Solving large-scale least squares semidefinite programming by alternating direction methods

Bingsheng He, Minghua Xu, Xiaoming YUAN*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

71 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)136-152
Number of pages17
JournalSIAM Journal on Matrix Analysis and Applications
Volume32
Issue number1
DOIs
Publication statusPublished - 2011

Scopus Subject Areas

  • Analysis

User-Defined Keywords

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

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