A Noise-Tolerant Quasi-Newton Algorithm for Unconstrained Optimization

DOI10.1137/20M1373190zbMath1484.90117arXiv2010.04352OpenAlexW4225873238MaRDI QIDQ5026838

Nocedal, Jorge, Yuchen Xie, Byrd, Richard H., Hao-Jun Michael Shi

Publication date: 8 February 2022

Published in: SIAM Journal on Optimization (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2010.04352

zbMATH Keywords

unconstrained optimizationstochastic optimizationnonlinear optimizationquasi-Newton methodderivative-free optimizationnoisy optimization

Mathematics Subject Classification ID

Nonlinear programming (90C30) Derivative-free methods and methods using generalized derivatives (90C56) Methods of quasi-Newton type (90C53)

Related Items

On the numerical performance of finite-difference-based methods for derivative-free optimization, Adaptive Finite-Difference Interval Estimation for Noisy Derivative-Free Optimization, An overview of stochastic quasi-Newton methods for large-scale machine learning, Secant penalized BFGS: a noise robust quasi-Newton method via penalizing the secant condition, Unnamed Item

Uses Software

• L-BFGS
• CUTEr
• PETSc
• SifDec
• IMFIL
• CUTEst

Cites Work

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• A Stochastic Quasi-Newton Method for Large-Scale Optimization
• On the limited memory BFGS method for large scale optimization
• A theoretical and empirical comparison of gradient approximations in derivative-free optimization
• CUTEst: a constrained and unconstrained testing environment with safe threads for mathematical optimization
• Random gradient-free minimization of convex functions
• Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization”
• Estimating Derivatives of Noisy Simulations
• Estimating Computational Noise
• Implicit Filtering
• Computing Forward-Difference Intervals for Numerical Optimization
• The Effect of Rounding Errors on Newton-like Methods
• Inaccuracy in quasi-Newton methods: Local improvement theorems
• A Tool for the Analysis of Quasi-Newton Methods with Application to Unconstrained Minimization
• Numerical Optimization
• Superlinear Convergence and Implicit Filtering
• Derivative-Free Optimization of Noisy Functions via Quasi-Newton Methods
• Global Convergence Rate Analysis of a Generic Line Search Algorithm with Noise
• Analysis of the BFGS Method with Errors
• CUTEr and SifDec