## 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 language | English |
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Pages (from-to) | 136-152 |

Number of pages | 17 |

Journal | SIAM Journal on Matrix Analysis and Applications |

Volume | 32 |

Issue number | 1 |

DOIs | |

Publication status | Published - 8 Feb 2011 |

## Scopus Subject Areas

- Analysis

## User-Defined Keywords

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