A nonmonotone line search method for stochastic optimization problems

DOI10.2298/FIL1819799KzbMath1499.90138MaRDI QIDQ5086883

Sanja Lončar, Nataša Krejić

Publication date: 7 July 2022

Published in: Filomat (Search for Journal in Brave)

zbMATH Keywords

sample average approximationunconstrained stochastic problemzero upper density convergence

Mathematics Subject Classification ID

Nonlinear programming (90C30) Stochastic programming (90C15)

Cites Work

• Sample size selection in optimization methods for machine learning
• A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems
• A stochastic steepest-descent algorithm
• Stochastic algorithms with Armijo stepsizes for minimization of functions
• Optimization algorithm with probabilistic estimation
• On efficiency of nonmonotone Armijo-type line searches
• Nonmonotone line search methods with variable sample size
• On the Use of Stochastic Hessian Information in Optimization Methods for Machine Learning
• Introduction to Stochastic Search and Optimization
• A derivative-free line search and global convergence of Broyden-like method for nonlinear equations
• Benchmarking optimization software with performance profiles.

This page was built for publication: A nonmonotone line search method for stochastic optimization problems