68 citations · 169 across the 22 of their papers we have counts for
23 papers
A Block-Coordinate Approach of Multi-level Optimization with an Application to Physics-Informed Neural Networks
Serge Gratton, Valentin Mercier, Elisa Riccietti +1
Multi-level methods are widely used for the solution of large-scale problems, because of their computational advantages and exploitation of the complementarity between the involved…
Multilevel Objective-Function-Free Optimization with an Application to Neural Networks Training
S. Gratton, A. Kopanicakova, Ph. L. Toint
A class of multi-level algorithms for unconstrained nonlinear optimization is presented which does not require the evaluation of the objective function. The class contains the mome…
Yet another fast variant of Newton's method for nonconvex optimization
Serge Gratton, Sadok Jerad, Philippe L. Toint
A class of second-order algorithms is proposed for minimizing smooth nonconvex functions that alternates between regularized Newton and negative curvature steps in an iteration-dep…
Convergence properties of an Objective-Function-Free Optimization regularization algorithm, including an complexity bound
S. Gratton, S. Jerad, Ph. L. Toint
An adaptive regularization algorithm for unconstrained nonconvex optimization is presented in which the objective function is never evaluated, but only derivatives are used. This a…
A coarse space acceleration of deep-DDM
Valentin Mercier, Serge Gratton, Pierre Boudier
The use of deep learning methods for solving PDEs is a field in full expansion. In particular, Physical Informed Neural Networks, that implement a sampling of the physical domain a…
OPM, a collection of Optimization Problems in Matlab
Serge Gratton, Philippe L. Toint
OPM is a small collection of CUTEst unconstrained and bound-constrained nonlinear optimization problems, which can be used in Matlab for testing optimization algorithms directly (i…