Convergence analysis of an improved BFGS method and its application in the Muskingum model
type: Item
instance of: scholarly article
title: Convergence analysis of an improved BFGS method and its application in the Muskingum model
zbMATH Open document ID: 1459.65088
DOI: 10.1155/2020/4519274
author: Tianshan Yang
author: Pengyuan Li
author: Xiaoliang Wang
published in: Mathematical Problems in Engineering
publication date: 2020-10-12T00:00:00Z
review text: Summary: The BFGS method is one of the most effective quasi-Newton algorithms for minimization-optimization problems. In this paper, an improved BFGS method with a modified weak Wolfe-Powell line search technique is used to solve convex minimization problems and its convergence analysis is established. Seventy-four academic test problems and the Muskingum model are implemented in the numerical experiment. The numerical results show that our algorithm is comparable to the usual BFGS algorithm in terms of the number of iterations and the time consumed, which indicates our algorithm is effective and reliable.
Mathematics Subject Classification ID: 65K05
Mathematics Subject Classification ID: 90C26
Mathematics Subject Classification ID: 90C30
Mathematics Subject Classification ID: 90C53
zbMATH DE Number: 7259607
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2020/4519274
OpenAlex ID: W3078710903
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description: scientific article; zbMATH DE number 7259607