17 citations · 84 across the 29 of their papers we have counts for
30 papers
A Stochastic Objective-Function-Free Adaptive Regularization Method with Optimal Complexity
Serge Gratton, Sadok Jerad, Philippe L. Toint
A fully stochastic second-order adaptive-regularization method for unconstrained nonconvex optimization is presented which never computes the objective-function value, but yet achi…
Complexity of Adagrad and other first-order methods for nonconvex optimization problems with bounds constraints
Serge Gratton, Sadok Jerad, Philippe L. Toint
A parametric class of trust-region algorithms for constrained nonconvex optimization is analyzed, where the objective function is never computed. By defining appropriate first-orde…
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…