10 citations · 24 across the 13 of their papers we have counts for
5 papers · 2 filters
NomadBBO: A direct-search framework for constrained mixed-variable blackbox optimization in Python
Edward Hallé-Hannan, Christophe Tribes
Mixed-variable blackbox optimization arises in simulation-based engineering and machine learning, where objective and constraint functions are expensive to evaluate and variables m…
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…