Department of Mathematics
Solving large-scale least squares covariance matrix problems by alternating direction methods
© 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.
Alternating direction method, Large-scale, Least squares semidefinite matrix, Variational inequality
Source Publication Title
SIAM Journal on Matrix Analysis and Applications
Society for Industrial and Applied Mathematics
Link to Publisher's Edition
He, Bingsheng, Minghua Xu, and Xiaoming Yuan. "Solving large-scale least squares covariance matrix problems by alternating direction methods." SIAM Journal on Matrix Analysis and Applications 32.1 (2011): 136-152.