68 citations · 169 across the 22 of their papers we have counts for
7 papers · 1 filter
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…
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…
Adaptive Regularization Minimization Algorithms with Non-Smooth Norms and Euclidean Curvature
Serge Gratton, Philippe L. Toint
A regularization algorithm (AR1pGN) for unconstrained nonlinear minimization is considered, which uses a model consisting of a Taylor expansion of arbitrary degree and regularizati…
Minimization of nonsmooth nonconvex functions using inexact evaluations and its worst-case complexity
S. Gratton, E. Simon, Ph. L. Toint
An adaptive regularization algorithm using inexact function and derivatives evaluations is proposed for the solution of composite nonsmooth nonconvex optimization. It is shown that…
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…