no code implementations • 11 Mar 2024 • Gabriel P. Langlois, Jatan Buch, Jérôme Darbon
State-of-the-art algorithms for Maxent models, however, were not originally designed to handle big data sets; these algorithms either rely on technical devices that may yield unreliable numerical results, scale poorly, or require smoothness assumptions that many practical Maxent models lack.
no code implementations • 30 Nov 2021 • Jérôme Darbon, Gabriel P. Langlois
Since modern big data sets can contain hundreds of thousands to billions of predictor variables, variable selection methods depend on efficient and robust optimization algorithms to perform well.
no code implementations • 24 Sep 2021 • Jérôme Darbon, Gabriel P. Langlois
To address this issue, we introduce accelerated nonlinear PDHG methods that achieve an optimal convergence rate with stepsize parameters that are simple and efficient to compute.
no code implementations • 22 Apr 2021 • Jérôme Darbon, Gabriel P. Langlois, Tingwei Meng
In [23, 26], connections between these optimization problems and (multi-time) Hamilton--Jacobi partial differential equations have been proposed under the convexity assumptions of both the data fidelity and regularization terms.