A modified Levenberg–Marquardt method with line search for nonlinear equations

Liang Chen ()
Additional contact information
Liang Chen: Huaibei Normal University

Computational Optimization and Applications, 2016, vol. 65, issue 3, No 9, 753-779

Abstract: Abstract In this paper, we propose a new modified Levenberg–Marquardt method for nonlinear equations. At every iteration, not only a general LM step, but also two additional approximate LM steps which save the Jacobian calculation and employ line search for the step size, are computed. Under the error bound condition which is weaker than nonsingularity, this method is shown to be of fourth convergence order. Numerical results show that the new method is very efficient and could save many calculations of the Jacobian.

Keywords: Unconstrained optimization; Nonlinear equations; Levenberg–Marquardt method; Local error bound; Line search; 65K05; 90C30 (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10589-016-9852-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:coopap:v:65:y:2016:i:3:d:10.1007_s10589-016-9852-y

Ordering information: This journal article can be ordered from
http://www.springer.com/math/journal/10589

DOI: 10.1007/s10589-016-9852-y

Access Statistics for this article

Computational Optimization and Applications is currently edited by William W. Hager

More articles in Computational Optimization and Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().