A trust region method for noisy unconstrained optimization

DOI10.1007/s10107-023-01941-9zbMath1526.65023arXiv2201.00973OpenAlexW4360829331MaRDI QIDQ6052069

Nocedal, Jorge, Shigeng Sun

Publication date: 23 October 2023

Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2201.00973

zbMATH Keywords

nonlinear optimizationtrust region methodnoisy optimization

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Complexity and performance of numerical algorithms (65Y20)

Cites Work

• Unnamed Item
• Unnamed Item
• Sample size selection in optimization methods for machine learning
• More test examples for nonlinear programming codes
• Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
• Stochastic optimization using a trust-region method and random models
• Numerical experiments with the Lancelot package (Release \(A\)) for large-scale nonlinear optimization
• A theoretical and empirical comparison of gradient approximations in derivative-free optimization
• Random gradient-free minimization of convex functions
• The impact of noise on evaluation complexity: the deterministic trust-region case
• Hybrid Deterministic-Stochastic Methods for Data Fitting
• Multifidelity approaches for optimization under uncertainty
• Estimating Computational Noise
• Numerical Optimization
• Adaptive Sampling Strategies for Stochastic Optimization
• Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
• On Sampling Rates in Simulation-Based Recursions
• Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods
• Optimization Methods for Large-Scale Machine Learning
• Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization
• Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise
• Adaptive Finite-Difference Interval Estimation for Noisy Derivative-Free Optimization
• On the Global Convergence of Trust Region Algorithms Using Inexact Gradient Information
• Analysis of the BFGS Method with Errors
• A Stochastic Line Search Method with Expected Complexity Analysis
• Exact and inexact subsampled Newton methods for optimization
• Constrained Optimization in the Presence of Noise