The global and superlinear convergence of a new nonmonotone MBFGS algorithm on convex objective functions

DOI10.1016/j.cam.2007.08.017zbMath1152.65068OpenAlexW1969738095MaRDI QIDQ939545

Liying Liu, Shengwei Yao, Zeng-xin Wei

Publication date: 22 August 2008

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2007.08.017

zbMATH Keywords

global convergencenumerical examplessuperlinear convergencequasi-Newton methodBroyden-Fletcher-Goldfarb-Shanno (BFGS) formulaGLL line search

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonlinear programming (90C30) Methods of quasi-Newton type (90C53)

Related Items

A Modified Non-Monotone BFGS Method for Non-Convex Unconstrained Optimization ⋮ The convergence of a new modified BFGS method without line searches for unconstrained optimization or complexity systems ⋮ A globally convergent BFGS method with nonmonotone line search for non-convex minimization ⋮ Nonlinear conjugate gradient methods with sufficient descent condition for large-scale unconstrained optimization

Uses Software

• ve08
• minpack

Cites Work

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