152 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Polytechnique MontréalCA27 papers
- HEC MontréalCA13 papers
- McGill UniversityCA9 papers
- Université de MontréalCA6 papers
- Mila - Quebec Artificial Intelligence InstituteCA5 papers
- Université de SherbrookeCA4 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Université Fédérale de Toulouse Midi-PyrénéesFR3 papers
- University of EdinburghGB3 papers
- Kyoto UniversityJP2 papers
- Laboratoire des Sciences du Numérique de NantesFR2 papers
- Laboratoire des Sciences pour la Conception, l'Optimisation et la ProductionFR2 papers
19 papers · 1 filter
High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings
Rémy Priem, Youssef Diouane, Nathalie Bartoli +2
Bayesian optimization (BO) is one of the most powerful strategies to solve computationally expensive-to-evaluate blackbox optimization problems. However, BO methods are conventiona…
Subsidizing a New Technology: An Impulse Stackelberg Game Approach
Utsav Sadana, Georges Zaccour
Governments are motivated to subsidize profit-driven firms that manufacture zero-emission vehicles to ensure they become price-competitive. This paper introduces a dynamic Stackelb…
Fidelity and interruption control for expensive constrained multi-fidelity blackbox optimization
Stéphane Alarie, Charles Audet, Miguel Diago +2
This work introduces a novel blackbox optimization algorithm for computationally expensive constrained multi-fidelity problems. When applying a direct search method to such problem…
High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft
Paul Saves, Youssef Diouane, Nathalie Bartoli +2
Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization. In this context, different approaches can be u…
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Utsav Sadana, Abhilash Chenreddy, Erick Delage +3
Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to so…
Sequential stochastic blackbox optimization with zeroth-order gradient estimators
Charles Audet, Jean Bigeon, Romain Couderc +1
This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise following an unknown d…
A stochastic integer programming approach to reserve staff scheduling with preferences
Carl Perreault-Lafleur, Margarida Carvalho, Guy Desaulniers
Nowadays, reaching a high level of employee satisfaction in efficient schedules is an important and difficult task faced by companies. We tackle a new variant of the personnel sche…
AILS-II: An Adaptive Iterated Local Search Heuristic for the Large-scale Capacitated Vehicle Routing Problem
Vinícius R. Máximo, Jean-François Cordeau, Mariá C. V. Nascimento
A recent study on the classical Capacitated Vehicle Routing Problem (CVRP) introduced an adaptive version of the widely used Iterated Local Search (ILS) paradigm, hybridized with a…
Quantifying uncertainty with ensembles of surrogates for blackbox optimization
Charles Audet, Sébastien Le Digabel, Renaud Saltet
This work is in the context of blackbox optimization where the functions defining the problem are expensive to evaluate and where no derivatives are available. A tried and tested t…
Feedback Nash Equilibria in Differential Games with Impulse Control
Utsav Sadana, Puduru Viswanadha Reddy, Georges Zaccour
We study a class of deterministic finite-horizon two-player nonzero-sum differential games where players are endowed with different kinds of controls. We assume that Player 1 uses…
Combining Cross Entropy and MADS methods for inequality constrained global optimization
Charles Audet, Romain Couderc, Jean Bigeon
This paper proposes a way to combine the Mesh Adaptive Direct Search (MADS) algorithm with the Cross-Entropy (CE) method for non smooth constrained optimization. The CE method is u…
Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates
Kwassi Joseph Dzahini, Michael Kokkolaras, Sébastien Le Digabel
This work introduces the StoMADS-PB algorithm for constrained stochastic blackbox optimization, which is an extension of the mesh adaptive direct-search (MADS) method originally de…
Scaled Projected-Directions Methods with Application to Transmission Tomography
Guillaume Mestdagh, Yves Goussard, Dominique Orban
Statistical image reconstruction in X-Ray computed tomography yields large-scale regularized linear least-squares problems with nonnegativity bounds, where the memory footprint of…
Implementing a smooth exact penalty function for general constrained nonlinear optimization
Ron Estrin, Michael Friedlander, Dominique Orban +1
We build upon Estrin et al. (2019) to develop a general constrained nonlinear optimization algorithm based on a smooth penalty function proposed by Fletcher (1970, 1973b). Although…
CONICOPF: Conic relaxations for AC optimal power flow computations
Christian Bingane, Miguel F. Anjos, Sébastien Le Digabel
Computational speed and global optimality are key needs for practical algorithms for the optimal power flow problem. Two convex relaxations offer a favorable trade-off between the…
Tight-and-cheap conic relaxation for the optimal reactive power dispatch problem
Christian Bingane, Miguel F. Anjos, Sébastien Le Digabel
The optimal reactive power dispatch (ORPD) problem is an alternating current optimal power flow (ACOPF) problem where discrete control devices for regulating the reactive power, su…
A Bilevel Approach to Optimal Price-Setting of Time-and-Level-of-Use Tariffs
Mathieu Besançon, Miguel F. Anjos, Luce Brotcorne +1
Time-and-Level-of-Use (TLOU) is a recently proposed pricing policy for energy, extending Time-of-Use with the addition of a capacity that users can book for a given time frame, red…
ε-Nash Mean Field Game Theory for Nonlinear Stochastic Dynamical Systems with Major and Minor Agents
Mojtaba Nourian, Peter E. Caines
This paper studies a large population dynamic game involving nonlinear stochastic dynamical systems with agents of the following mixed types: (i) a major agent, and (ii) a populati…
Linear Consensus Algorithms Based on Balanced Asymmetric Chains
Sadegh Bolouki, Roland P. Malhame
Multi agent consensus algorithms with update steps based on so-called balanced asymmetric chains, are analyzed. For such algorithms it is shown that (i) the set of accumulation poi…