, the Canadian provinces of Quebec and Ontario as well as t...

We consider the iterative solution of regularized saddle-point systems. When the leading block is symmetric and positive semi-definite on an appropriate sub...

In this paper, we introduce a framework for new product dfiffusion that integrates consumer heterogeneity and strategic interactions at individual level. For...

Let \(G=(V,E)\) be a graph and let \(S\subseteq V\) be a subset of its vertices. If the subgraph of \(G\) induced by \(V\setminus S\) is acyclic, the...

A new business opportunity is emerging with the combination of three key market trends: (1) Increased penetration of residential solar PV; (2) Rapid reductio...

Restless bandits are a class of sequential resource allocation problems concerned with allocating one or more resources among several alternative processes...

We consider an integrated optimization problem including the production, inventory, and outbound transportation decisions where a central plant fulfills the ...

Column generation (CG) algorithms are well known to suffer from convergence issues due, mainly, to the degenerate structure of their master problem and the ...

The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destinati...

In this paper, we apply a flexibility based operational planning paradigm to microgrid (MG) energy dispatch. The classic energy dispatch problem with energ...

We provide eigenvalues bounds for a new formulation of the step equations in interior methods for convex quadratic optimization. The matrix of our formulati...

Utility-based shortfall risk measure (SR) effectively captures decision maker’s risk attitude on tail losses by an increasing convex loss function. In this ...

Column generation (CG) is widely used for solving large-scale optimization problems. This article presents a new approach based on a machine learning (ML) t...

Deep learning has redefined modern standards and performance in several areas such as computer vision and natural language processing. With increasing amou...

Artificial Intelligence (AI) is the next society transformation builder. Massive AI-based applications include cloud servers, cell phones, cars, and pandemic...

The accessibility and reducing cost of distributed renewable energy sources are stimulating the emergence of small-scale residential prosumers who can produc...

The monthly crew pairing problem (CPP) consists of determining a least-cost set of feasible crew pairings (sequences of flights starting and ending at a crew...

Maintenance planning for hydropower plants is a crucial problem. In this paper, we evaluate the impact of the Hydropower Production Function (HPF) formulatio...

Cities worldwide struggle with overloaded transportation systems and their externalities, such as traffic congestion and emissions. The emerging technology o...

Traditionally, the design of supply chains for humanitarian operations has been developed distinctly for the different disaster management phases, with littl...

The context of this research is multiobjective optimization where conflicting objectives are present. In this work, these objectives are only available as th...

The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perf...

In the area of hydraulic power generation, there is a great deal of interest in two interdependent domains: operation and maintenance. This interdependence...

Implementation of quantized neural networks on computing hardware leads to considerable speed up and memory saving. However, quantized deep networks are diff...

Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection. However, the large ...

We introduce a novel ensemble learning approach which combines random partitions models through Potts clustering with a non-parametric predictor such as sha...

Deep neural networks usually have unnecessarily high complexities and possibly many features of low utility, especially for tasks that they are not designed ...

The determinantal point process (DPP) provides a promising and attractive alternative to simple random sampling in cluster analysis or classification, for th...

Min-max formulations have attracted great attention in the ML community due to the rise of deep generative models and adversarial methods, while understandin...

Binary neural networks improve computationally efficiency of deep models with a large margin. However, there is still a performance gap between a successful...

Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loadi...

In time-to-event data analysis, the main object of interest is the time elapsed between the occurrence of two ordered events, say \(E_1, E_2\). Sampling fr...

Knowledge distillation is a technique that consists in training a student network, usually of a low capacity, to mimic the representation space and the perfo...

The design of compact deep neural networks is a crucial task to enable widespread adoption of deep neural networks in the real-world, particularly for edge a...

The past few years have seen the ability of cooperative Malware Detection Systems (MDS) to detect complex and unknown malware. In a cooperative setting, an M...

Pruning methods for deep neural networks based on weight magnitude have shown promise in recent research. We propose a new, highly flexible approach to neura...

One of the major challenges in large-scale distributed machine learning involving stochastic gradient methods is the high cost of gradient communication ove...

The smooth operation of largely deployed Internet of Things (IoT) applications will depend on, among other things, effective infrastructure failure detectio...

This work presents the convergence rate analysis of stochastic variants of the broad class of direct-search methods of directional type. It introduces an alg...

Conditional Value at Risk (CVaR) is widely used to account for the preferences of a risk-averse agent in the extreme loss scenarios. To study the effectiven...

This paper proposes a detailed analysis of the evolution of Canadian energy systems under some selected EMF (Energy Modeling Forum) 34 scenarios. Our analysi...

Quebec and Canada at large face difficult challenges to adapt and compete in the upcoming data driven economy. Many analysts already consider the game to be ...

The crew pairing problem is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of...

It is well known that the integrated optimization of multiple and inter-related decisions in a supply chain can bring important benefits to companies. In thi...

Given a set \(\mathcal{R}\) of m disjoint finite regions in the 2-dimensional plane, all regions having polygonal boundaries, and given a set `(\mathc...

This work considers the graph partitioning problem known as maximum k-cut. It focuses on investigating features of a branch-and-bound method to efficiently...

While one can exploit the storage properties and thus the deferability or anticipation potential of many classes of power system loads (such as thermal loads...

In this paper, we introduce, model and solve the pickup and delivery problem with time windows, multiple stacks, and handling operations (PDPTWMS-H). % In...

Column generation algorithms for solving vehicle routing problems often rely on a relaxed pricing subproblem where routes may be non-elementary and which is ...

The energy of a graph \(G\), denoted by \({\cal E}(G)\), is defined as the sum of the absolute values of all eigenvalues of \(G\). In this paper we stu...

The solution to a biobjective optimization problem is composed of a collection of trade-off solution called the Pareto set. The present work studies the que...

Free-Floating Carsharing (FFCS) systems are a promising concept to reduce the traffic volume in cities. However, spatial and temporal mismatches of supply an...

In this paper we consider a differentiated oligopoly with two product varieties that are supplied by two groups of firms. We assume that firms can change t...

In this paper we study the spectral properties of the threshold graphs. In particular, we give lower and upper bounds for the largest and smallest eigenvalue...

Wireless communications can facilitate transfer of synchrophasor data between spatially separated phasor measurement units (PMUs) and phasor data concentrato...

Networked protection systems use information, communication and computation technologies to collect and process sensor data from spatially distributed sensor...