Benchmarking new optimization methods on test problems is essential for assessing their performance and tuning their parameters. Yet, few problems are avail...

Benchmarking is essential for assessing the effectiveness of optimization algorithms. This is especially true in derivative-free optimization, where target ...

Multiobjective blackbox optimization deals with problems where the objective and constraint functions are the outputs of a numerical simulation. In this cont...

This work introduces solar, a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present differ...

The fine-tuning of Large Language Models (LLMs) has enabled them to recently achieve milestones in natural language processing applications. The emergenc...

NOMAD is software for optimizing blackbox problems. In continuous development since 2001, it constantly evolved with the integration of new algorithmic...

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 pr...

We are interested in blackbox optimization for which the user is aware of monotonic behaviour of some constraints defining the problem. That is, when incr...

The mesh adaptive direct search (MADS) algorithm is designed for blackbox optimization problems for which the functions defining the objective and the constr...

Despite the lack of theoretical and practical convergence support, the Nelder-Mead (NM) algorithm is widely used to solve unconstrained optimization proble...

Blackbox optimization problems are often contaminated with numerical noise, and direct search methods such as the Mesh Adaptive Direct Search (MADS) algorit...

This document describes the NOMAD software, a C++ implementation of the Mesh Adaptive Direct Search (MADS) algorithm designed for constrained optimization of...

Blackbox optimization deals with situations in which the objective function and constraints are typically computed by launching a time-consuming computer ...

The Mesh Adaptive Direct Search (MADS) class of algorithms is designed for nonsmooth optimization, where the objective function and constraints are typical...