The impact of noise on evaluation complexity: the deterministic trust-region case (Q2696963)

scientific article; zbMATH DE number 7675405

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English	The impact of noise on evaluation complexity: the deterministic trust-region case	scientific article; zbMATH DE number 7675405

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instance of

scholarly article

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title

The impact of noise on evaluation complexity: the deterministic trust-region case (English)

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Journal of Optimization Theory and Applications

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publication date

17 April 2023

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full work available at URL

https://arxiv.org/abs/2104.02519

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zbMATH Keywords

noise

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evaluation complexity

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trust-region methods

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inexact functions and derivatives

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describes a project that uses

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Convergence of Trust-Region Methods Based on Probabilistic Models

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Stochastic analysis of an adaptive cubic regularization method under inexact gradient evaluations and dynamic Hessian accuracy

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Adaptive cubic regularization methods with dynamic inexact Hessian information and applications to finite-sum minimization

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Adaptive Regularization Algorithms with Inexact Evaluations for Nonconvex Optimization

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Adaptive regularization for nonconvex optimization using inexact function values and randomly perturbed derivatives

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Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise

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On the employment of inexact restoration for the minimization of functions whose evaluation is subject to errors

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Iteration and evaluation complexity for the minimization of functions whose computation is intrinsically inexact

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On the Global Convergence of Trust Region Algorithms Using Inexact Gradient Information

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Worst-case evaluation complexity of regularization methods for smooth unconstrained optimization using Hölder continuous gradients

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Universal Regularization Methods: Varying the Power, the Smoothness and the Accuracy

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Sharp Worst-Case Evaluation Complexity Bounds for Arbitrary-Order Nonconvex Optimization with Inexpensive Constraints

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Stochastic optimization using a trust-region method and random models

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Worst-Case Examples for Lasserre’s Measure–Based Hierarchy for Polynomial Optimization on the Hypercube

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Regularized Newton Methods for Minimizing Functions with Hölder Continuous Hessians

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Recursive Trust-Region Methods for Multiscale Nonlinear Optimization

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An algorithm for the minimization of nonsmooth nonconvex functions using inexact evaluations and its worst-case complexity

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A note on solving nonlinear optimization problems in variable precision

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Testing Unconstrained Optimization Software

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Gradient methods for minimizing composite functions

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Universal gradient methods for convex optimization problems

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A Stochastic Line Search Method with Expected Complexity Analysis

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Newton-type methods for non-convex optimization under inexact Hessian information

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Recent advances in trust region algorithms

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Identifiers

Mathematics Subject Classification ID

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10.1007/S10957-022-02153-5

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Sitelinks

Mathematics(1 entry)

mardi Publication:2696963