Updating the regularization parameter in the adaptive cubic regularization algorithm

N. Gould (), M. Porcelli () and P. Toint ()

Computational Optimization and Applications, 2012, vol. 53, issue 1, 22 pages

Abstract: The adaptive cubic regularization method (Cartis et al. in Math. Program. Ser. A 127(2):245–295, 2011 ; Math. Program. Ser. A. 130(2):295–319, 2011 ) has been recently proposed for solving unconstrained minimization problems. At each iteration of this method, the objective function is replaced by a cubic approximation which comprises an adaptive regularization parameter whose role is related to the local Lipschitz constant of the objective’s Hessian. We present new updating strategies for this parameter based on interpolation techniques, which improve the overall numerical performance of the algorithm. Numerical experiments on large nonlinear least-squares problems are provided. Copyright Springer Science+Business Media, LLC 2012

Keywords: Unconstrained optimization; Cubic regularization; Numerical performance (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://hdl.handle.net/10.1007/s10589-011-9446-7 (text/html)
Access to full text is restricted to subscribers.

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:53:y:2012:i:1:p:1-22

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

DOI: 10.1007/s10589-011-9446-7

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 ().