Updating the regularization parameter in the adaptive cubic regularization algorithm
AbstractThe 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
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Springer in its journal Computational Optimization and Applications.
Volume (Year): 53 (2012)
Issue (Month): 1 (September)
Contact details of provider:
Web page: http://www.springer.com/math/journal/10589
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn) or (Christopher F Baum).
If references are entirely missing, you can add them using this form.