229 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université Fédérale de Toulouse Midi-PyrénéesFR19 papers
- Centre National de la Recherche ScientifiqueFR17 papers
- Institut Supérieur de l'Aéronautique et de l'Espace (ISAE-SUPAERO)FR15 papers
- Université Toulouse III - Paul SabatierFR8 papers
- École Nationale de l’Aviation CivileFR6 papers
- Office National d'Études et de Recherches AérospatialesFR5 papers
- Université Paris-SaclayFR5 papers
- Institut Clément AderFR4 papers
- Institut de Recherche en Astrophysique et PlanétologieFR4 papers
- Polytechnique MontréalCA4 papers
- Université de ToulouseFR4 papers
- Astrophysique, Instrumentation et ModélisationFR3 papers
7 papers · 1 filter
System Architecture Optimization Strategies: Dealing with Expensive Hierarchical Problems
Jasper H. Bussemaker, Paul Saves, Nathalie Bartoli +2
Choosing the right system architecture for the problem at hand is challenging due to the large design space and high uncertainty in the early stage of the design process. Formulati…
High-dimensional mixed-categorical Gaussian processes with application to multidisciplinary design optimization for a green aircraft
Paul Saves, Youssef Diouane, Nathalie Bartoli +2
Recently, there has been a growing interest in mixed-categorical metamodels based on Gaussian Process (GP) for Bayesian optimization. In this context, different approaches can be u…
Graph-Based Convexification of Nested Signal Temporal Logic Constraints for Trajectory Optimization
Thomas Claudet, Davide Martire, Damiana Losa +2
Optimizing high-level mission planning constraints is traditionally solved in exponential time and requires to split the problem into several ones, making the connections between t…
A mixed-categorical correlation kernel for Gaussian process
P. Saves, Y. Diouane, N. Bartoli +2
Recently, there has been a growing interest for mixed-categorical meta-models based on Gaussian process (GP) surrogates. In this setting, several existing approaches use different…
A Subsampling Line-Search Method with Second-Order Results
El-houcine Bergou, Youssef Diouane, Vladimir Kunc +2
In many contemporary optimization problems such as those arising in machine learning, it can be computationally challenging or even infeasible to evaluate an entire function or its…
A stochastic Levenberg-Marquardt method using random models with complexity results
E. Bergou, Y. Diouane, V. Kungurtsev +1
Globally convergent variants of the Gauss-Newton algorithm are often the methods of choice to tackle nonlinear least-squares problems. Among such frameworks, Levenberg-Marquardt an…
A Line-Search Algorithm Inspired by the Adaptive Cubic Regularization Framework and Complexity Analysis
El houcine Bergou, Youssef Diouane, Serge Gratton
Adaptive regularized framework using cubics has emerged as an alternative to line-search and trust-region algorithms for smooth nonconvex optimization, with an optimal complexity a…