Numerical Experience with a Class of Algorithms for Nonlinear Optimization Using Inexact Function and Gradient Information

DOI10.1137/0914023zbMath0772.65042OpenAlexW2043728417MaRDI QIDQ5286355

Publication date: 29 June 1993

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2060/19900002916

zbMATH Keywords

unconstrained optimization robustness numerical tests nonlinear programming inexact function evaluations inexact gradient low accuracy trust region quasi-Newton algorithms variable-accuracy simulation

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30)

Related Items (13)

Inexact restoration for derivative-free expensive function minimization and applications ⋮ Evaluating gradients in optimal control: continuous adjoints versus automatic differentiation ⋮ Statistics of Nadaraya-Watson estimator errors in surrogate-based optimization ⋮ Using inexact gradients in a multilevel optimization algorithm ⋮ Effect of inexact adjoint solutions on the discrete-adjoint approach to gradient-based optimization ⋮ A nonlinear conjugate gradient method using inexact first-order information ⋮ A note on solving nonlinear optimization problems in variable precision ⋮ A proximal trust-region method for nonsmooth optimization with inexact function and gradient evaluations ⋮ Truncated Newton methods for optimization with inaccurate functions and gradients ⋮ A Certified Trust Region Reduced Basis Approach to PDE-Constrained Optimization ⋮ An algorithm for the minimization of nonsmooth nonconvex functions using inexact evaluations and its worst-case complexity ⋮ Effect of time stepping strategy on adjoint-based production optimization ⋮ Newton's Method for Monte Carlo--Based Residuals

Uses Software

minpack

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