Please use this identifier to cite or link to this item: https://doi.org/10.1137/080727075
Title: Calibrating least squares semidefinite programming with equality and inequality constraints
Authors: Gao, Y.
Sun, D. 
Keywords: Covariance matrix
Quadratic convergence
Smoothing Newton method
Issue Date: 2009
Citation: Gao, Y., Sun, D. (2009). Calibrating least squares semidefinite programming with equality and inequality constraints. SIAM Journal on Matrix Analysis and Applications 31 (3) : 1432-1457. ScholarBank@NUS Repository. https://doi.org/10.1137/080727075
Abstract: In this paper, we consider the least squares semidefinite programming with a large number of equality and inequality constraints. One difficulty in finding an efficient method for solving this problem is due to the presence of the inequality constraints. In this paper, we propose to overcome this difficulty by reformulating the problem as a system of semismooth equations with two level metric projection operators. We then design an inexact smoothing Newton method to solve the resulting semismooth system. At each iteration, we use the BiCGStab iterative solver to obtain an approximate solution to the generated smoothing Newton linear system. Our numerical experiments confirm the high efficiency of the proposed method. © 2009 Society for Industrial and Applied Mathematics.
Source Title: SIAM Journal on Matrix Analysis and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/102966
ISSN: 08954798
DOI: 10.1137/080727075
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

34
checked on Oct 15, 2018

WEB OF SCIENCETM
Citations

29
checked on Dec 25, 2017

Page view(s)

29
checked on Oct 12, 2018

Google ScholarTM

Check

Altmetric


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