Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5776071 | Journal of Computational and Applied Mathematics | 2018 | 13 Pages |
Abstract
The positive semidefinite constraint and equality constraint arise widely in matrix optimization problems of different areas including signal/image processing, finance and risk management. In this paper, an inexact accelerated Augmented Lagrangian Method (ALM) relying on a parameter m is designed to solve the structured low-rank minimization with equality constraint, which is more general and flexible than the existing ALM and its variants. We prove a worst-case O(1âk2) convergence rate of the new method in terms of the residual of the Lagrangian function, and we analyze that when mâ[0,1) the residual of our method is smaller than that of the traditional accelerated ALM. Compared with several state-of-the-art methods, preliminary numerical experiments on solving the Q-weighted low-rank correlation matrix problem from finance validate the efficiency of the proposed method.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Jianchao Bai, Jicheng Li, Fengmin Xu, Pingfan Dai,