49 citations · 137 across the 24 of their papers we have counts for
24 papers
Parallel versions of the mesh adaptive direct search algorithm
Sébastien Le Digabel, Antoine Lesage-Landry, Samuel Mendoza +1
This work surveys the different parallel variants of the mesh adaptive direct search (MADS) algorithm for constrained blackbox optimization. These problems can inherently imply hig…
Adaptive direct search algorithms with relaxable and quantifiable constraints
Charles Audet, Théo Denorme, Youssef Diouane +2
This work introduces ADS-PB, an extension of the Adaptive Direct Search (ADS) framework for solving constrained blackbox optimization problems. With ADS, iterates progress without…
Surrogate-based categorical neighborhoods for mixed-variable blackbox optimization
Charles Audet, Youssef Diouane, Edward Hallé-Hannan +2
In simulation-based engineering, design choices are often obtained following the optimization of complex blackbox models. These models frequently involve mixed-variable domains wit…
A penalty-interior point method combined with MADS for equality and inequality constrained optimization
Charles Audet, Andrea Brilli, Youssef Diouane +3
This work introduces MADS-PIP, an efficient framework that integrates a penalty-interior point strategy into the mesh adaptive direct search (MADS) algorithm for solving nonsmooth…
Multi-fidelity constraints in blackbox optimization
Stéphane Alarie, Charles Audet, Miguel Diago +2
This work studies constrained blackbox optimization problems that cannot be solved in reasonable time due to prohibitive computational costs. This challenge is especially prevalent…
An Inexact Modified Quasi-Newton Method for Nonsmooth Regularized Optimization
Nathan Allaire, Sébastien Le Digabel, Dominique Orban
We introduce iR2N, a modified proximal quasi-Newton method for minimizing the sum of a smooth function and a lower semi-continuous prox-bounded function , allowing inexact e…
Adaptive direct search algorithms for constrained optimization
Charles Audet, Théo Denorme, Youssef Diouane +2
Two families of directional direct search methods have emerged in derivative-free and blackbox optimization (DFO and BBO), each based on distinct principles: Mesh Adaptive Direct S…
CatMADS: Mesh Adaptive Direct Search for constrained blackbox optimization with categorical variables
Charles Audet, Youssef Diouane, Edward Hallé-Hannan +2
Solving optimization problems in which functions are blackboxes and variables involve different types poses significant theoretical and algorithmic challenges. Nevertheless, such s…
Efficient search strategies for constrained multiobjective blackbox optimization
Sébastien Le Digabel, Antoine Lesage-Landry, Ludovic Salomon +1
Multiobjective blackbox optimization deals with problems where the objective and constraint functions are the outputs of a numerical simulation. In this context, no derivatives are…
solar: A solar thermal power plant simulator for blackbox optimization benchmarking
Nicolau Andrés-Thió, Charles Audet, Miguel Diago +5
This work introduces solar, a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a c…
A distance for mixed-variable and hierarchical domains with meta variables
Edward Hallé-Hannan, Charles Audet, Youssef Diouane +2
Heterogeneous datasets emerge in various machine learning and optimization applications that feature different input sources, types or formats. Most models or methods do not native…
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…
Efficient Training Under Limited Resources
Mahdi Zolnouri, Dounia Lakhmiri, Christophe Tribes +2
Training time budget and size of the dataset are among the factors affecting the performance of a Deep Neural Network (DNN). This paper shows that Neural Architecture Search (NAS),…
Scaling Deep Networks with the Mesh Adaptive Direct Search algorithm
Dounia Lakhmiri, Mahdi Zolnouri, Vahid Partovi Nia +2
Deep neural networks are getting larger. Their implementation on edge and IoT devices becomes more challenging and moved the community to design lighter versions with similar perfo…
Handling of constraints in multiobjective blackbox optimization
Jean Bigeon, Sébastien Le Digabel, Ludovic Salomon
This work proposes the integration of two new constraint-handling approaches into the blackbox constrained multiobjective optimization algorithm DMulti-MADS, an extension of the Me…
Hierarchically constrained blackbox optimization
Stéphane Alarie, Charles Audet, Paulin Jacquot +1
In blackbox optimization, evaluation of the objective and constraint functions is time consuming. In some situations, constraint values may be evaluated independently or sequential…
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…
NOMAD version 4: Nonlinear optimization with the MADS algorithm
Charles Audet, Sébastien Le Digabel, Viviane Rochon Montplaisir +1
NOMAD is software for optimizing blackbox problems. In continuous development since 2001, it constantly evolved with the integration of new algorithmic features published in scient…
Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system
Dounia Lakhmiri, Ryan Alimo, Sebastien Le Digabel
The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operat…
Optimization of noisy blackboxes with adaptive precision
Stéphane Alarie, Charles Audet, Pierre-Yves Bouchet +1
In derivative-free and blackbox optimization, the objective function is often evaluated through the execution of a computer program seen as a blackbox. It can be noisy, in the sens…
StoMADS: Stochastic blackbox optimization using probabilistic estimates
Charles Audet, Kwassi Joseph Dzahini, Michael Kokkolaras +1
This work introduces StoMADS, a stochastic variant of the mesh adaptive direct-search (MADS) algorithm originally developed for deterministic blackbox optimization. StoMADS conside…
HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search
Dounia Lakhmiri, Sébastien Le Digabel, Christophe Tribes
The performance of deep neural networks is highly sensitive to the choice of the hyperparameters that define the structure of the network and the learning process. When facing a ne…
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
Victor Picheny, Robert B. Gramacy, Stefan M. Wild +1
An augmented Lagrangian (AL) can convert a constrained optimization problem into a sequence of simpler (e.g., unconstrained) problems, which are then usually solved with local solv…
A Taxonomy of Constraints in Simulation-Based Optimization
Sébastien Le Digabel, Stefan M. Wild
The types of constraints encountered in black-box and simulation-based optimization problems differ significantly from those treated in nonlinear programming. We introduce a charac…