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20172022
most citedEvaluation complexity bounds for smooth constrained nonlinear optimisation using scaled KKT conditions, high-order models and the criticality measure

4 citations · 13 across the 8 of their papers we have counts for

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13 papers · 1 filter

math.OC2022

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…

math.OC20211 cited

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…

math.OC2021

The Impact of Noise on Evaluation Complexity: The Deterministic Trust-Region Case

Stefania Bellavia, Gianmarco Gurioli, Benedetta Morini +1

Intrinsic noise in objective function and derivatives evaluations may cause premature termination of optimization algorithms. Evaluation complexity bounds taking this situation int…

math.OC2020

Strong Evaluation Complexity of An Inexact Trust-Region Algorithm for Arbitrary-Order Unconstrained Nonconvex Optimization

C. Cartis, N. I. M. Gould, Ph. L. Toint

A trust-region algorithm using inexact function and derivatives values is introduced for solving unconstrained smooth optimization problems. This algorithm uses high-order Taylor m…

math.OC2020

Adaptive Regularization for Nonconvex Optimization Using Inexact Function Values and Randomly Perturbed Derivatives

S. Bellavia, G. Gurioli, B. Morini +1

A regularization algorithm allowing random noise in derivatives and inexact function values is proposed for computing approximate local critical points of any order for smooth unco…

math.OC20203 cited

Strong Evaluation Complexity Bounds for Arbitrary-Order Optimization of Nonconvex Nonsmooth Composite Functions

Coralia Cartis, Nick Gould, Philippe L. Toint

We introduce the concept of strong high-order approximate minimizers for nonconvex optimization problems. These apply in both standard smooth and composite non-smooth settings, and…