no code implementations • 18 Aug 2023 • Charlie Sire, Yann Richet, Rodolphe Le Riche, Didier Rullière, Jérémy Rohmer, Lucie Pheulpin
Quantization summarizes continuous distributions by calculating a discrete approximation.
no code implementations • 30 Oct 2021 • Jhouben Cuesta-Ramirez, Rodolphe Le Riche, Olivier Roustant, Guillaume Perrin, Cedric Durantin, Alain Gliere
In this article, costly mixed problems are approached through Gaussian processes where the discrete variables are relaxed into continuous latent variables.
1 code implementation • 30 Mar 2021 • Rodolphe Le Riche, Victor Picheny
It is commonly believed that Bayesian optimization (BO) algorithms are highly efficient for optimizing numerically costly functions.
1 code implementation • 9 Mar 2021 • Reda El Amri, Rodolphe Le Riche, Céline Helbert, Christophette Blanchet-Scalliet, Sébastien da Veiga
The main contribution of this work is an acquisition criterion that accounts for both the average improvement in objective function and the constraint reliability.
no code implementations • 18 Jan 2021 • Youssef Diouane, Victor Picheny, Rodolphe Le Riche, Alexandre Scotto Di Perrotolo
By following a classical scheme for the trust region (based on a sufficient decrease condition), the proposed algorithm enjoys global convergence properties, while departing from EGO only for a subset of optimization steps.
no code implementations • 29 Aug 2019 • David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoit Enaux, Vincent Herbert
Parametric shape optimization aims at minimizing an objective function f(x) where x are CAD parameters.
no code implementations • 12 Nov 2018 • Adrien Spagnol, Rodolphe Le Riche, Sebastien Da Veiga
However it does not account for the specific structure of optimization problems where we would like to identify which variables most lead to constraints satisfaction and low values of the objective function.
no code implementations • 9 Nov 2018 • David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoit Enaux, Vincent Herbert
Multi-objective optimization aims at finding trade-off solutions to conflicting objectives.
no code implementations • 27 Sep 2018 • David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoit Enaux, Vincent Herbert
When the number of experiments is severely restricted and/or when the number of objectives increases, uncovering the whole set of Pareto optimal solutions is out of reach, even for surrogate-based approaches: the proposed solutions are sub-optimal or do not cover the front well.
no code implementations • 8 Mar 2016 • Hossein Mohammadi, Rodolphe Le Riche, Eric Touboul
The Efficient Global Optimization (EGO) algorithm uses a conditional Gaus-sian Process (GP) to approximate an objective function known at a finite number of observation points and sequentially adds new points which maximize the Expected Improvement criterion according to the GP.
no code implementations • 2 Feb 2016 • Hossein Mohammadi, Rodolphe Le Riche, Nicolas Durrande, Eric Touboul, Xavier Bay
A measure for data-model discrepancy is proposed which serves for choosing a regularization technique. In the second part of the paper, a distribution-wise GP is introduced that interpolates Gaussian distributions instead of data points.