This work is in the context of blackbox optimization where the functions defining the problem are expensive to evaluate and where no derivatives are availabl...

The goal of the Encyclopedia of Optimization is to introduce the reader to a complete set of topics that sh...

The Quadratic Knapsack Problem (QKP) is a well-known combinatorial optimization problem which amounts to maximizing a quadratic function of binary variables,...

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

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

L'algorithme NCL est conçu pour les problèmes d'optimisation lisse dont les dérivées premières et secondes sont disponibles, y compris les problèmes dont ...

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

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

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...

Le Neuvième atelier de résolution de problèmes industriels de Montréal, qui eut lieu du 19 au 23 août 2019, fut organisé conjointement par le CRM et l'Instit...

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...

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

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

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

Nous développons des bornes sur les valeurs propres d’une nouvelle formulation des équations de Newton dans les méthodes de points intérieurs pour l’optimisa...

In derivative-free and blackbox optimization, the objective function is often evaluated through the execution of a computer program seen as a blackbox. It ...

A highly influential ingredient of many techniques designed to exploit sparsity in numerical optimization is the so-called chordal extension of a graph repre...

We introduce an iterative method named BiLQ for solving general square linear systems \(Ax=b\) based on the Lanczos biorthogonalization process defined by ...

Dans ce papier, nous comparons la méthode BFGS à la méthode du gradient conjugué (CG) pour résoudre un problème d'optimisation sans contrainte avec un algori...

Statistical image reconstruction in X-Ray computed tomography yields large-scale regularized linear least-squares problems with nonnegativity bounds, where t...