ASTRO-DF

swMATH26833MaRDI QIDQ38558

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• ASTRO-DF: A Class of Adaptive Sampling Trust-Region Algorithms for Derivative-Free Stochastic Optimization

Related Items (16)

A discussion on variational analysis in derivative-free optimization ⋮ A theoretical and empirical comparison of gradient approximations in derivative-free optimization ⋮ Plausible Screening Using Functional Properties for Simulations with Large Solution Spaces ⋮ Stochastic optimization using a trust-region method and random models ⋮ A Variable Sample-Size Stochastic Quasi-Newton Method for Smooth and Nonsmooth Stochastic Convex Optimization ⋮ Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates ⋮ Open Problem—Iterative Schemes for Stochastic Optimization: Convergence Statements and Limit Theorems ⋮ Newton-type methods for non-convex optimization under inexact Hessian information ⋮ 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 ⋮ An extended two-stage sequential optimization approach: properties and performance ⋮ Stochastic mesh adaptive direct search for blackbox optimization using probabilistic estimates ⋮ Derivative-free optimization methods ⋮ Adaptive Sequential Sample Average Approximation for Solving Two-Stage Stochastic Linear Programs ⋮ Adaptive sampling line search for local stochastic optimization with integer variables ⋮ Optimization of Stochastic Blackboxes with Adaptive Precision

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