1 citations · 1 across the 6 of their papers we have counts for
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Refining asymptotic complexity bounds for nonconvex optimization methods, including why steepest descent is rather than
Serge Gratton, Chee-Khian Sim, Philippe L. Toint
We revisit the standard ``telescoping sum'' argument ubiquitous in the final steps of analyzing evaluation complexity of algorithms for smooth nonconvex optimization, and obtain a…
S2MPJ and CUTEst optimization problems for Matlab, Python and Julia
Serge Gratton, Philippe L. Toint
A new decoder for the SIF test problems of the CUTEst collection is described, which produces problem files allowing the computation of values and derivatives of the objective func…
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…
Trust-region algorithms: probabilistic complexity and intrinsic noise with applications to subsampling techniques
S. Bellavia, G. Gurioli, B. Morini +1
A trust-region algorithm is presented for finding approximate minimizers of smooth unconstrained functions whose values and derivatives are subject to random noise. It is shown tha…