On the Optimal Step-size Selection for the Alternating Direction Method of Multipliers*

摘要: The alternating direction method of multipliers is a powerful technique for structured large-scale optimization that has recently found applications in variety fields including networked optimization, estimation, compressed sensing and multi-agent systems. While this have received lot attention, there lack theoretical support how to set the algorithm parameters, its step-size typically tuned experimentally. In paper we consider three different formulations present explicit expressions minimizes convergence rate. We also compare our with one existing selection techniques consensus applications.