A quasi-Newton based pattern search algorithm for unconstrained optimization
type: Item
instance of: scholarly article
title: A quasi-Newton based pattern search algorithm for unconstrained optimization
zbMATH Open document ID: 1110.65052
author: Ting Wu
author: Linping Sun
published in: Applied Mathematics and Computation
publication date: 2007-02-13T00:00:00Z
review text: The authors study the general unconstrained optimization problem where the objective function is a continuously differentiable function. It improves upon the classic BFGS (Broyden, Fletcher, Goldfard, Shanno) algorithm, by combining this technique with pattern search. In particular, the conjugate factorization of approximating Hessian matrices of the BFGS scheme are used to produce a series of positive basis for the pattern search. The authors, after presenting the methodological background of the BFGS algorithm and the novelty of their approach in section 2, proceed to study convergence issues (with proof) in section 3. The article concludes with a section containing numerical experimentation and a list of useful references.
reviewed by: Efstratios Rappos
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C53
Mathematics Subject Classification ID: 90C56
Mathematics Subject Classification ID: 90C30
zbMATH DE Number: 5125261
zbMATH Keywords: pattern search
zbMATH Keywords: BFGS algorithm
zbMATH Keywords: derivative-free optimization
zbMATH Keywords: numerical examples
zbMATH Keywords: Broyden-Fletcher-goldfarb-Shanno method
zbMATH Keywords: convergence
describes a project that uses: minpack
MaRDI profile type: Publication
full work available at URL: j.amc.2006.05.107
OpenAlex ID: W2101804102
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DOI: 10.1016/J.AMC.2006.05.107
description: scientific article; zbMATH DE number 5125261