Philippe L. Toint

activity

20172026

most citedA note on preconditioning weighted linear least squares, with consequences for weakly-constrained variational data assimilation

6 citations · 14 across the 20 of their papers we have counts for

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Showing 2024Show all

3 papers · 1 filter

math.OC2024

Examples of slow convergence for adaptive regularization optimization methods are not isolated

Philippe L. Toint

The adaptive regularization algorithm for unconstrained nonconvex optimization was shown in Nesterov and Polyak (2006) and Cartis, Gould and Toint (2011) to require, under standard…

math.OC2024

Refining asymptotic complexity bounds for nonconvex optimization methods, including why steepest descent is $o (ε^{- 2})$ rather than $O (ε^{- 2})$

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…

math.OC2024

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…

Examples of slow convergence for adaptive regularization optimization methods are not isolated

Refining asymptotic complexity bounds for nonconvex optimization methods, including why steepest descent is o(ε−2) rather than O(ε−2)

S2MPJ and CUTEst optimization problems for Matlab, Python and Julia

Refining asymptotic complexity bounds for nonconvex optimization methods, including why steepest descent is $o (ε^{- 2})$ rather than $O (ε^{- 2})$