A new regularized quasi-Newton algorithm for unconstrained optimization

Hao Zhang and Qin Ni

Applied Mathematics and Computation, 2015, vol. 259, issue C, 460-469

Abstract: In this paper, we present a new regularized quasi-Newton algorithm for unconstrained optimization. In this algorithm, an adaptive quadratic term is employed to regularize the quasi-Newton model. At each iteration, the trial step is obtained by solving an unconstrained quadratic subproblem. The global convergence and superlinear convergence of the new algorithm are established under reasonable assumptions. The numerical results show that new algorithm is effective.

Keywords: Unconstrained optimization; Quasi-Newton method; Regularization (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S009630031500212X
Full text for ScienceDirect subscribers only

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:eee:apmaco:v:259:y:2015:i:c:p:460-469

DOI: 10.1016/j.amc.2015.02.032

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().