http://dx.doi.org/10.1137/090768813">
 

Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Solving large-scale least squares covariance matrix problems by alternating direction methods

Language

English

Abstract

© 2011 Society for Industrial and Applied Mathematics. The well-known least squares semidefinite programming (LSSDP) problem seeks the nearest adjustment of a given symmetric matrix in the intersection of the cone of positive semidefinite matrices and a set of linear constraints, and it captures many applications in diversing fields. The task of solving large-scale LSSDP with many linear constraints, however, is numerically challenging. This paper mainly shows the applicability of the classical alternating direction method (ADM) for solving LSSDP and convinces the efficiency of the ADM approach. We compare the ADM approach with some other existing approaches numerically, and we show the superiority of ADM for solving large-scale LSSDP.

Keywords

Alternating direction method, Large-scale, Least squares semidefinite matrix, Variational inequality

Publication Date

2011

Source Publication Title

SIAM Journal on Matrix Analysis and Applications

Volume

32

Issue

1

Start Page

136

End Page

152

Publisher

Society for Industrial and Applied Mathematics

ISSN (print)

08954798

ISSN (electronic)

10957162

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