Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6898674 | European Journal of Operational Research | 2010 | 11 Pages |
Abstract
We propose a modified alternating direction method for solving convex quadratically constrained quadratic semidefinite optimization problems. The method is a first-order method, therefore requires much less computational effort per iteration than the second-order approaches such as the interior point methods or the smoothing Newton methods. In fact, only a single inexact metric projection onto the positive semidefinite cone is required at each iteration. We prove global convergence and provide numerical evidence to show the effectiveness of this method.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Jie Sun, Su Zhang,