Nous considérons les problèmes d'optimisation sans dérivées avec variables continues, entières, discrètes et de catégorie dans le contexte d'applications i...

We study X-ray tomograqphic reconstruction using statistical methods. The problem is expressed in cylindrical coordinates, which yield significant computatio...

La Programmation Dynamique Stochastique (PDS) est une puissante méthode applicable aux problèmes mutli-étapes non-convexes et stochastiques. Nous étudions ...

Nous proposons une méthode itérative pour les problèmes aux moindres carrés linéaires \(A x \approx b\) nommée LSLQ. La méthode repose sur le processus ...

La quadrature de Gauss-Radau nous permet d'obtenir une borne supérieure peu coûteuse sur l'erreur en norme Euclidienne associée aux itérés de SYMMLQ appliqu...

NLP.py constitue un écosystème de programmation simplifiant le développement d'algorithmes d'optimisation dans un langage de haut-niveau aussi puissant ...

Une collection de systèmes linéaires engendrés au cours des itérations d'une méthode de points intérieurs pour l'optimisation quadratique convexe est...

Adaptative cubic regularization (ARC) methods for unconstrained optimization compute steps from linear systems with a shifted Hessian in the spirit of the mo...

Dans de nombreuses applications réelles d'ingénierie, il est impossible de stocker les Jacobiens ou les Hessiens de manière explicite. L'implémentation de mé...

A preconditioned variant of the Golub and Kahan (1965) bidiagonalization process recently proposed by Arioli (2013) and Arioli and Orban (2013) allows us to ...

We propose a generalization of the limited-memory Cholesky factorization of Lin and Moré (1999) to the symmetric indefinite case with special interest in sym...

Symmetric quasi-definite systems may be interpreted as regularized linear least-squares problem in appropriate metrics and arise from applications such as re...

We describe the most recent evolution of our constrained and unconstrained testing environment and its accompanying SIF decoder. Code-named SIFDecode and CU...

Projected Krylov methods are full-space formulations of Krylov methods that take place in a nullspace. Provided projections into the nullspace can be compute...

Interior-point methods feature prominently among numerical methods for inequality-constrained optimization problems, and involve the need to solve a sequ...

Interior-point methods in semi-definite programming (SDP) require the solution of a sequence of linear systems which are used to derive the search directions...

OPAL is a general-purpose system for modeling and solving algorithm optimization problems. OPAL takes an algorithm as input, and as output it suggests para...

Implementations of the Simplex method differ only in very specific aspects such as the pivot rule. Similarly, most relaxation methods for mixed-integer ...

The analytic center cutting plane method and its proximal variant are well known techniques for solving convex programming problems. We propose two seq...

We consider a class of infeasible, path-following methods for convex quadratric programming. Our methods are designed to be effective for solving both nonde...