A modified alternating direction method for convex quadratically constrained quadratic semidefinite programs

We propose a modified alternate 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. Key Words: Alternating direction method, Conic programming, Quadratic semidefinite optimization Corresponding author. Department of Decision Sciences and Risk Management Institute, National University

• Type: Journal
• Year: 2010
• Where: EOR
• Authors: Jie Sun, Su Zhang
• Keywords: Direction Method, EOR 2010, Quadratic Semidefinite Optimization, Semidefinite Optimization Problems