On simplifying the primal-dual method of multipliers

Guoqiang Zhang; Richard Heusdens

ICASSP 2016accepted0 citations

On simplifying the primal-dual method of multipliers

Abstract

Recently, the primal-dual method of multipliers (PDMM) has been proposed to solve a convex optimization problem defined over a general graph. In this paper, we consider simplifying PDMM for a subclass of the convex optimization problems. This subclass includes the consensus problem as a special form. By using algebra, we show that the update expressions of PDMM can be simplified significantly. We then evaluate PDMM for training a support vector machine (SVM). The experimental results indicate that PDMM converges considerably faster than the alternating direction method of multipliers (ADMM).

BibTeX

@inproceedings{icassp2016_onsimplifyingthe,
  title = {On simplifying the primal-dual method of multipliers},
  author = {Guoqiang Zhang and Richard Heusdens},
  booktitle = {ICASSP 2016},
  year = {2016}
}