A new class of supermemory gradient methods
type: Item
instance of: scholarly article
title: A new class of supermemory gradient methods
zbMATH Open document ID: 1111.65055
author: Zhenjun Shi
author: Jie Shen
published in: Applied Mathematics and Computation
publication date: 2007-02-19T00:00:00Z
review text: An unconstrained optimization problem consisting in minimizing a continuously differentiable function \(f:\mathbb R^n\to \mathbb R^1\) is considered. A new class of supermemory gradient methods for the unconstrained minimization problem is proposed. Conditions, which guarantee global convergence are presented. In a sufficiently small neighborhood of the optimal solution the algorithms can be reduced to quasi-Newton methods. Unlike to many line search methods, the proposed algorithms use not only the current iterative information, but also some information, which can be obtained from preceding iterations. Numerical results reported in the concluding part of the paper show the effectiveness of the proposed algorithms in practical computation.
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 90C53
zbMATH DE Number: 5128259
zbMATH Keywords: unconstrained optimization
zbMATH Keywords: global convergence
zbMATH Keywords: quasi-Newton methods
zbMATH Keywords: algorithms
zbMATH Keywords: numerical results
reviewed by: Karel Zimmermann
describes a project that uses: L-BFGS
describes a project that uses: minpack
MaRDI profile type: Publication
full work available at URL: j.amc.2006.05.079
OpenAlex ID: W2104668044
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DOI: 10.1016/J.AMC.2006.05.079
description: scientific article; zbMATH DE number 5128259