Stochastic derivative-free optimization using a trust region framework

DOI10.1007/s10589-016-9827-zzbMath1381.90098OpenAlexW2280955991MaRDI QIDQ301671

Jeffrey Larson, Stephen C. Billups

Publication date: 1 July 2016

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://www.osti.gov/biblio/1366470

zbMATH Keywords

stochastic optimizationderivative-free optimizationmodel-based trust region methods

Mathematics Subject Classification ID

Derivative-free methods and methods using generalized derivatives (90C56) Stochastic approximation (62L20)

Related Items

Survey of derivative-free optimization ⋮ Stochastic optimization using a trust-region method and random models ⋮ Robust optimization of noisy blackbox problems using the mesh adaptive direct search algorithm ⋮ ASTRO-DF: A Class of Adaptive Sampling Trust-Region Algorithms for Derivative-Free Stochastic Optimization ⋮ A Stochastic Levenberg--Marquardt Method Using Random Models with Complexity Results ⋮ Stochastic Trust-Region Methods with Trust-Region Radius Depending on Probabilistic Models ⋮ Coupled Learning Enabled Stochastic Programming with Endogenous Uncertainty ⋮ Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates ⋮ Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization ⋮ Newton-type methods for non-convex optimization under inexact Hessian information ⋮ Surrogate-Based Promising Area Search for Lipschitz Continuous Simulation Optimization ⋮ Convergence of Newton-MR under Inexact Hessian Information ⋮ A Derivative-Free Trust-Region Algorithm for the Optimization of Functions Smoothed via Gaussian Convolution Using Adaptive Multiple Importance Sampling ⋮ Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates ⋮ A zeroth order method for stochastic weakly convex optimization ⋮ Derivative-free optimization methods ⋮ Expected complexity analysis of stochastic direct-search ⋮ Manifold Sampling for $\ell_1$ Nonconvex Optimization

Uses Software

• UOBYQA
• QNSTOP

Cites Work

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