A new supermemory gradient method for unconstrained optimization problems
type: Item
instance of: scholarly article
title: A new supermemory gradient method for unconstrained optimization problems
zbMATH Open document ID: 1279.90165
published in: Optimization Letters
publication date: 2012-11-07T00:00:00Z
Mathematics Subject Classification ID: 90C30
zbMATH DE Number: 6102936
zbMATH Keywords: unconstrained optimization
zbMATH Keywords: supermemory gradient method
zbMATH Keywords: trust region technique
zbMATH Keywords: ODE-based methods
zbMATH Keywords: global convergence
author: Yigui Ou
author: Guan-shu Wang
describes a project that uses: L-BFGS
describes a project that uses: minpack
MaRDI profile type: Publication
full work available at URL: s11590-011-0328-9
OpenAlex ID: W2123676331
cites work: Optimization theory and methods. Nonlinear programming
cites work: Handbook of applied optimization
cites work: A Nonlinear Conjugate Gradient Method with a Strong Global Convergence Property
cites work: Modified nonlinear conjugate gradient methods with sufficient descent property for large-scale optimization problems
cites work: Convergence of supermemory gradient method
cites work: Strong global convergence of an adaptive nonmonotone memory gradient method
cites work: Global convergence of a memory gradient method for unconstrained optimization
cites work: Restart procedures for the conjugate gradient method
cites work: A new super-memory gradient method with curve search rule
cites work: Global convergence of a memory gradient method without line search
cites work: A new class of supermemory gradient methods
cites work: On memory gradient method with trust region for unconstrained optimization
cites work: Some effective methods for unconstrained optimization based on the solution of systems of ordinary differential equations
cites work: The convergence of subspace trust region methods
cites work: An ODE-based trust region method for unconstrained optimization problems
cites work: On the limited memory BFGS method for large scale optimization
cites work: Representations of quasi-Newton matrices and their use in limited memory methods
cites work: Testing Unconstrained Optimization Software
cites work: A class of nonmonotone stabilization trust region methods
DOI: 10.1007/S11590-011-0328-9
description: scientific article; zbMATH DE number 6102936