Global convergence of a memory gradient method without line search

DOI10.1007/s12190-007-0021-4zbMath1193.90214OpenAlexW2069863795MaRDI QIDQ949285

Zhensheng Yu

Publication date: 21 October 2008

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

Full work available at URL: https://doi.org/10.1007/s12190-007-0021-4

zbMATH Keywords

unconstrained optimizationglobal convergencememory gradientwithout line search

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Methods of reduced gradient type (90C52)

Related Items (3)

An ODE-based trust region method for unconstrained optimization problems ⋮ A new supermemory gradient method for unconstrained optimization problems ⋮ The new spectral conjugate gradient method for large-scale unconstrained optimisation

Uses Software

• minpack

Cites Work

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• Global convergence of a two-parameter family of conjugate gradient methods without line search
• Global convergence of a memory gradient method for unconstrained optimization
• Memory gradient method for the minimization of functions
• Study on a supermemory gradient method for the minimization of functions
• Descent Property and Global Convergence of the Fletcher—Reeves Method with Inexact Line Search
• Testing Unconstrained Optimization Software
• Global Convergence Properties of Conjugate Gradient Methods for Optimization
• A new class of memory gradient methods with inexact line searches
• On a profit maximizing location model

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