Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/29960
Title: A Penalty Method for Correlation Matrix Problems with Prescribed Constraints
Authors: CHEN XIAOQUAN
Keywords: nearest correlation matrix, majorization method, semismoothness, Newton’s method
Issue Date: 10-Aug-2011
Citation: CHEN XIAOQUAN (2011-08-10). A Penalty Method for Correlation Matrix Problems with Prescribed Constraints. ScholarBank@NUS Repository.
Abstract: In this thesis, we apply the penalty technique to solve the nearest correlation matrix problem, i.e, we consider the penalized version of the former problem. To deal with the penalized problem, we first apply the essential idea of the majorization method by solving a sequence of unconstrained inner problems iteratively. Actually, the inner problem is generated by the Lagrangian dual approach based on the metric projection and the Moreau-Yosida regularization. Since the objective function in the inner problem is not twice continuously differentiable, we take advantage of the strongly semismooth to overcome this difficulty. Then we propose a semismooth Newton-CG method to solve the inner problem. Finally, we analyze the convergence properties of the semismooth Newton-CG method by using the constraint nondegeneracy. The numerical results reported indicate that our algorithm is efficient and robust.
URI: http://scholarbank.nus.edu.sg/handle/10635/29960
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
ChenXQ.pdf289.86 kBAdobe PDF

OPEN

NoneView/Download

Page view(s)

380
checked on Sep 29, 2018

Download(s)

328
checked on Sep 29, 2018

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.